From 4198ee8e5e4c4249e50f837f247c27b90c4c8284 Mon Sep 17 00:00:00 2001 From: andrewhooker Date: Thu, 22 Feb 2024 07:58:50 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20UUPharma?= =?UTF-8?q?cometrics/xpose4@c1701a796608659dd596f887fa04135e45d4fecc=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- index.html | 4 ++-- pkgdown.yml | 2 +- reference/compute.cwres.html | 2 +- reference/export.variable.definitions.html | 2 +- reference/runsum.html | 4 ++-- reference/simprazExample.html | 4 ++-- reference/tabulate.parameters.html | 4 ++-- reference/xp.scope3.html | 20 ++++++++++---------- reference/xpose.data.html | 4 ++-- search.json | 2 +- 10 files changed, 24 insertions(+), 24 deletions(-) diff --git a/index.html b/index.html index 3cb2f1a..021cc4e 100644 --- a/index.html +++ b/index.html @@ -133,7 +133,7 @@

Running Xpose 4

The Xpose 4 Bestiary

-

A more detailed description of Xpose with example plots and explanaitions for most of the functions in the package is available in our Bestiarium: http://xpose.sourceforge.net/bestiarium_v1.0.pdf

+

A more detailed description of Xpose with example plots and explanaitions for most of the functions in the package is available in our Bestiarium: https://xpose.sourceforge.net/bestiarium_v1.0.pdf

Don’t Panic @@ -198,7 +198,7 @@

Developers

Dev status

  • CRAN_Status_Badge
    • -
  • codecov.io
    • +
  • codecov.io
  • R-CMD-check
diff --git a/pkgdown.yml b/pkgdown.yml index f5e4f8c..95f1852 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 2.19.2 pkgdown: 2.0.7 pkgdown_sha: ~ articles: {} -last_built: 2024-02-22T07:18Z +last_built: 2024-02-22T07:57Z urls: reference: http://uupharmacometrics.github.io/xpose4/reference article: http://uupharmacometrics.github.io/xpose4/articles diff --git a/reference/compute.cwres.html b/reference/compute.cwres.html index 6e162ec..bc2cced 100644 --- a/reference/compute.cwres.html +++ b/reference/compute.cwres.html @@ -230,7 +230,7 @@

Setting up the NONMEM model fileThis procedure can be done automatically using Perl Speaks NONMEM (PsN) and we highly recommend using PsN for this purpose. After installing PsN just type 'execute [modelname] -cwres'. See -http://psn.sourceforge.net for more details.

+https://uupharmacometrics.github.io/PsN/ for more details.

There are five main insertions needed in your NONMEM control file:

  1. $ABB COMRES=X.

    Insert this line directly after your $DATA line. The value of X is the number of ETA() terms plus the number of EPS() terms in your model. For diff --git a/reference/export.variable.definitions.html b/reference/export.variable.definitions.html index 787cc48..cf1d653 100644 --- a/reference/export.variable.definitions.html +++ b/reference/export.variable.definitions.html @@ -119,7 +119,7 @@

    Author<

    Examples

    od = setwd(tempdir()) # move to a temp directory
 (cur.files <- dir()) # current files in temp directory
-#> [1] "bslib-b4e0a141bd7a6d87d4e27f8e112db7d2"
+#> [1] "bslib-f00e6fae00d8efe8984ec802f708f91a"
 #> [2] "downlit"                               
 
 export.variable.definitions(simpraz.xpdb,file="xpose.vardefs.ini")
diff --git a/reference/runsum.html b/reference/runsum.html
index afee0cd..cfd54e9 100644
--- a/reference/runsum.html
+++ b/reference/runsum.html
@@ -223,9 +223,9 @@ 
    Author<
     
    Examples
    
     
    od = setwd(tempdir()) # move to a temp directory
 (cur.files <- dir()) # current files in temp directory
-#> [1] "bslib-b4e0a141bd7a6d87d4e27f8e112db7d2"
+#> [1] "bslib-f00e6fae00d8efe8984ec802f708f91a"
 #> [2] "downlit"                               
-#> [3] "file1775638e0fef"                      
+#> [3] "file55221cc6cf3b"                      
 
 simprazExample(overwrite=TRUE) # write files
 (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?
diff --git a/reference/simprazExample.html b/reference/simprazExample.html
index 4773411..9ed9feb 100644
--- a/reference/simprazExample.html
+++ b/reference/simprazExample.html
@@ -107,9 +107,9 @@ 
    Examples
    
 od = setwd(tempdir()) # move to a temp directory
 (cur.files <- dir()) # current files in temp directory
-#> [1] "bslib-b4e0a141bd7a6d87d4e27f8e112db7d2"
+#> [1] "bslib-f00e6fae00d8efe8984ec802f708f91a"
 #> [2] "downlit"                               
-#> [3] "file1775638e0fef"                      
+#> [3] "file55221cc6cf3b"                      
 
 simprazExample(overwrite=TRUE) # write files
 
diff --git a/reference/tabulate.parameters.html b/reference/tabulate.parameters.html
index 1758dff..339bef1 100644
--- a/reference/tabulate.parameters.html
+++ b/reference/tabulate.parameters.html
@@ -120,9 +120,9 @@ 
    Examples
    
 od = setwd(tempdir()) # move to a temp directory
 (cur.files <- dir()) # current files in temp directory
-#> [1] "bslib-b4e0a141bd7a6d87d4e27f8e112db7d2"
+#> [1] "bslib-f00e6fae00d8efe8984ec802f708f91a"
 #> [2] "downlit"                               
-#> [3] "file1775638e0fef"                      
+#> [3] "file55221cc6cf3b"                      
 
 simprazExample(overwrite=TRUE) # write files
 (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?
diff --git a/reference/xp.scope3.html b/reference/xp.scope3.html
index 9f01c3b..9026b97 100644
--- a/reference/xp.scope3.html
+++ b/reference/xp.scope3.html
@@ -169,43 +169,43 @@ 
    Examplesxp.scope3(simpraz.xpdb)
 #> $SEX
 #> ~1 + SEX
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $RACE
 #> ~1 + RACE
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $SMOK
 #> ~1 + SMOK
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $HCTZ
 #> ~1 + HCTZ
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $PROP
 #> ~1 + PROP
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $CON
 #> ~1 + CON
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $AGE
 #> ~1 + AGE + ns(AGE, df = 2)
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $HT
 #> ~1 + HT + ns(HT, df = 2)
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $WT
 #> ~1 + WT + ns(WT, df = 2)
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 #> $SECR
 #> ~1 + SECR + ns(SECR, df = 2)
-#> <environment: 0x556bacd55370>
+#> <environment: 0x564553f286e8>
 #> 
 
 
    
diff --git a/reference/xpose.data.html b/reference/xpose.data.html
index 02b79a9..d471d75 100644
--- a/reference/xpose.data.html
+++ b/reference/xpose.data.html
@@ -229,9 +229,9 @@ 
    Examples 
 od = setwd(tempdir()) # move to a temp directory
 (cur.files <- dir()) # current files in temp directory
-#> [1] "bslib-b4e0a141bd7a6d87d4e27f8e112db7d2"
+#> [1] "bslib-f00e6fae00d8efe8984ec802f708f91a"
 #> [2] "downlit"                               
-#> [3] "file1775638e0fef"                      
+#> [3] "file55221cc6cf3b"                      
 
 simprazExample(overwrite=TRUE) # write files
 (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?
diff --git a/search.json b/search.json
index bf46cd9..8b17168 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"http://uupharmacometrics.github.io/xpose4/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Andrew C. Hooker. Author, maintainer, copyright holder. Mats O. Karlsson. Author, copyright holder. Justin J. Wilkins. Author. E. Niclas Jonsson. Author, translator, copyright holder. Ron Keizer. Contributor.            functionality bootstrap GAM SCM","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Jonsson, E.N. & Karlsson, M.O. (1999) Xpose--S-PLUS based population   pharmacokinetic/pharmacodynamic model building aid NONMEM. Computer Methods  Programs Biomedicine. 58(1):51-64. Keizer RJ, Karlsson MO, Hooker AC (2013). “Modeling Simulation Workbench NONMEM: Tutorial Pirana, PsN, Xpose.” CPT: Pharmacometrics & Systems Pharmacology, 2(6). doi:10.1038/psp.2013.24.","code":"@Article{,   title = {Xpose--an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM},   journal = {Computer Methods and Programs in Biomedicine},   volume = {58},   number = {1},   pages = {51-64},   year = {1999},   author = {E. N. Jonsson and M. O. Karlsson},   doi = {10.1016/s0169-2607(98)00067-4}, } @Article{,   title = {Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose},   author = {Ron J Keizer and Mats O Karlsson and Andrew C Hooker},   journal = {CPT: Pharmacometrics & Systems Pharmacology},   year = {2013},   volume = {2},   number = {6},   doi = {10.1038/psp.2013.24}, }"},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"xpose-4-","dir":"","previous_headings":"","what":"Diagnostics for Nonlinear Mixed-Effect Models","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Andrew C. Hooker, Mats O. Karlsson E. Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Xpose 4 collection functions used model building aid nonlinear mixed-effects (population) analysis using NONMEM. facilitates data set checkout, exploration visualization, model diagnostics, candidate covariate identification model comparison.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"install xpose need R (>= version 2.2.0). install Xpose R use one following methods: latest stable release – CRAN. Write R command line: Latest development version – Github. Note command installs “master” (development) branch; want release branch Github add ref=\"release\" install_github() call.","code":"install.packages(\"xpose4\") # install.packages(\"devtools\") devtools::install_github(\"UUPharmacometrics/xpose4\")"},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"running-xpose-4","dir":"","previous_headings":"","what":"Running Xpose 4","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Start R load xpose: use classic menu system, type R command prompt: function independently available command line, Xpose library loaded. First create set files NONMEM run can import files Xpose Display goodness--fit plots:  Clean files created show examples: help available online documentation, can found typing (example) ?xpose4 R command line.","code":"library(xpose4) #> Loading required package: lattice xpose4() cur.files <- dir() # current files in temp directory  simprazExample() # write files from an example NONMEM run new.files <- dir()[!(dir() %in% cur.files)]  # the new files created by simprazExample xpdb <- xpose.data(1) basic.gof(xpdb) unlink(new.files)"},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"the-xpose-4-bestiary","dir":"","previous_headings":"","what":"The Xpose 4 Bestiary","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"detailed description Xpose example plots explanaitions functions package available Bestiarium: http://xpose.sourceforge.net/bestiarium_v1.0.pdf","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"dont-panic","dir":"","previous_headings":"","what":"Don’t Panic","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Andrew Hooker (andrew.hooker farmaci.uu.se) able get answer run trouble. website https://uupharmacometrics.github.io/xpose4/ also help.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"release-schedule","dir":"","previous_headings":"","what":"Release Schedule","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Bugfix releases released regularly, fixing problems found.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Xpose 4 free software: can redistribute /modify terms GNU Lesser General Public License published Free Software Foundation, either version 3 License, (option) later version. program distributed hope useful, WITHOUT WARRANTY; without even implied warranty MERCHANTABILITY FITNESS PARTICULAR PURPOSE. See GNU Lesser General Public License details https://www.gnu.org/licenses/.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"known-bugs","dir":"","previous_headings":"","what":"Known Bugs","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"None present, certainly . Report https://github.com/UUPharmacometrics/xpose4/issues.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"GAM functions for Xpose 4 — GAM_summary_and_plot","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"functions summarizing plotting results  generalized additive model within Xpose","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"","code":"xp.akaike.plot(   gamobj = NULL,   title = \"Default\",   xlb = \"Akaike value\",   ylb = \"Models\",   ... )  xp.cook(gam.object)  xp.ind.inf.fit(   gamobj = NULL,   plot.ids = TRUE,   idscex = 0.7,   ptscex = 0.7,   title = \"Default\",   recur = FALSE,   xlb = NULL,   ylb = NULL,   ... )  xp.ind.inf.terms(   gamobj = NULL,   xlb = NULL,   ylb = NULL,   plot.ids = TRUE,   idscex = 0.7,   ptscex = 0.7,   prompt = TRUE,   ... )  xp.ind.stud.res(   gamobj = NULL,   title = \"Default\",   recur = FALSE,   xlb = NULL,   ylb = NULL )  xp.plot(   gamobj = NULL,   plot.ids = TRUE,   idscex = 0.7,   ptscex = 0.7,   prompt = TRUE,   ... )  xp.summary(gamobj = NULL)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"gamobj GAM object use plot. null user asked choose list GAM objects memory. title text string indicating plot title. NULL, left blank. xlb text string indicating x-axis legend. NULL, left blank. ylb text string indicating y-axis legend. NULL, left blank. ... arguments passed GAM functions. gam.object GAM object (see gam. plot.ids Logical, specifies whether ID numbers displayed. idscex ID label size. ptscex Point size. recur dispersion used GAM object. prompt Specifies whether user prompted press RETURN plot pages. Default TRUE. object xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"Plots summaries.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"xp.akaike.plot(): Akaike plot results. xp.cook(): Individual parameters GAM fit. xp.ind.inf.fit(): Individual influence GAM fit. xp.ind.inf.terms(): Individual influence GAM terms. xp.ind.stud.res(): Studentized residuals. xp.plot(): GAM residuals base model vs. covariates. xp.summary(): Summarize GAM.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"Niclas Jonsson & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"creates stack box whisker plot absolute population conditional weighted residuals (|CWRES|) vs covariates, specific function Xpose 4.  wrapper encapsulating arguments codexpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"","code":"absval.cwres.vs.cov.bw(object, xlb = \"|CWRES|\", main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"object xpose.data object. xlb string giving label x-axis. NULL none. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.bw.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"Returns stack box--whisker plots |CWRES| vs covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling box--whisker plots available. See xpose.plot.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  absval.cwres.vs.cov.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"plot absolute population conditional weighted residuals (|CWRES|) vs population predictions (PRED) conditioned covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"","code":"absval.cwres.vs.pred.by.cov(   object,   covs = \"Default\",   ylb = \"|CWRES|\",   type = \"p\",   smooth = TRUE,   idsdir = \"up\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"object xpose.data object. covs vector covariates use plot. \"Default\"  covariates defined object@Prefs@Xvardef$Covariates used. ylb string giving label y-axis. NULL none. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed. default TRUE. idsdir Direction displaying point labels. default \"\", since displaying absolute values. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"Returns stack xyplots |CWRES| vs PRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. main argument supported owing multiple plots generated function. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"","code":"absval.cwres.vs.pred.by.cov(simpraz.xpdb, covs=c(\"HCTZ\",\"WT\"), max.plots.per.page=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"plot absolute population conditional weighted residuals (|CWRES|) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"","code":"absval.cwres.vs.pred(object, idsdir = \"up\", type = \"p\", smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"object xpose.data object. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"Returns xyplot |CWRES| vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.cwres.vs.pred(xpdb)   ## A conditioning plot absval.cwres.vs.pred(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.cwres.vs.pred(xpdb, main=\"My conditioning plot\", ylb=\"|CWRES|\", xlb=\"PRED\")   ## Custom colours and symbols, no IDs absval.cwres.vs.pred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"matrix plot absolute population weighted residuals (|CWRES|) vs population predictions (PRED) absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED), specific function Xpose 4. wrapper encapsulating arguments absval.cwres.vs.pred absval.iwres.vs.ipred functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"","code":"absval.iwres.cwres.vs.ipred.pred(object, main = \"Default\", ...)  absval.iwres.wres.vs.ipred.pred(object, main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"object xpose.data object. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"Returns compound plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"plots created absval.wres.vs.pred absval.iwres.vs.ipred functions presented side side comparison. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"absval.iwres.wres.vs.ipred.pred(): absolute population weighted residuals (|WRES|) vs population predictions (PRED) absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED)","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot absval.iwres.wres.vs.ipred.pred(xpdb)  absval.iwres.cwres.vs.ipred.pred(xpdb)   ## Custom colours and symbols absval.iwres.cwres.vs.ipred.pred(xpdb, cex=0.6, pch=8, col=1)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.cov.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","title":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","text":"box whisker plots absolute value  individual weighted residuals vs. covariates","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.cov.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","text":"","code":"absval.iwres.vs.cov.bw(object, xlb = \"|iWRES|\", main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.cov.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","text":"object \"xpose.data\" object. xlb string giving label x-axis. NULL none. main string giving plot title NULL none. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.cov.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","text":"xpose.multiple.plot object","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","title":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","text":"absolute value  individual weighted residuals vs. independent variable","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","text":"","code":"absval.iwres.vs.idv(   object,   ylb = \"|iWRES|\",   smooth = TRUE,   idsdir = \"up\",   type = \"p\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","text":"object \"xpose.data\" object. ylb string giving label y-axis. NULL none. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. idsdir string indicating directions extremes include labelling. Possible values \"\", \"\" \"\". type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","text":"lattice object","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"plot absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED) conditioned covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"","code":"absval.iwres.vs.ipred.by.cov(   object,   ylb = \"|IWRES|\",   idsdir = \"up\",   type = \"p\",   smooth = TRUE,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"object xpose.data object. ylb string giving label y-axis. NULL none. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"Returns stack xyplots |IWRES| vs IPRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.iwres.vs.ipred.by.cov(xpdb)  ## Custom axis labels absval.iwres.vs.ipred.by.cov(xpdb, ylb=\"|IWRES|\", xlb=\"IPRED\")  ## Custom colours and symbols, no IDs absval.iwres.vs.ipred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=FALSE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"plot absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"","code":"absval.iwres.vs.ipred(   object,   ylb = \"|iWRES|\",   type = \"p\",   ids = FALSE,   idsdir = \"up\",   smooth = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"object xpose.data object. ylb string giving label y-axis. NULL none. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. ids id values displayed? idsdir Direction displaying point labels. default \"\", since displaying absolute values. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"Returns xyplot |IWRES| vs IPRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"","code":"## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.iwres.vs.ipred(xpdb)   ## A conditioning plot absval.iwres.vs.ipred(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.iwres.vs.ipred(xpdb, main=\"My conditioning plot\", ylb=\"|IWRES|\", xlb=\"IPRED\")   ## Custom colours and symbols, no IDs absval.iwres.vs.ipred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"plot absolute individual weighted residuals (|IWRES|) vs individual predictions (PRED) independent variable (IDV), specific functions Xpose 4. functions wrappers encapsulating arguments xpose.plot.default function.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"","code":"absval.iwres.vs.pred(   object,   ylb = \"|IWRES|\",   smooth = TRUE,   idsdir = \"up\",   type = \"p\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"object xpose.data object. ylb string giving label y-axis. NULL none. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"Returns xyplot |IWRES| vs PRED |IWRES| vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.iwres.vs.pred(xpdb)   ## A conditioning plot absval.iwres.vs.pred(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.iwres.vs.pred(xpdb, main=\"My conditioning plot\", ylb=\"|IWRES|\", xlb=\"PRED\")   ## Custom colours and symbols, no IDs absval.iwres.vs.pred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"creates stack box whisker plot absolute population weighted residuals (|WRES| |iWRES|) vs covariates.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"","code":"absval.wres.vs.cov.bw(object, xlb = \"|WRES|\", main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"object xpose.data object. xlb string giving label x-axis. NULL none. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.bw.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"Returns stack box--whisker plots |WRES| vs covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling box--whisker plots available. See xpose.plot.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.wres.vs.cov.bw(xpdb)  ## A custom plot absval.wres.vs.cov.bw(xpdb, bwdotcol=\"white\",    bwdotpch=15,   bwreccol=\"red\",   bwrecfill=\"red\",   bwumbcol=\"red\",   bwoutpch=5,   bwoutcol=\"black\")  ## A vanilla plot using IWRES absval.iwres.vs.cov.bw(xpdb) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"plot absolute value CWRES (default, residuals option) vs independent variable, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"","code":"absval.wres.vs.idv(   object,   idv = \"idv\",   wres = \"Default\",   ylb = \"Default\",   smooth = TRUE,   idsdir = \"up\",   type = \"p\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"object xpose.data object. idv independent variable. wres weighted residual use. \"Default\" CWRES. ylb Y-axis label. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"Returns xyplot |CWRES| vs idv (often TIME, defined xvardef).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.wres.vs.idv(xpdb)   ## A conditioning plot absval.wres.vs.idv(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.wres.vs.idv(xpdb, main=\"Hello World\", ylb=\"|CWRES|\", xlb=\"IDV\")   ## Custom colours and symbols absval.wres.vs.idv(xpdb, cex=0.6, pch=3, col=1)   ## using the NPDEs instead of CWRES absval.wres.vs.idv(xpdb,wres=\"NPDE\") #>  #>  -----------Variable(s) not defined!------------- #>  NPDE is/are not defined in the current database #>  and must be defined for this command to work! #>  ------------------------------------------------ #> NULL  ## subsets absval.wres.vs.idv(xpdb,subset=\"TIME<10\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"plot absolute population weighted residuals (|WRES|) vs population predictions (PRED) conditioned covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"","code":"absval.wres.vs.pred.by.cov(   object,   ylb = \"|WRES|\",   type = \"p\",   smooth = TRUE,   ids = FALSE,   idsdir = \"up\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"object xpose.data object. ylb string giving label y-axis. NULL none. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ids Logical. id labels points shown? idsdir Direction displaying point labels. default \"\", since displaying absolute values. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"Returns stack xyplots |WRES| vs PRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.wres.vs.pred.by.cov(xpdb)  ## Custom axis labels absval.wres.vs.pred.by.cov(xpdb, ylb=\"|CWRES|\", xlb=\"PRED\")  ## Custom colours and symbols, IDs absval.wres.vs.pred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"plot absolute population weighted residuals (|WRES|) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"","code":"absval.wres.vs.pred(   object,   ylb = \"|WRES|\",   idsdir = \"up\",   type = \"p\",   smooth = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"object xpose.data object. ylb string giving label y-axis. NULL none. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"Returns xyplot |WRES| vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.wres.vs.pred(xpdb)   ## A conditioning plot absval.wres.vs.pred(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.wres.vs.pred(xpdb, main=\"My conditioning plot\",    ylb=\"|WRES|\", xlb=\"PRED\")   ## Custom colours and symbols absval.wres.vs.pred(xpdb, cex=0.6, pch=19, col=1,    smcol=\"blue\", smlty=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"functions plot absolute differences PRED, IPRED, WRES, CWRES IWRES covariates two specified model fits.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"","code":"absval.dcwres.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"CWRES|\")),   main = \"Default\",   ... )  absval.dipred.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"IPRED|\")),   main = \"Default\",   ... )  absval.diwres.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"IWRES|\")),   main = \"Default\",   ... )  absval.dpred.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"PRED|\")),   main = \"Default\",   ... )  absval.dwres.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"WRES|\")),   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"object xpose.data object. object.ref xpose.data object. supplied, user prompted. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. ylb string giving label y-axis. NULL none. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"Returns stack plots comprising comparisons PRED, IPRED, WRES (CWRES) IWRES two specified runs.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"Conditional weighted residuals (CWRES) may require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"absval.dcwres.vs.cov.model.comp(): absolute differences individual predictions covariates two specified model fits. absval.dipred.vs.cov.model.comp(): absolute differences individual predictions covariates two specified model fits. absval.diwres.vs.cov.model.comp(): absolute differences individual weighted  residuals covariates two specified model fits. absval.dpred.vs.cov.model.comp(): absolute differences population predictions covariates two specified model fits. absval.dwres.vs.cov.model.comp(): absolute differences  population weighted residuals covariates two specified model fits.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for runs ## 5 and 6 in the current working directory xpdb5 <- xpose.data(5) xpdb6 <- xpose.data(6)  ## A basic dWRES plot, without prompts absval.dwres.vs.cov.model.comp(xpdb5, xpdb6)  ## Custom colours and symbols, no user IDs absval.dpred.vs.cov.model.comp(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":null,"dir":"Reference","previous_headings":"","what":"Print tables or text in a grid object — add.grid.table","title":"Print tables or text in a grid object — add.grid.table","text":"functions take array values labels array text add one many grid viewports orderly fashion.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print tables or text in a grid object — add.grid.table","text":"","code":"add.grid.table(   txt,   col.nams = NULL,   ystart,   xstart = unit(0, \"npc\"),   start.pt = 1,   vp,   vp.num = 1,   minrow = 5,   cell.padding = 0.5,   mult.col.padding = 1,   col.optimize = TRUE,   equal.widths = FALSE,   space.before.table = 1,   center.table = FALSE,   use.rect = FALSE,   fill.type = NULL,   fill.col = \"grey\",   cell.lines.lty = 0,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print tables or text in a grid object — add.grid.table","text":"txt text table values add grid object. col.nams column names table values ystart y location start printing grid viewport xstart x location start printing grid viewport start.pt start point (row) table array start printing vp viewport(s) add table text vp.num viewport number vp start printing minrow minimum rows printing columns use table cell.padding padding cells table mult.col.padding padding multiple columns table col.optimize column optimize (TRUE) row optimize (FALSE) equal.widths columns equal widths space..table space table center.table center table viewport? use.rect make rectangles background color around table entries TRUE FALSE fill.type rectangles filled.  Allowed values \"\", \"top\", \"side\", \"\" NULL. fill.col color filled rectangles cell.lines.lty line-type lines cells, using values lty. ... arguments passed various functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print tables or text in a grid object — add.grid.table","text":"List returned following components ystart new starting point new text stop.pt null everything gets printed vp.num viewport needed next text printed xpose.table grob object can plotted.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print tables or text in a grid object — add.grid.table","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional model comparison plots, for Xpose 4 — add.model.comp","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"creates stack four plots, comparing absolute values PRED, absolute values IPRED, delta CWRES (WRES) delta IWRES estimates two specified model fits.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"","code":"add.model.comp(   object,   object.ref = NULL,   onlyfirst = FALSE,   inclZeroWRES = FALSE,   subset = xsubset(object),   main = \"Default\",   force.wres = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"object xpose.data object. object.ref xpose.data object. supplied, user prompted. onlyfirst Logical value indicating whether first row per individual included plot. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default TRUE. subset string giving subset expression applied data plotting. See xsubset. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. force.wres use WRES plots instead CWRES (logical TRUE FALSE) ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"Returns stack plots comprising comparisons absolute values PRED, absolute values IPRED, absolute differences CWRES (WRES) absolute differences IWRES two specified runs.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"Four model comparison plots displayed sequence. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for runs ## 5 and 6 in the current working directory xpdb5 <- xpose.data(5) xpdb6 <- xpose.data(6)  ## A vanilla plot, without prompts add.model.comp(xpdb5, xpdb6, prompt = FALSE)  ## Custom colours and symbols, no user IDs add.model.comp(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":null,"dir":"Reference","previous_headings":"","what":"Column-transformation functions for Xpose 4 — add_transformed_columns","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"functions transform existing Xpose 4 data columns, adding new columns.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"","code":"add.absval(object, listall = TRUE, classic = FALSE)  add.dichot(object, listall = TRUE, classic = FALSE)  add.exp(object, listall = TRUE, classic = FALSE)  add.log(object, listall = TRUE, classic = FALSE)  add.tad(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"object xpose.data object. listall logical operator specifying whether items database listed. classic logical operator specifying whether function assume classic menu system.  internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"xpose.data object (classic == FALSE) null (classic == TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"functions may used create new data columns within Xpose data object transforming existing ones.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"add.absval(): Create column containing absolute values data  another column. add.dichot(): Create categorical data column based continuous data column add.exp(): Create exponentiated version existing variable add.log(): Create log transformation existing variable add.tad(): Create time--dose (TAD) data item based upon dose time variables dataset.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"Niclas Jonsson, Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Create a column containing the absolute values of data in another  ## column add.absval(xpdb5)  ## Create a categorical data column based on a continuous data column,  ## and do not list variables  add.dichot(xpdb5, listall = FALSE)  ## Create a column containing the exponentiated values of data in  ## another column add.exp(xpdb5)  ## Create a column containing log-transformations of data in another  ## column add.log(xpdb5)  ## Create a time-after-dose column add.tad(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic internal functions for Xpose 4 — addid","title":"Generic internal functions for Xpose 4 — addid","text":"internal functions relating Xpose generic functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic internal functions for Xpose 4 — addid","text":"","code":"addid(   x,   y,   ids = ids,   idsmode = NULL,   idsext = 0.05,   idscex = 0.7,   idsdir = \"both\",   gridmode = TRUE )  computePI(   x,   y,   object,   limits = object@Prefs@Graph.prefs$PIlimits,   logy = FALSE,   logx = FALSE,   onlyfirst = FALSE,   inclZeroWRES = FALSE,   PI.subset = NULL,   subscripts )  create.rand(data, object, frac, seed = NULL)  create.strat.rand(data, object, x, y, frac, dilci, seed = NULL)  eq.xpose(x, number = 6, overlap = 0.5)  get.refrunno(database = \".ref.db\")  xpose.stack(data, object, select, rep, subset = NULL, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic internal functions for Xpose 4 — addid","text":"Internal helper functions generic Xpose functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generic internal functions for Xpose 4 — addid","text":"internal Xpose functions, adding ID numbers, computing prediction intervals, randomization, stacking, binning. intended direct use.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generic internal functions for Xpose 4 — addid","text":"Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"compound plot consisting plots weighted population residuals (WRES) vs population predictions (PRED), absolute individual weighted residuals (|IWRES|) vs independent variable (IDV), WRES vs IDV, weighted population residuals vs log(IDV), specific function Xpose 4. wrapper encapsulating arguments wres.vs.pred, iwres.vs.idv wres.vs.idv functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"","code":"addit.gof(   object,   type = \"p\",   title.size = 0.02,   title.just = c(\"center\", \"top\"),   main = \"Default\",   force.wres = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"object xpose.data object. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. title.size Amount, range 0-1, much space title take plot) title.just title justified main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. force.wres Plot WRES even residuals available. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"Returns compound plot comprising plots weighted population residuals (WRES) vs population predictions (PRED), absolute individual weighted residuals (|IWRES|) vs independent variable (IDV), WRES vs IDV, weighted population residuals vs log(IDV).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"Four additional goodness--fit plots presented side side comparison. wide array extra options controlling xyplots available. See xpose.plot.default xpose.multiple.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot addit.gof(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":null,"dir":"Reference","previous_headings":"","what":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"autocorrelation plot conditional weighted residuals, specific function Xpose 4. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"","code":"autocorr.cwres(   object,   type = \"p\",   smooth = TRUE,   ids = F,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"object xpose.data object. type 1-character string giving type plot desired. following values possible, details, see plot: '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. smooth Logical value indicating whether smooth superimposed. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"Returns autocorrelation plot conditional weighted population residuals (CWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"wide array extra options controlling xyplots available. See xpose.plot.default details. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot autocorr.cwres(xpdb)   ## A conditioning plot autocorr.cwres(xpdb, dilution=TRUE)   ## Custom heading and axis labels autocorr.cwres(xpdb, main=\"My conditioning plot\", ylb=\"|CWRES|\", xlb=\"PRED\")   ## Custom colours and symbols, IDs autocorr.cwres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.iwres.html","id":null,"dir":"Reference","previous_headings":"","what":"autocorrelation of the individual weighted residuals — autocorr.iwres","title":"autocorrelation of the individual weighted residuals — autocorr.iwres","text":"autocorrelation individual weighted residuals","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.iwres.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"autocorrelation of the individual weighted residuals — autocorr.iwres","text":"","code":"autocorr.iwres(   object,   type = \"p\",   smooth = TRUE,   ids = F,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.iwres.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"autocorrelation of the individual weighted residuals — autocorr.iwres","text":"object \"xpose.data\" object. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). main string giving plot title NULL none. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.iwres.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"autocorrelation of the individual weighted residuals — autocorr.iwres","text":"Lattice object","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":null,"dir":"Reference","previous_headings":"","what":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"autocorrelation plot weighted residuals.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"","code":"autocorr.wres(   object,   type = \"p\",   smooth = TRUE,   ids = F,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"object xpose.data object. type 1-character string giving type plot desired. following values possible, details, see plot: '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. smooth Logical value indicating whether smooth superimposed. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"Returns autocorrelation plot weighted population residuals (WRES) individual weighted residuals (IWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot autocorr.wres(xpdb)   ## A conditioning plot autocorr.wres(xpdb, dilution=TRUE)   ## Custom heading and axis labels autocorr.wres(xpdb, main=\"My conditioning plot\", ylb=\"|CWRES|\", xlb=\"PRED\")   ## Custom colours and symbols, IDs autocorr.wres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)   ## A vanilla plot with IWRES autocorr.iwres(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"compound plot consisting plots observations (DV) vs population predictions (PRED), observations (DV) vs individual predictions (IPRED), absolute individual weighted residuals (|IWRES|) vs IPRED, weighted population residuals (CWRES) vs independent variable (IDV), specific function Xpose 4. WRES also supported. wrapper encapsulating arguments dv.vs.pred, dv.vs.ipred, absval.iwres.vs.ipred wres.vs.idv functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"","code":"basic.gof(object, force.wres = FALSE, main = \"Default\", use.log = FALSE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"object xpose.data object. force.wres plots use WRES?  Values can TRUE/FALSE.  Otherwise CWRES used present. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. use.log use log transformations plots? ... arguments passed xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"Returns compound plot comprising plots observations (DV) vs population predictions (PRED), DV vs individual predictions (IPRED), absolute individual weighted residuals (|IWRES|) vs IPRED, weighted populations residuals (WRES) vs independent variable (IDV).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"Four basic goodness--fit plots presented side side comparison. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details. basic.gof.cwres just wrapper basic.gof use.cwres=TRUE.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"","code":"basic.gof(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic model comparison plots, for Xpose 4 — basic.model.comp","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"creates stack four plots, comparing PRED, IPRED, WRES (CWRES), IWRES estimates two specified model fits.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"","code":"basic.model.comp(   object,   object.ref = NULL,   onlyfirst = FALSE,   inclZeroWRES = FALSE,   subset = xsubset(object),   main = \"Default\",   force.wres = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"object xpose.data object. object.ref xpose.data object. supplied, user prompted. onlyfirst Logical value indicating whether first row per individual included plot. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default TRUE. subset string giving subset expression applied data plotting. See xsubset. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. force.wres Force function use WRES? ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"Returns stack plots comprising comparisons PRED, IPRED, WRES (CWRES) IWRES two specified runs.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"Four basic model comparison plots displayed sequence. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for runs ## 5 and 6 in the current working directory xpdb5 <- xpose.data(5) xpdb6 <- xpose.data(6)  ## A vanilla plot, without prompts basic.model.comp(xpdb5, xpdb6, prompt = FALSE)  ## Custom colours and symbols, no user IDs basic.model.comp.cwres(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"Reads results bootstrap tool PsN creates histograms.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"","code":"boot.hist(   results.file = \"raw_results_run1.csv\",   incl.ids.file = \"included_individuals1.csv\",   min.failed = FALSE,   cov.failed = FALSE,   cov.warnings = FALSE,   boundary = FALSE,   showOriginal = TRUE,   showMean = FALSE,   showMedian = FALSE,   showPCTS = FALSE,   PCTS = c(0.025, 0.975),   excl.id = c(),   layout = NULL,   sort.plots = TRUE,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"results.file location results file bootstrap tool PsN incl.ids.file location included ids file bootstrap tool PsN min.failed NONMEM runs failed minimization skipped? TRUE FALSE cov.failed NONMEM runs failed covariance step skipped? TRUE FALSE cov.warnings NONMEM runs covariance step warnings skipped? TRUE FALSE boundary NONMEM runs boundary warnings skipped? TRUE FALSE showOriginal show value original NONMEM run histograms? TRUE FALSE showMean show mean histogram data? TRUE FALSE showMedian show median histogram data? TRUE FALSE showPCTS show percentiles histogram data? TRUE FALSE PCTS percentiles show.  Can vector length.  example, c(0.05,0.2,0.5,0.7) excl.id Vector id numbers exclude. layout Layout plots.  vector number rows columns plot. c(3,3) example. sort.plots plots sorted based type parameter. Sorting parameters, standard errors, shrinkage eigenvalues. main title plot. ... Additional arguments can passed xpose.plot.histogram, xpose.panel.histogram, histogram lattice-package functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"lattice object","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"PsN","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"","code":"if (FALSE) { boot.hist(results.file=\"./boot1/raw_results_run1.csv\",            incl.ids.file=\"./boot1/included_individuals1.csv\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary information for a bootgam or bootscm — bootgam.print","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"functions prints summary information bootgam performed Xpose, bootscm performed PsN.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"","code":"bootgam.print(bootgam.obj = NULL)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"bootgam.obj bootgam bootscm object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"value returned","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"","code":"if (FALSE) { bootgam.print(current.bootgam)  # Print summary for the current Xpose bootgam object bootgam.print(current.bootscm)  # Print summary for the current Xpose bootscm object }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootscm.import.html","id":null,"dir":"Reference","previous_headings":"","what":"Import bootscm data into R/Xpose — bootscm.import","title":"Import bootscm data into R/Xpose — bootscm.import","text":"function imports data generated PsN boot_scm function Xpose / R environment.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootscm.import.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import bootscm data into R/Xpose — bootscm.import","text":"","code":"bootscm.import(   scm.folder = NULL,   silent = FALSE,   n.bs = NULL,   cov.recoding = NULL,   group.by.cov = NULL,   skip.par.est.import = FALSE,   dofv.forward = 3.84,   dofv.backward = 6.64,   runno = NULL,   return.obj = FALSE )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootscm.import.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import bootscm data into R/Xpose — bootscm.import","text":"scm.folder folder PsN-generated bootscm data . silent output progress report. Default FALSE. n.bs number bootstraps performed. Defaults 100. cov.recoding categorical covariates recoded dichotomous covariates within bootscm configuration file, list can specified containing data frames recoding. See example details. group..cov Group inclusion frequencies covariate, instead calculating per parameter-covariates relationship. Default NULL, means user asked make choice. skip.par.est.import Skip import parameter estimates (final model scm's, well parameter estimates first step scm). data required make plot show inclusion bias correlation parameter estimates. Importing data takes bit time (may take minute ), intend make plots anyhow step can skipped. Default FALSE. dofv.forward dOFV value used forward step scm. dofv.backward dOFV value used backward step scm. runno run-number base model bootSCM. return.obj bootscm object returned function?","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootscm.import.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import bootscm data into R/Xpose — bootscm.import","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":null,"dir":"Reference","previous_headings":"","what":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"Categorical observations vs. independent variable using stacked bars.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"","code":"cat.dv.vs.idv.sb(   object,   dv = xvardef(\"dv\", object),   idv = xvardef(\"idv\", object),   by = NULL,   groups = dv,   force.by.factor = FALSE,   recur = F,   xlb = idv,   ylb = \"Proportion\",   subset = NULL,   vary.width = T,   level.to.plot = NULL,   refactor.levels = TRUE,   main = xpose.create.title.text(idv, dv, \"Proportions of\", object, subset = subset, ...),   stack = TRUE,   horizontal = FALSE,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   scales = list(),   inclZeroWRES = TRUE,   onlyfirst = FALSE,   samp = NULL,   aspect = object@Prefs@Graph.prefs$aspect,   auto.key = \"Default\",   mirror = FALSE,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"object Xpose data object. dv dependent variable (e.g. \"DV\" \"CP\".) idv independent variable (e.g. \"TIME\".) Conditioning variable groups group values conditional plot. force..factor force data treated factors? recur used. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. subset Subset data. vary.width vary width bars match amount information? level..plot levels DV plot. refactor.levels refactor levels? main title plot. stack stack bars? horizontal bars horizontal? strip Defining strips appear conditioning plots. scales Scales argument xyplot. inclZeroWRES Include rows WRES=0? onlyfirst include first data point individual? samp Sample use mirror plot (number). aspect Aspect argument xyplot. auto.key Make legend. mirror Mirror can FALSE, TRUE, 1 3. mirror.aspect Aspect mirror. pass.plot.list plot list passed back user? x.cex Size x axis label. y.cex Size Y axis label. main.cex Size Title. mirror.internal Internal stuff. ... arguments passed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"","code":"if (FALSE) { ## read in table files runno <- 45 xpdb <- xpose.data(runno)  ## make some stacked bar plots cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F) cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F,by=\"DOSE\") cat.dv.vs.idv.sb(xpdb,idv=\"DOSE\") cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F,by=\"TIME\") cat.dv.vs.idv.sb(xpdb,idv=\"TIME\") cat.dv.vs.idv.sb(xpdb,idv=\"CAVH\") cat.dv.vs.idv.sb(xpdb,idv=\"TIME\",by=\"DOSE\",scales=list(x=list(rot=45)))  ## make some mirror plots cat.dv.vs.idv.sb(xpdb,idv=\"DOSE\",mirror=1) cat.dv.vs.idv.sb(xpdb,idv=\"CAVH\",mirror=1,auto.key=F) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":null,"dir":"Reference","previous_headings":"","what":"Categorical (visual) predictive check. — cat.pc","title":"Categorical (visual) predictive check. — cat.pc","text":"Categorical (visual) predictive check plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Categorical (visual) predictive check. — cat.pc","text":"","code":"cat.pc(   object,   dv = xvardef(\"dv\", object),   idv = xvardef(\"idv\", object),   level.to.plot = NULL,   subset = NULL,   histo = T,   median.line = F,   PI.lines = F,   xlb = if (histo) {      paste(\"Proportion of \", dv)  } else {      paste(idv)  },   ylb = if (histo) {      paste(\"Percent of Total\")  } else {          paste(\"Proportion of Total\")  },   main = xpose.create.title.text(NULL, dv, \"Predictive check of\", object, subset =     subset, ...),   strip = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Categorical (visual) predictive check. — cat.pc","text":"object Xpose data object. dv dependent variable (e.g. \"DV\" \"CP\".) idv independent variable (e.g. \"TIME\".) level..plot levels plot. subset Subset data. histo FALSE VPC created, given idv defined. median.line Make median line? PI.lines Make prediction interval lines? xlb Label x axis. ylb label y axis. main Main title. strip Defining strips appear conditioning plots. ... Extra arguments passed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Categorical (visual) predictive check. — cat.pc","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Categorical (visual) predictive check. — cat.pc","text":"","code":"if (FALSE) { ## read in table files runno <- 45 xpdb <- xpose.data(runno)  ## create proportion (visual) predictive check cat.pc(xpdb,idv=NULL) cat.pc(xpdb,idv=\"DOSE\") cat.pc(xpdb,idv=\"DOSE\",histo=F) cat.pc(xpdb,idv=\"TIME\",histo=T,level.to.plot=1) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic table functions for Xpose 4 — categorical.table","title":"Generic table functions for Xpose 4 — categorical.table","text":"internal table functions relating Xpose summary functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic table functions for Xpose 4 — categorical.table","text":"","code":"categorical.table(   object,   vars,   onlyfirst = TRUE,   subset = xsubset(object),   inclZeroWRES = FALSE,   miss = object@Prefs@Miss )  continuous.table(   object,   vars,   onlyfirst = TRUE,   subset = xsubset(object),   inclZeroWRES = FALSE,   miss = object@Prefs@Miss )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic table functions for Xpose 4 — categorical.table","text":"Internal helper functions generic Xpose summary functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generic table functions for Xpose 4 — categorical.table","text":"internal Xpose functions outputting summary tables. intended direct use.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generic table functions for Xpose 4 — categorical.table","text":"Niclas Jonsson, Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":null,"dir":"Reference","previous_headings":"","what":"Change parameter scope. — change.parm","title":"Change parameter scope. — change.parm","text":"Function change parameter scope.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change parameter scope. — change.parm","text":"","code":"change.parm(object, listall = TRUE, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change parameter scope. — change.parm","text":"object xpose data object. listall whether list current parameters. classic true used classic menu system (internal use).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change parameter scope. — change.parm","text":"classic return nothing.  Otherwise return new data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change parameter scope. — change.parm","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":null,"dir":"Reference","previous_headings":"","what":"Changes the name of an Xpose data item — change.var.name","title":"Changes the name of an Xpose data item — change.var.name","text":"function allows names data items Xpose database changed.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Changes the name of an Xpose data item — change.var.name","text":"","code":"change.var.name(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Changes the name of an Xpose data item — change.var.name","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Changes the name of an Xpose data item — change.var.name","text":"xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Changes the name of an Xpose data item — change.var.name","text":"function facilitates changing data item names object@Data slot.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Changes the name of an Xpose data item — change.var.name","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Changes the name of an Xpose data item — change.var.name","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  xpdb5 <- change.var.name(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":null,"dir":"Reference","previous_headings":"","what":"Changes the label of an Xpose data item — change.xlabel","title":"Changes the label of an Xpose data item — change.xlabel","text":"function allows labels data items Xpose database changed.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Changes the label of an Xpose data item — change.xlabel","text":"","code":"change.xlabel(object, listall = TRUE, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Changes the label of an Xpose data item — change.xlabel","text":"object xpose.data object. listall logical operator specifying whether items database listed. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Changes the label of an Xpose data item — change.xlabel","text":"xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Changes the label of an Xpose data item — change.xlabel","text":"function facilitates changing data item labels object@Prefs@Labels slot.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Changes the label of an Xpose data item — change.xlabel","text":"Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Changes the label of an Xpose data item — change.xlabel","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  xpdb5 <- change.xlabel(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":null,"dir":"Reference","previous_headings":"","what":"Change Xpose variable definitions. — change.xvardef","title":"Change Xpose variable definitions. — change.xvardef","text":"functions allow changing Xpose variable definitions like \"idv\" \"dv\". variable definitions used refer columns observed data generic way, generic plotting functions can created.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change Xpose variable definitions. — change.xvardef","text":"","code":"change.xvardef(   object,   var = \".ask\",   def = \".ask\",   listall = TRUE,   classic = FALSE,   check.var = FALSE,   ... )  change.xvardef(   object,   var,   listall = FALSE,   classic = FALSE,   check.var = FALSE,   ... ) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change Xpose variable definitions. — change.xvardef","text":"object xpose.data object. var Xpose variable like change add current object. one-element character vector (e.g. \"idv\").  \".ask\" user prompted input value. def vector column names NONMEM table files (names(object@Data)) associated variable (e.g. c(\"TIME\")).  Multiple values allowed. \".ask\" user prompted input values. listall function list database values? classic function used classic interface.  internal option. check.var variables checked current variables object? ... Items passed functions within function. value vector values","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change Xpose variable definitions. — change.xvardef","text":"called command line function returns xpose database.  called classic interface function updates current xpose database (.cur.db).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Change Xpose variable definitions. — change.xvardef","text":"change.xvardef(   object,   var,   listall = FALSE,   classic = FALSE,   check.var = FALSE,   ... ) <- value: Change covariate scope xpose database object","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"additional-arguments","dir":"Reference","previous_headings":"","what":"Additional arguments","title":"Change Xpose variable definitions. — change.xvardef","text":"default xpose variables : id Individual identifier column dataset idlab values used plotting ID values data points plots occ occasion variable dv dv variable pred pred variable ipred ipred variable wres wres variable cwres cwres variable res res variable parms parameters database covariates covariates database ranpar random parameters database","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change Xpose variable definitions. — change.xvardef","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change Xpose variable definitions. — change.xvardef","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  # Change the \"id\" variable to point to \"PRED\" in the xpose object xpdb <- change.xvardef(xpdb,var=\"id\",def=\"PRED\")  # Check the value of the \"id\" variable xvardef(\"id\",xpdb) #> [1] \"PRED\"  # Change the \"idv\" variable change.xvardef(xpdb,var=\"idv\") <- \"TIME\"  # Change the covariate scope change.xvardef(xpdb,var=\"covariates\") <- c(\"SEX\",\"AGE\",\"WT\")  if (FALSE) { # Use the interactive capabilities of the function xpdb <- change.xvardef(xpdb) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"functions allow customization Xpose's graphics settings.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"","code":"change.ab.graph.par(object, classic = FALSE)  change.bw.graph.par(object, classic = FALSE)  change.cond.graph.par(object, classic = FALSE)  change.dil.graph.par(object, classic = FALSE)  change.label.par(object, classic = FALSE)  change.lm.graph.par(object, classic = FALSE)  change.misc.graph.par(object, classic = FALSE)  change.pi.graph.par(object, classic = FALSE)  change.smooth.graph.par(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"xpose.data object (classic == FALSE) null (classic == TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"Settings can saved loaded using export.graph.par import.graph.par, respectively.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"change.ab.graph.par(): change settings line identity. change.bw.graph.par(): sets preferences box--whisker plots change.cond.graph.par(): sets preferences conditioning change.dil.graph.par(): responsible dilution preferences change.label.par(): responsible labelling preferences change.lm.graph.par(): responsible linear regression lines. change.misc.graph.par(): sets basic graphics parameters, including plot type, point type size, colour, line type, line width. change.pi.graph.par(): responsible prediction interval plotting preferences change.smooth.graph.par(): sets preferences loess smooths.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Change default miscellaneous graphic preferences xpdb5 <- change.misc.graph.par(xpdb5)  ## Change default linear regression line preferences, creating a new  ## object xpdb5.a <- change.lm.graph.par(xpdb5)  ## Change conditioning preferences xpdb5 <- change.cond.graph.par(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"functions allow viewing changing settings relating subsets, categorical threshold values, documentation numbers indicating missing data values.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"","code":"change.cat.cont(   object,   listall = TRUE,   classic = FALSE,   to.cat.vec = NULL,   to.cont.vec = NULL,   change.type.vec = NULL,   ... )  change.cat.cont(   object,   listall = TRUE,   classic = FALSE,   to.cat.vec = NULL,   to.cont.vec = NULL,   ... ) <- value  change.cat.levels(object, classic = FALSE, cat.limit = NULL, ...)  change.cat.levels(object, classic = FALSE, ...) <- value  change.dv.cat.levels(object, classic = FALSE, dv.cat.limit = NULL, ...)  change.dv.cat.levels(object, classic = FALSE, ...) <- value  change.miss(object, classic = FALSE)  change.subset(object, classic = FALSE)  get.doc(object, classic = FALSE)  set.doc(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"object xpose.data object. listall logical operator specifying whether items database listed. classic logical operator specifying whether function assume classic menu system. internal option need never called command line. .cat.vec vector strings specifying names categorical variables transformed continuous. .cont.vec vector strings specifying names continuous variables transformed categorical. change.type.vec vector strings specifying names variables transformed /continuous/categorical. ... arguments passed functions. value value replaced xpose data object object.  value used “replacement function” version functions.  form function.name(object) <- value.  value NULL functions prompt user value.  change.cat.levels, value categorical limit cat.limit.  change.dv.cat.levels, value DV categorical limit dv.cat.limit.  change.cat.cont, value change.type.vec.  See examples . cat.limit limit treat list values categorical.  cat.limit less unique values list treated categorical. dv.cat.limit limit treat DV categorical.  dv.cat.limit less unique dv values dv treated categorical.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"xpose.data object, except get.doc, returns value object@Doc.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"change.cat.cont(): allows interchange categorical continuous data formats within Xpose database. turn affects plots drawn. change.cat.cont(   object,   listall = TRUE,   classic = FALSE,   .cat.vec = NULL,   .cont.vec = NULL,   ... ) <- value: allows interchange categorical continuous data formats within Xpose database. turn affects plots drawn. change.cat.levels(): change settings number unique data values required variable order define continuous ordinary variables. change.cat.levels(object, classic = FALSE, ...) <- value: change settings number unique data values required variable order define continuous ordinary variables. change.dv.cat.levels(): change settings number unique data values required variable order define continuous dependent variable. change.dv.cat.levels(object, classic = FALSE, ...) <- value: change settings number unique data values required variable order define continuous dependent variable. change.miss(): change value use 'missing'. change.subset(): used setting data item's subset field.  specify subset data process, use variable names regular R selection operators. combine subset two variables, selection expressions two variables combined using R's unary logical operators. variable names specified NONMEM table files (e.g. PRED, TIME, SEX). selection operators : == (equal) != (equal) || () > (greater ) < (less ) example, specify TIME less 24 processed, type expression: TIME < 24. unary logical operators : & () | () example, specify TIME less 24 males (SEX equal 1), type expression: TIME < 24 & SEX == 1 subset selection scheme works variables, including ID numbers. subset selection entirely stable. example, check user enters valid expression, user specifies existing variable names. erroneous expression become evident plot attempted expression takes effect. get.doc(): get documentation field Xpose data object. set.doc(): set documentation field Xpose data object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"Andrew Hooker, Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Change default subset xpdb5 <- change.subset(xpdb5)  ## Set documentation field xpdb5 <- set.doc(xpdb5) ## View it view.doc(xpdb5)  ## change the categorical limit for the dv variable change.dv.cat.levels(xpdb5) <- 10  ## change the categorical limit for non DV variables change.cat.levels(xpdb5) <- 2 ## or xpdb5 <- change.cat.levels(xpdb5,cat.levels=2)  ## chnage variables from categorical to continuous xpdb5 <- change.cat.cont(xpdb5,to.cat.vec=c(\"AGE\"),to.cont.vec=c(\"SEX\")) xpdb5 <- change.cat.cont(xpdb5,change.type.vec=c(\"AGE\",\"SEX\")) change.cat.cont(xpdb5) <- c(\"AGE\",\"SEX\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Data functions for Xpose 4 — check.vars","title":"Data functions for Xpose 4 — check.vars","text":"functions perform various tasks managing Xpose data objects.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data functions for Xpose 4 — check.vars","text":"","code":"check.vars(vars, object, silent = FALSE)  is.readable.file(filename)  test.xpose.data(object)  xpose.bin(data, y, bins = 10)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data functions for Xpose 4 — check.vars","text":"vars List variables checked. object xpose.data object. silent logical operator specifying whether output displayed. filename filename checked readability.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Data functions for Xpose 4 — check.vars","text":"TRUE, FALSE NULL.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data functions for Xpose 4 — check.vars","text":"internal Xpose functions, intended direct use.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Data functions for Xpose 4 — check.vars","text":"Niclas Jonsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the Conditional Weighted Residuals — compute.cwres","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"function computes conditional weighted residuals (CWRES) NONMEM run.  CWRES extension weighted residuals (WRES), calculated based first-order conditional estimation (FOCE) method linearizing pharmacometric model (WRES calculated based first-order (FO) method). function requires NONMEM table file extra output file must explicitly asked running NONMEM, see details .","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"","code":"compute.cwres(   run.number,   tab.prefix = \"cwtab\",   sim.suffix = \"\",   est.tab.suffix = \".est\",   deriv.tab.suffix = \".deriv\",   old.file.convention = FALSE,   id = \"ALL\",   printToOutfile = TRUE,   onlyNonZero = TRUE,   ... )  xpose.calculate.cwres(   object,   cwres.table.prefix = \"cwtab\",   tab.suffix = \"\",   sim.suffix = \"sim\",   est.tab.suffix = \".est\",   deriv.tab.suffix = \".deriv\",   old.file.convention = FALSE,   id = \"ALL\",   printToOutfile = TRUE,   onlyNonZero = FALSE,   classic = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"run.number run number NONMEM CWRES calculated. tab.prefix prefix two NONMEM file containing needed values computation CWRES, described details section. sim.suffix suffix ,\".\", NONMEM file containing needed values computation CWRES, described details section. example, table files might named cwtab1sim.est cwtab1sim.deriv, case sim.suffix=\"sim\". est.tab.suffix suffix, \".\", NONMEM file containing estimated parameter values needed CWRES calculation. deriv.tab.suffix suffix, \".\", NONMEM file containing derivatives model respect random parameters needed CWRES calculation. old.file.convention backwards compatibility.  Use using previous file convention CWRES (table files named cwtab1, cwtab1.50, cwtab1.51, ... , cwtab.58 example). id Can either \"\" number matching ID label datasetname. Value fixed \"\" xpose.calculate.cwres. printToOutfile Logical (TRUE/FALSE) indicating whether CWRES values calculated appended copy datasetname. works id=\"\".  chosen resulting output file datasetname.cwres.  Value fixed TRUE xpose.calculate.cwres. onlyNonZero Logical (TRUE/FALSE) indicating return value (CWRES values) compute.cwres include zero values associated non-measurement lines NONMEM data file. ... arguments passed basic functions code. object xpose.data object. cwres.table.prefix prefix NONMEM table file containing derivative model respect etas epsilons, described details section. tab.suffix suffix NONMEM table file containing derivative model respect etas epsilons, described details section. classic Indicates function used classic menu system.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"xpose.calculate.cwres Returns Xpose data object contains CWRES. simulated data present, CWRES also calculated data.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"function  reads following two files: paste(tab.prefix,run.number,sim.suffix,est.tab.suffix,sep=\"\") paste(tab.prefix,run.number,sim.suffix,deriv.tab.suffix,sep=\"\") might example: (depending input values function) returns CWRES vector form well creating new table file named: paste(tab.prefix,run.number,sim.suffix,sep=\"\") might example:","code":"cwtab1.est cwtab1.deriv cwtab1"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"xpose.calculate.cwres(): function wrapper around function compute.cwres.  computes CWRES model file associated Xpose data object input function.  possible also computes CWRES simulated data associated current Xpose data object.  problems function try using compute.cwres rereading dataset Xpose.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"setting-up-the-nonmem-model-file","dir":"Reference","previous_headings":"","what":"Setting up the NONMEM model file","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"order function calculate CWRES, NONMEM must run requesting certain tables files created.  files created differs depending using $PRED ADVAN well version NONMEM using. procedures known work NONMEM VI may different NONMEM V NONMEM VII.  attempted indicate NONMEM V may different, extensively tested!  NONMEM VII CWRES calculated internally function rarely needed. procedure can done automatically using Perl Speaks NONMEM (PsN) highly recommend using PsN purpose.  installing PsN just type 'execute [modelname] -cwres'.  See http://psn.sourceforge.net details. five main insertions needed NONMEM control file: $ABB COMRES=X. Insert line directly $DATA line.  value X number ETA() terms plus number EPS() terms model.  example model three ETA() terms two EPS() terms code look like :  Verbatim code. Using ADVAN. using ADVAN routines model, Verbatim code inserted directly $ERROR section model file.  length code depends number ETA() terms EPS() terms model.  ETA(y) model corresponding term G(y,1) must assign COM() variable.  EPS(y) model, corresponding HH(y,1) term must assign COM() variable. example model using ADVAN routines three ETA() terms two EPS() terms code look like :  Using PRED. using $PRED, verbatim code inserted directly $PRED section model file.  ETA(y) model corresponding term G(y,1) must assign COM() variable. EPS(y) model, corresponding H(y,1) term must assign COM() variable. code look like three ETA() terms two EPS() terms:   INFN routine. Using ADVAN NONMEM VI higher. using ADVAN routines model, $INFN section placed directly $PK section using following code.  example assuming model file named something like 'run1.mod', thus prefix file names 'cwtab' run number attached (.e. 'cwtab1').  changed new run number.  Using ADVAN NONMEM V. using ADVAN routines model, need use INFN subroutine.  call INFN subroutine 'myinfn.' $SUBS line model file include INFN option.  , using ADVAN2 TRANS2 model file $SUBS line look like:  'myinfn.' routine 4 thetas, 3 etas 1 epsilon shown . model different numbers thetas, etas epsilons values NTH, NETA, NEPS, changed respectively.  vales found DATA statement subroutine.  additionally, example assuming model file named something like 'run1.mod', thus prefix output file names ('cwtab') subroutine run number attached (.e. 'cwtab1').  number changed new run number (see line beginning 'OPEN').  Using $PRED NONMEM VI higher. using $PRED, following code placed end $PRED section model file (together verbatim code).  example assuming model file named something like 'run1.mod', thus prefix file names 'cwtab' run number attached (.e. 'cwtab1').  changed new run number.  Using $PRED NONMEM V. using $PRED NONMEM V, need add verbatim code immediately $PRED command.  example assume 4 thetas, 3 etas 1 epsilon.  model different numbers thetas, etas epsilons values NTH, NETA, NEPS, changed respectively.  vales found DATA statement . verbatim code add abbreviated code needed $PRED routine model file.  abbreviated code verbatim code needed.  verbatim code added verbatim code discussed point 2.  example assuming model file named something like 'run1.mod', thus prefix output file names ('cwtab') run number attached (.e. 'cwtab1').  number changed new run number (see line beginning 'OPEN').   cwtab*.deriv table file. special table file needs created print values contained COMRES variables.  addition ID, IPRED, MDV, DV, PRED RES data items needed computation CWRES.  following code added NONMEM model file.  example continue assume using model three ETA() terms two EPS() terms, extra terms added new ETA() EPS() terms model file.  also assume model file named something like 'run1.mod', thus prefix file names 'cwtab' run number attached (.e. 'cwtab1').  changed new run number.  $ESTIMATION. compute CWRES, NONMEM model file must use (least) FO method POSTHOC step.  FO method used POSTHOC step included CWRES values equivalent WRES.  CWRES calculations based FOCE approximation, consequently give idea ability FOCE method fit model data. using another method parameter estimation (e.g. FOCE interaction), CWRES calculated based model linearization procedure.","code":"$DATA temp.csv IGNORE=@  $ABB COMRES=5  $INPUT ID TIME DV MDV AMT EVID  $SUB ADVAN2 TRANS2 \"LAST  \"  COM(1)=G(1,1)  \"  COM(2)=G(2,1)  \"  COM(3)=G(3,1)  \"  COM(4)=HH(1,1)  \"  COM(5)=HH(2,1) \"LAST  \"  COM(1)=G(1,1)  \"  COM(2)=G(2,1)  \"  COM(3)=G(3,1)  \"  COM(4)=H(1,1)  \"  COM(5)=H(2,1) $INFN IF (ICALL.EQ.3) THEN   OPEN(50,FILE='cwtab1.est')   WRITE(50,*) 'ETAS'   DO WHILE(DATA)                                                            IF (NEWIND.LE.1) WRITE (50,*) ETA                                       ENDDO                                                                   WRITE(50,*) 'THETAS'   WRITE(50,*) THETA   WRITE(50,*) 'OMEGAS'   WRITE(50,*) OMEGA(BLOCK)   WRITE(50,*) 'SIGMAS'   WRITE(50,*) SIGMA(BLOCK) ENDIF $SUB ADVAN2 TRANS2 INFN=myinfn.for SUBROUTINE INFN(ICALL,THETA,DATREC,INDXS,NEWIND)      DIMENSION THETA(*),DATREC(*),INDXS(*)      DOUBLE PRECISION THETA      COMMON /ROCM6/ THETAF(40),OMEGAF(30,30),SIGMAF(30,30)      COMMON /ROCM7/ SETH(40),SEOM(30,30),SESIG(30,30)      COMMON /ROCM8/ OBJECT      COMMON /ROCM9/ IERE,IERC      DOUBLE PRECISION THETAF, OMEGAF, SIGMAF      DOUBLE PRECISION OBJECT      REAL SETH,SEOM,SESIG      DOUBLE PRECISION ETA(10)      INTEGER J,I      INTEGER IERE,IERC      INTEGER MODE      INTEGER NTH,NETA,NEPS      DATA NTH,NETA,NEPS/4,3,1/      IF (ICALL.EQ.0) THEN C      open files here, if necessary        OPEN(50,FILE='cwtab1.est')      ENDIF      IF (ICALL.EQ.3) THEN        MODE=0        CALL PASS(MODE)        MODE=1        WRITE(50,*) 'ETAS' 20     CALL PASS(MODE)        IF (MODE.EQ.0) GO TO 30        IF (NEWIND.NE.2) THEN          CALL GETETA(ETA)          WRITE (50,97) (ETA(I),I=1,NETA)        ENDIF        GO TO 20 30     CONTINUE        WRITE (50,*) 'THETAS'        WRITE (50,99) (THETAF(J),J=1,NTH)        WRITE(50,*) 'OMEGAS'        DO 7000 I=1,NETA 7000     WRITE (50,99) (OMEGAF(I,J),J=1,NETA)        WRITE(50,*) 'SIGMAS'        DO 7999 I=1,NEPS 7999     WRITE (50,99) (SIGMAF(I,J),J=1,NEPS)      ENDIF 99   FORMAT (20E15.7) 98   FORMAT (2I8) 97   FORMAT (10E15.7)      RETURN      END IF (ICALL.EQ.3) THEN   OPEN(50,FILE='cwtab1.est')   WRITE(50,*) 'ETAS'   DO WHILE(DATA)                                                            IF (NEWIND.LE.1) WRITE (50,*) ETA                                       ENDDO                                                                   WRITE(50,*) 'THETAS'   WRITE(50,*) THETA   WRITE(50,*) 'OMEGAS'   WRITE(50,*) OMEGA(BLOCK)   WRITE(50,*) 'SIGMAS'   WRITE(50,*) SIGMA(BLOCK) ENDIF $PRED \"FIRST   \"     COMMON /ROCM6/ THETAF(40),OMEGAF(30,30),SIGMAF(30,30)  \"     COMMON /ROCM7/ SETH(40),SEOM(30,30),SESIG(30,30)  \"     COMMON /ROCM8/ OBJECT  \"     DOUBLE PRECISION THETAF, OMEGAF, SIGMAF  \"     DOUBLE PRECISION OBJECT  \"     REAL SETH,SEOM,SESIG  \"     INTEGER J,I  \"     INTEGER MODE  \"     INTEGER NTH,NETA,NEPS  \"     DATA NTH,NETA,NEPS/4,3,1/ \"     IF (ICALL.EQ.0) THEN  \"C    open files here, if necessary  \"       OPEN(50,FILE='cwtab1.est')  \"     ENDIF  \"     IF (ICALL.EQ.3) THEN  \"       MODE=0  \"       CALL PASS(MODE)  \"       MODE=1  \" \t     WRITE(50,*) 'ETAS'  \"20     CALL PASS(MODE)  \"       IF (MODE.EQ.0) GO TO 30  \"       IF (NEWIND.NE.2) THEN  \"         CALL GETETA(ETA)  \"         WRITE (50,97) (ETA(I),I=1,NETA)  \"       ENDIF  \"       GO TO 20  \"30     CONTINUE  \"       WRITE (50,*) 'THETAS'  \"       WRITE (50,99) (THETAF(J),J=1,NTH)  \"       WRITE (50,*) 'OMEGAS'  \"       DO 7000 I=1,NETA  \"7000     WRITE (50,99) (OMEGAF(I,J),J=1,NETA)  \"       WRITE (50,*) 'SIGMAS'  \"       DO 7999 I=1,NEPS  \"7999     WRITE (50,99) (SIGMAF(I,J),J=1,NEPS)  \"     ENDIF  \"99   FORMAT (20E15.7)  \"98   FORMAT (2I8)  \"97   FORMAT (10E15.7) $TABLE ID COM(1)=G11 COM(2)=G21 COM(3)=G31 COM(4)=H11 COM(5)=H21         IPRED MDV NOPRINT ONEHEADER FILE=cwtab1.deriv"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"Hooker AC, Staatz CE, Karlsson MO. Conditional weighted residuals, improved model diagnostic FO/FOCE methods. PAGE 15 (2006) Abstr 1001 [http://www.page-meeting.org/?abstract=1001]. Hooker AC, Staatz CE Karlsson MO, Conditional weighted residuals (CWRES):  model diagnostic FOCE method, Pharm Res, 24(12): p. 2187-97, 2007, [doi:10.1007/s11095-007-9361-x ].","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"","code":"if (FALSE) { ## Capture CWRES from cwtab5.est and cwtab5.deriv cwres <- compute.cwres(5) mean(cwres) var(cwres)  ## Capture CWRES from cwtab1.est and cwtab1.deriv, do not print out, allow zeroes cwres <- compute.cwres(\"1\", printToOutFile = FALSE,   onlyNonZero = FALSE)  ## Capture CWRES for ID==1 cwres.1 <- compute.cwres(\"1\", id=1)  ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Compare WRES, CWRES xpdb5 <- xpose.calculate.cwres(xpdb5) cwres.wres.vs.idv(xpdb5)   }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"function takes generic plotting functions Xpose 4 calls multiple times current arguments functions, changing arguments needed mirror plotting.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"","code":"create.mirror(   fun,   arg.list,   mirror,   plotTitle,   fix.y.limits = TRUE,   fix.x.limits = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"fun function name call multiple times arg.list arguments function mirror type mirror plots desired (1 3 mirror plots can created) plotTitle title plots fix.y.limits fix y axes ? fix.x.limits fix x axes ? ... additional arguments passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"list plots, NULL.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"mostly internal function Xpose","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.xpose.plot.classes.html","id":null,"dir":"Reference","previous_headings":"","what":"Create xpose.multiple.plot class. — create.xpose.plot.classes","title":"Create xpose.multiple.plot class. — create.xpose.plot.classes","text":"Creates class viewing plotting xpose plots multiple plots page multiple pages.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.xpose.plot.classes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create xpose.multiple.plot class. — create.xpose.plot.classes","text":"","code":"create.xpose.plot.classes()"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.xpose.plot.classes.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create xpose.multiple.plot class. — create.xpose.plot.classes","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":null,"dir":"Reference","previous_headings":"","what":"This function creates the Xpose data classes (","title":"This function creates the Xpose data classes (","text":"function defines sets Xpose data classes.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This function creates the Xpose data classes (","text":"","code":"createXposeClasses(nm7 = F)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This function creates the Xpose data classes (","text":"nm7 FALSE using NONMEM 7.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"This function creates the Xpose data classes (","text":"default settings defined function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This function creates the Xpose data classes (","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"histogram distribution conditional weighted residuals (CWRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.histogram function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"","code":"cwres.dist.hist(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"object xpose.data object. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"Returns histogram conditional weighted residuals (CWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"Displays histogram conditional weighted residuals (CWRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot cwres.dist.hist(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"QQ plot distribution conditional weighted residuals (CWRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.qq function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"","code":"cwres.dist.qq(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"object xpose.data object. ... arguments passed link{xpose.plot.qq}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"Returns QQ plot conditional weighted residuals (CWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"Displays QQ plot conditional weighted residuals (CWRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"","code":"cwres.dist.qq(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"creates stack plots conditional weighted residuals (CWRES) plotted covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default xpose.plot.histogram functions. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"","code":"cwres.vs.cov(   object,   ylb = \"CWRES\",   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"object xpose.data object. ylb string giving label y-axis. NULL none. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default} link{xpose.plot.histogram}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"Returns stack xyplots histograms CWRES versus covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots histograms available. See xpose.plot.default xpose.plot.histogram details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  cwres.vs.cov(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"creates box whisker plot conditional weighted residuals (CWRES) vs independent variable (IDV), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"","code":"cwres.vs.idv.bw(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"object xpose.data object. ... arguments passed link{xpose.plot.bw}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"Returns stack box--whisker plots CWRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"creates box whisker plot conditional weighted residuals (CWRES) vs independent variable (IDV), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling bwplots available. See xpose.plot.bw xpose.panel.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  cwres.vs.idv.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"plot population conditional weighted residuals (CWRES) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"","code":"cwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"Returns xyplot CWRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"Conditional weighted residuals (CWRES) plotted independent variable, specified object@Prefs@Xvardef$idv. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot cwres.vs.idv(xpdb)   ## A conditioning plot cwres.vs.idv(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"creates box whisker plot conditional weighted residuals (CWRES) vs population predictions (PRED), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"","code":"cwres.vs.pred.bw(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"object xpose.data object. ... arguments passed link{xpose.plot.bw}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"Returns box--whisker plot CWRES vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"creates box whisker plot conditional weighted residuals (CWRES) vs population predictions (PRED), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling bwplots available. See xpose.plot.bw xpose.panel.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  cwres.vs.pred.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"plot population conditional weighted residuals (cwres) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"","code":"cwres.vs.pred(object, abline = c(0, 0), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"Returns xyplot CWRES vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  cwres.vs.pred(xpdb)   ## A conditioning plot cwres.vs.pred(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"graphical comparison  WRES CWRES plotted independent variable. Conditional weighted residuals (CWRES) require  extra steps calculate. Either add CWRES NONMEM  table files compute using information proveded  compute.cwres. wide array extra options controlling  xyplots available. See xpose.plot.default  xpose.panel.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"","code":"cwres.wres.vs.idv(   object,   ylb = \"Residuals\",   abline = c(0, 0),   smooth = TRUE,   scales = list(),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"object xpose.data object. ylb string giving label y-axis. NULL none. abline Vector arguments panel.abline function. abline drawn NULL. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. scales scales passed  xpose.plot.default. ... arguments passed xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"compound xyplot.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"Niclas Jonsson & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"","code":"cwres.wres.vs.idv(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"Graphically compares WRES CWRES plotted population predictions.Conditional weighted residuals (CWRES) require  extra steps calculate. Either add CWRES NONMEM  table files compute using information proveded  compute.cwres. wide array extra options controlling  xyplots available. See xpose.plot.default  xpose.panel.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"","code":"cwres.wres.vs.pred(   object,   ylb = \"Residuals\",   abline = c(0, 0),   smooth = TRUE,   scales = list(),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"object xpose.data object. ylb string giving label y-axis. NULL none. abline Vector arguments panel.abline function. abline drawn NULL. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. scales scales passed  xpose.plot.default ... arguments passed xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"compound xyplot.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"Niclas Jonsson & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"","code":"cwres.wres.vs.pred(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"Change individual objective function value vs. covariate value.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"","code":"dOFV.vs.cov(   xpdb1,   xpdb2,   covariates = xvardef(\"covariates\", xpdb1),   ylb = expression(paste(Delta, OFV[i])),   main = \"Default\",   smooth = TRUE,   abline = c(0, 0),   ablcol = \"grey\",   abllwd = 2,   abllty = \"dashed\",   max.plots.per.page = 1,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"xpdb1 Xpose data object first NONMEM run xpdb2 Xpose data object second NONMEM run covariates Covariates plot ylb Label Y axis. main Title plot. smooth smooth? abline abline description. ablcol color abline abllwd line width abline abllty type abline max.plots.per.page Plots per page. ... additional arguments function","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"","code":"if (FALSE) { ## read in table files xpdb8 <- xpose.data(8) xpdb11 <- xpose.data(11)  ## Make some plots dOFV.vs.cov(xpdb8,xpdb11,\"AGE\") dOFV.vs.cov(xpdb8,xpdb11,c(\"AGE\",\"SECR\")) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":null,"dir":"Reference","previous_headings":"","what":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"plot showing least influential individuals determining drop OFV two models.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"","code":"dOFV.vs.id(   xpdb1,   xpdb2,   sig.drop = -3.84,   decrease.label.number = 3,   increase.label.number = 3,   id.lab.cex = 0.6,   id.lab.pos = 2,   type = \"o\",   xlb = \"Number of subjects removed\",   ylb = expression(paste(Delta, \"OFV\")),   main = \"Default\",   sig.line.col = \"red\",   sig.line.lty = \"dotted\",   tot.line.col = \"grey\",   tot.line.lty = \"dashed\",   key = list(columns = 1, lines = list(pch = c(super.sym$pch[1:2], NA, NA), type =     list(\"o\", \"o\", \"l\", \"l\"), col = c(super.sym$col[1:2], sig.line.col, tot.line.col),     lty = c(super.sym$lty[1:2], sig.line.lty, tot.line.lty)), text =     list(c(expression(paste(Delta, OFV[i] < 0)), expression(paste(Delta, OFV[i] > 0)),     expression(paste(\"Significant  \", Delta, OFV)), expression(paste(\"Total  \", Delta,     OFV)))), corner = c(0.95, 0.5), border = T),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"xpdb1 Xpose data object first NONMEM run (\"new\" run) xpdb2 Xpose data object Second NONMEM run (\"reference\" run) sig.drop significant drop OFV? decrease.label.number many points bw labeled ID values IDs drop iOFV? increase.label.number many points bw labeled ID values IDs increase iOFV? id.lab.cex Size ID labels. id.lab.pos ID label position. type Type lines. xlb X-axis label. ylb Y-axis label. main Title plot. sig.line.col Significant OFV drop line color. sig.line.lty Significant OFV drop line type. tot.line.col Total OFV drop line color. tot.line.lty Total OFV drop line type. key Legend plot. ... Additional arguments function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"","code":"if (FALSE) { library(xpose4)  ## first make sure that the iofv values are read into xpose cur.dir <- getwd() setwd(paste(cur.dir,\"/LAG_TIME\",sep=\"\")) xpdb1 <- xpose.data(1) setwd(paste(cur.dir,\"/TRANSIT_MODEL\",sep=\"\")) xpdb2 <- xpose.data(1) setwd(cur.dir)  ## then make the plot dOFV.vs.id(xpdb1,xpdb2) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":null,"dir":"Reference","previous_headings":"","what":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"Change individual objective function value 1 vs. individual objective","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"","code":"dOFV1.vs.dOFV2(   xpdb1,   xpdb2,   xpdb3,   ylb = expression(paste(Delta, OFV1[i])),   xlb = expression(paste(Delta, OFV2[i])),   main = \"Default\",   smooth = NULL,   abline = c(0, 1),   ablcol = \"grey\",   abllwd = 2,   abllty = \"dashed\",   lmline = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"xpdb1 Xpose data object first NONMEM run xpdb2 Xpose data object second NONMEM run xpdb3 Xpose data object third NONMEM run ylb Label Y axis. xlb Label X axis. main Title plot. smooth smooth? abline abline description. ablcol color abline abllwd line width abline abllty type abline lmline Linear regression line? ... Additional arguments function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"","code":"if (FALSE) { ## read in table files xpdb8 <- xpose.data(8) xpdb8 <- xpose.data(9) xpdb11 <- xpose.data(11)  ## Make the plot dOFV.vs.cov(xpdb8,xpdb9,xpdb11) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":null,"dir":"Reference","previous_headings":"","what":"Check through the source dataset to detect problems — data.checkout","title":"Check through the source dataset to detect problems — data.checkout","text":"function graphically \"checks \" dataset identify errors inconsistencies.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check through the source dataset to detect problems — data.checkout","text":"","code":"data.checkout(   obj = NULL,   datafile = \".ask.\",   hlin = -99,   dotcol = \"black\",   dotpch = 16,   dotcex = 1,   idlab = \"ID\",   csv = NULL,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check through the source dataset to detect problems — data.checkout","text":"obj NULL xpose.data object. datafile data file, suitable import read.table. hlin integer, specifying line number column headers appear. dotcol Colour dots dotplot. obj xpose data object default use value defined box--whisker plots. dotpch Plotting character dots dotplot. obj xpose data object default use value defined box--whisker plots. dotcex Relative scaling dots dotplot.  obj xpose data object default use value defined box--whisker plots. idlab ID column label dataset.  Input text string. csv data file CSV format (comma separated values)?  value NULL user asked command line.  supplied function value can TRUE/FALSE. main title plot. \"default\" means Xpose creates title. ... arguments passed link[lattice]{dotplot}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check through the source dataset to detect problems — data.checkout","text":"stack dotplots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check through the source dataset to detect problems — data.checkout","text":"function creates series dotplots, one variable dataset, individual ID. Outliers clusters may easily detected manner.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check through the source dataset to detect problems — data.checkout","text":"Niclas Jonsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check through the source dataset to detect problems — data.checkout","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run, table and data files for run ## 5 in the current working directory  xpdb5 <- xpose.data(5)  data.checkout(xpdb5, datafile = \"mydata.dta\") data.checkout(datafile = \"mydata.dta\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or assign data from an xpose.data object. — data_extract_or_assign","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"Extracts assigns data Data SData slots \"xpose.data\" object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"","code":"Data(object, inclZeroWRES = FALSE, onlyfirst = FALSE, subset = NULL)  Data(object, quiet = TRUE, keep.structure = F) <- value  SData(   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   subset = NULL,   samp = NULL )  SData(object) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"object \"xpose.data\" object inclZeroWRES Logical value indicating whether rows WRES==0 included extracted data. onlyfirst Logical value indicating whether first line per individual included extracted data. subset Expression extracted data subset (see xsubset) quiet TRUE FALSE FALSE information printed adding data Xpose object. keep.structure TRUE FALSE ifFALSE values converted continuous categorical according rules set xpose using object@Prefs@Cat.levels, object@Prefs@DV.cat.levels values \"catab\" file. value R data.frame. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"Returns data.frame Data SData slots, excluding rows indicated arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"using Data assign data.frame Data slot \"xpose.data\" object number things happen: column data.frame checked set factor number unique values less value Cat.levels (see xpose.prefs-class). checked predefined xpose data variables exists data.frame. variable definitions exist set NULL. column identified dv xpose variable definition, checked set factor number unique values less equal DV.Cat.levels (see xpose.prefs-class). Finally, column name data.frame checked label (see xpose.prefs-class). non-existent, label set column name. SData used assign data.frame SData slot first checked number rows SData data.frame even multiple number rown Data. Next, column SData data.frame assigned class corresponding column Data data.frame (required columns Data SData). Finally, extra column, \"iter\", added SData, indicates iteration number row belongs . time, Nsim slot \"xpose.data\" object set number iterations (see nsim).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"Data(): Extract data Data(object, quiet = TRUE, keep.structure = F) <- value: assign data SData(): extract simulated data SData(object) <- value: assign simulated data","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"","code":"xpdb <- simpraz.xpdb  ## Extract data my.dataframe <- Data(xpdb)  ## Assign data Data(xpdb) <- my.dataframe  ## Extract simulated data my.simulated.dataframe <- SData(xpdb)  ## Assign simulated data SData(xpdb) <- my.simulated.dataframe"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints the contents of an Xpose data object — db.names","title":"Prints the contents of an Xpose data object — db.names","text":"functions print summary specified Xpose object R console.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints the contents of an Xpose data object — db.names","text":"","code":"db.names(object)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prints the contents of an Xpose data object — db.names","text":"object xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prints the contents of an Xpose data object — db.names","text":"detailed summary contents specified xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prints the contents of an Xpose data object — db.names","text":"functions return detailed summary contents specified xpose.data object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prints the contents of an Xpose data object — db.names","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prints the contents of an Xpose data object — db.names","text":"","code":"db.names(simpraz.xpdb) #>  #> The current run number is 1. #>  #> The database contains the following observed items: #> ID TIME IPRED IWRES CWRES CL V KA ETA1 ETA2 ETA3 AGE HT WT  #> SECR SEX RACE SMOK HCTZ PROP CON OCC DV PRED RES WRES #>  #> The following variables are defined: #>  #> ID variable: ID  #> Label variable: ID  #> Independent variable: TIME  #> Occasion variable: OCC  #> Dependent variable: DV  #> Population prediction variable: PRED  #> Individual prediction variable: IPRED  #> Weighted population residual variable: WRES  #> Weighted individual residual variable: IWRES  #> Population residual variable: RES  #> Parameters: ETA3 ETA2 ETA1 KA V CL #> Covariates: SEX RACE SMOK HCTZ PROP CON OCC AGE HT WT SECR #>   ( Continuous: AGE HT WT SECR ) #>   ( Categorical: SEX RACE SMOK HCTZ PROP CON OCC ) #> Variability parameters: ETA1 ETA2 ETA3 #> Missing value label: -99  #> NULL"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"plot observations (DV) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"","code":"dv.vs.idv(object, smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"object xpose.data object. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"Returns xyplot DV vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"wide array extra options controlling xyplot available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  dv.vs.idv(xpdb)   ## A conditioning plot dv.vs.idv(xpdb, by=\"HCTZ\")   ## Logarithmic Y-axis dv.vs.idv(xpdb, logy=TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"plot dependent variable (DV) vs individual predictions (IPRED) conditioned covariates, specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"","code":"dv.vs.ipred.by.cov(   object,   covs = \"Default\",   abline = c(0, 1),   smooth = TRUE,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"object xpose.data object. covs vector covariates use plot. \"Default\"  covariates defined object@Prefs@Xvardef$Covariates used. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"Returns stack xyplots DV vs IPRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling xyplot available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"","code":"dv.vs.ipred.by.cov(simpraz.xpdb, covs=c(\"HCTZ\",\"WT\"), max.plots.per.page=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"plot dependent variable (DV) vs individual predictions (IPRED) conditioned independent variable, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"","code":"dv.vs.ipred.by.idv(object, abline = c(0, 1), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"Returns stack xyplots DV vs IPRED, conditioned independent variable.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"wide array extra options controlling xyplot available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"","code":"dv.vs.ipred.by.idv(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"plot observations (DV) vs individual predictions (IPRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"","code":"dv.vs.ipred(object, abline = c(0, 1), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"Returns xyplot DV vs IPRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"wide array extra options controlling xyplot available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  dv.vs.ipred(xpdb)   ## A conditioning plot dv.vs.ipred(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"plot dependent variable (DV) vs population predictions (PRED) conditioned covariates, specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"","code":"dv.vs.pred.by.cov(   object,   covs = \"Default\",   abline = c(0, 1),   smooth = TRUE,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"object xpose.data object. covs vector covariates use plot. \"Default\"  covariates defined object@Prefs@Xvardef$Covariates used. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"Returns stack xyplots DV vs PRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"","code":"dv.vs.pred.by.cov(simpraz.xpdb, covs=c(\"HCTZ\",\"WT\"), max.plots.per.page=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"plot dependent variable (DV) vs population predictions (PRED) conditioned independent variable, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"","code":"dv.vs.pred.by.idv(object, abline = c(0, 1), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"Returns stack xyplots DV vs PRED, conditioned independent variable.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"","code":"dv.vs.pred.by.idv(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"plot observations (DV) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"","code":"dv.vs.pred(object, abline = c(0, 1), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"Returns xyplot DV vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot dv.vs.pred(xpdb)   ## A conditioning plot dv.vs.pred(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"compound plot consisting plots observations (DV) individual predictions (IPRED) population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"","code":"dv.vs.pred.ipred(   object,   xlb = \"Predictions\",   layout = c(2, 1),   abline = c(0, 1),   lmline = TRUE,   smooth = NULL,   scales = list(),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"object xpose.data object. xlb string giving label x-axis. NULL none. layout list giving layout graphs plot, columns rows. abline Vector arguments panel.abline function. abline drawn NULL. lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) smooth NULL TRUE value indicating whether x-y smooth superimposed. scales list used scales argument xyplot. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"Returns compound plot comprising plots observations (DV) individual predictions (IPRED) population predictions (PRED).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"Plots DV vs PRED IPRED presented side side comparison. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"","code":"dv.vs.pred.ipred(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":null,"dir":"Reference","previous_headings":"","what":"Exports Xpose graphics settings to a file. — export.graph.par","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"function exports graphics settings specified Xpose data object file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"","code":"export.graph.par(object)  xpose.write(object, file = \"xpose.ini\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"object xpose.data object. file file contain exported Xpose settings.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"Null.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"function exports graphics settings (contents object@Prefs@Graph.prefs) given xpose.data object file, typically 'xpose.ini'. wrapper xpose.write. Note file format used import.variable.definitions export.variable.definitions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"xpose.write(): export graphics settings specified Xpose data object file.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## For a filename prompt export.graph.par(xpdb5)  ## Command-line driven xpose.write(xpdb5, \"c:/XposeSettings/mytheme.ini\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"function exports variable definitions specified Xpose data object file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"","code":"export.variable.definitions(object, file = \"\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"object xpose.data object. file file name string.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"Null.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"function exports variable definitions (contents object@Prefs@Xvardef) given xpose.data object file, typically 'xpose.vardefs.ini'.  Note file format used graphics settings. wrapper R function dput.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"","code":"od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-b4e0a141bd7a6d87d4e27f8e112db7d2\" #> [2] \"downlit\"                                 export.variable.definitions(simpraz.xpdb,file=\"xpose.vardefs.ini\") (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"xpose.vardefs.ini\"  file.remove(new.files) # remove this file #> [1] TRUE setwd(od)  # restore working directory"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/find.right.table.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal functions for the VPC — find.right.table","title":"Internal functions for the VPC — find.right.table","text":"Internal functions VPC","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/find.right.table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal functions for the VPC — find.right.table","text":"","code":"find.right.table(   object,   inclZeroWRES,   onlyfirst,   samp,   PI.subset,   subscripts,   PI.bin.table,   panel.number,   ... )  setup.PPI(PIlimits, PI.mirror, tmp.table, ...)  get.polygon.regions(PPI, PI.mirror, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/find.right.table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal functions for the VPC — find.right.table","text":"object Xpose object inclZeroWRES Include row sof data WRES=0 onlyfirst Use first data individual samp sample number PI.subset Prediction interval subset subscripts subscripts PI.bin.table prediction interval binning table panel.number panel number ... Extra options passed arguments PIlimits Prediction interval limits PI.mirror Prediction interval mirror tmp.table temporary table PPI Plot prediction intervals","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/find.right.table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal functions for the VPC — find.right.table","text":"Returned xpose.VPC","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":null,"dir":"Reference","previous_headings":"","what":"Structured goodness of fit diagnostics. — gof","title":"Structured goodness of fit diagnostics. — gof","text":"template function creating structured goodness fit diagnostics using functions Xpose specific library.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Structured goodness of fit diagnostics. — gof","text":"","code":"gof(   runno = NULL,   save = FALSE,   onefile = FALSE,   saveType = \"pdf\",   pageWidth = 7.6,   pageHeight = 4.9,   structural = TRUE,   residual = TRUE,   covariate = FALSE,   iiv = FALSE,   iov = FALSE,   all = FALSE,   myTrace = xpPage )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Structured goodness of fit diagnostics. — gof","text":"runno run number fo Xpose identify appropriate files read. addition runno used construct file name save plots . runno can also NULL cases function used non-Xpose based code. save Logical. TRUE plot(s) saved file. FALSE plot(s) displayed screen. plot(s) saved file named function name followed word 'run', run number, order number followed file name extension appropriate selected saveType. example 'gofrun1-01.pdf' first plot file created script called gof based output run 1 saveType='pdf'. onefile Logical. TRUE plots save single file FALSE plot saved separate file. latter case, file incremented order number (01-99). saveType type graphics file produce save=TRUE. Allowed values 'pdf' (default), 'wmf' (Windows) 'png'. pageWidth width graphics device inches. pageHeight height graphics device inches. structural Logical. TRUE code structural model section (see ) executed FALSE . residual Logical. TRUE code residual model section (see ) executed FALSE . covariate Logical. TRUE code covariate model section (see ) executed FALSE . iiv Logical. TRUE code IIV model section (see ) executed FALSE . iov Logical. TRUE code IOV model section (see ) executed FALSE . Logical. TRUE code sections (see ) executed. myTrace NULL name function. value myTrace can used lattice page= argument annotate plots traceability.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Structured goodness of fit diagnostics. — gof","text":"return anything unless user specify return value.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Structured goodness of fit diagnostics. — gof","text":"gof function provided template facilitate (structured) use functions Xpose specific library. Xpose specific extensively described 'Xpose Bestiary'. function can renamed multiple scripts can used parallel. function set make easy display plots screen well save files. latter case, plots save sub-directory called 'Plots'. arguments structural, residual, covariate, iiv, iov just \"switches\" different parts code (-blocks). blocks can removed default values arguments changed better suit needs user. also possible add tracing information produced plots. done via myTrace argument. non-NULL value function returns panel.text object. default xpPage function put string concatenated device name, function name, working directory date, small, faint grey, font bottom graph page. Note user need add page=myTrace argument Xpose functions effect. function calls support function called gofSetup, responsible setting graphics device determining file names saved graphs.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Structured goodness of fit diagnostics. — gof","text":"E. Niclas Jonsson, Mats Karlsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Structured goodness of fit diagnostics. — gof","text":"","code":"if (FALSE) { ## This is an example of how the function may be setup by a user.  library(xpose4) mygof <- gof fix(mygof)  myggof <- function (runno = NULL, save = FALSE, onefile = FALSE, saveType = \"pdf\",           pageWidth = 7.6, pageHeight = 4.9, structural = TRUE, residual = TRUE,           covariate = FALSE, iiv = FALSE, iov = FALSE, all = FALSE, myTrace=xpPage) {           gofSetup(runno, save, onefile, saveType, pageWidth, pageHeight)          xpdb <- xpose.data(runno)           if (structural || all) {           xplot <- dv.vs.pred.ipred(xpdb, page = myPage)           print(xplot)          }          if (residual || all) {           xplot <- absval.wres.vs.pred(xpdb, page = myPage)           print(xplot)          }          if (covariate || all) {          }          if (iiv || all) {          }          if (iov || all) {          }          if (save) dev.off()     invisible()   }  ## The function can then be execute, e.g.: mygof(1)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":null,"dir":"Reference","previous_headings":"","what":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"function imports graphics settings specified Xpose data object file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"","code":"import.graph.par(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"xpose.data object (classic = FALSE) null (classic = TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"function imports graphics settings (contents object@Prefs@Graph.prefs) given xpose.data object file, typically 'xpose.ini'. wrapper xpose.read. returns xpose.data object.  Note file format used import.variable.definitions export.variable.definitions.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Import graphics preferences you saved earlier using export.graph.par xpdb5 <- import.graph.par(xpdb5)  ## Command-line driven xpdb5 <- xpose.read(xpdb5, \"c:/XposeSettings/mytheme.ini\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"function imports variable definitions specified Xpose data object file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"","code":"import.variable.definitions(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"xpose.data object (classic == FALSE) null (classic == TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"function imports variable definitions (contents object@Prefs@Xvardef) given xpose.data object file, typically 'xpose.vardefs.ini'.  returns xpose.data object. Note file format used graphics settings. wrapper R function dget.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  xpdb5 <- import.variable.definitions(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"compound plot consisting plots observations (DV), individual predictions (IPRED) population predictions (PRED) independent variable every individual dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"","code":"ind.plots(   object,   y.vals = c(xvardef(\"dv\", new.obj), xvardef(\"ipred\", new.obj), xvardef(\"pred\", new.obj)),   x.vals = xvardef(\"idv\", new.obj),   id.vals = xvardef(\"id\", new.obj),   key.text = y.vals,   main = \"Default\",   key = \"Default\",   xlb = xlabel(xvardef(\"idv\", object), object),   ylb = NULL,   layout = c(4, 4),   inclZeroWRES = FALSE,   subset = xsubset(object),   type = \"o\",   grid = FALSE,   col = c(1, 2, 4),   lty = c(0, 1, 3),   lwd = c(1, 1, 1),   pch = c(21, 32, 32),   cex = c(0.7, 0.7, 0.7),   fill = c(\"lightgrey\", 0, 0),   prompt = FALSE,   mirror = NULL,   main.cex = 0.9,   max.plots.per.page = 1,   pch.ip.sp = c(21, 19, 18),   cex.ip.sp = c(0.7, 0.4, 0.4),   y.vals.subset = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"object xpose.data object. y.vals Y values use. x.vals X values use. id.vals ID values use. key.text text legend use. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. key Create legend. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. layout list giving layout graphs plot, columns rows. default 4x4. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default TRUE. subset string giving subset expression applied data plotting. See xsubset. type 1-character string giving type plot desired. default \"o\", -plotted points lines. See xpose.plot.default. grid plots grid plot? col list three elements, giving plotting characters observations, individual predictions, population predictions, order. default black DV, red individual predictions, blue population predictions. lty list three elements, giving line types observations, individual predictions, population predictions, order. lwd list three elements, giving line widths observations, individual predictions, population predictions, order. pch list three elements, giving plotting characters observations, individual predictions, population predictions, order. cex list three elements, giving relative point size observations, individual predictions, population predictions, order. default c(0.7,0.7,0.7). fill Fill circles points? prompt Specifies whether user prompted press RETURN plot pages. Default TRUE. mirror Mirror plots yet implemented function argument must contain value NULL main.cex size title. max.plots.per.page Maximum number plots per page. pch.ip.sp panel just one observation specifies type points DV, IPRED PRED respectively. cex.ip.sp panel just one observation specifies size points DV, IPRED PRED respectively. y.vals.subset Used subset DV, IPRED PRED variables separately.  Either NULL vector three strings, corresponding subset DV, IPRED PRED respectively. See examples . ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"Returns stack plots observations (DV) individual predictions (IPRED) population predictions (PRED). wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"Matrices individual plots presented comparison closer inspection.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## Monochrome, suitable for manuscript or report ind.plots(xpdb,    subset=\"ID>40 & ID<57\",    col=c(1,1,1),    lty=c(0,2,3),    strip=function(..., bg)      strip.default(..., bg=\"grey\"))       if (FALSE) {     ## IF we simulate in NONMEM using a dense grid of time points ## and all non-observed DV items are equal to zero. ind.plots(xpdb,inclZeroWRES=TRUE,y.vals.subset=c(\"DV!=0\",\"NULL\",\"NULL\"))   # to plot individual plots of multiple variables ind.plots(xpdb,subset=\"FLAG==1\") ind.plots(xpdb,subset=\"FLAG==2\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"compound plot consisting histograms distribution weighted residuals (weighted residual available NONMEM) every individual dataset.  wrapper encapsulating arguments xpose.plot.histogram function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"","code":"ind.plots.cwres.hist(object, wres = \"cwres\", ...)  ind.plots.wres.hist(   object,   main = \"Default\",   wres = \"wres\",   ylb = NULL,   layout = c(4, 4),   inclZeroWRES = FALSE,   subset = xsubset(object),   scales = list(cex = 0.7, tck = 0.5),   aspect = \"fill\",   force.by.factor = TRUE,   ids = F,   as.table = TRUE,   hicol = object@Prefs@Graph.prefs$hicol,   hilty = object@Prefs@Graph.prefs$hilty,   hilwd = object@Prefs@Graph.prefs$hilwd,   hidcol = object@Prefs@Graph.prefs$hidcol,   hidlty = object@Prefs@Graph.prefs$hidlty,   hidlwd = object@Prefs@Graph.prefs$hidlwd,   hiborder = object@Prefs@Graph.prefs$hiborder,   prompt = FALSE,   mirror = NULL,   main.cex = 0.9,   max.plots.per.page = 1,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"object xpose.data object. wres weighted residual plot? Defaults WRES. ... arguments passed xpose.plot.histogram. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ylb string giving label y-axis. NULL none. layout list giving layout graphs plot, columns rows. default 4x4. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default FALSE. subset string giving subset expression applied data plotting. See xsubset. scales see xpose.plot.histogram aspect see xpose.plot.histogram force..factor see xpose.plot.histogram ids see xpose.plot.histogram .table see xpose.plot.histogram hicol fill colour histogram - integer string.  default blue (see histogram). hilty border line type histogram - integer.  default 1 (see histogram). hilwd border line width histogram - integer.  default 1 (see histogram). hidcol fill colour density line - integer string. default black (see histogram). hidlty border line type density line - integer.  default 1 (see histogram). hidlwd border line width density line - integer.  default 1 (see histogram). hiborder border colour histogram - integer string. default black (see histogram). prompt Specifies whether user prompted press RETURN plot pages. Default FALSE. mirror Mirror plots yet implemented function argument must contain value NULL main.cex size title. max.plots.per.page Maximum number plots per page","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"Returns compound plot comprising histograms weighted residual conditioned individual.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"Matrices histograms weighted residuals included individual displayed. ind.plots.cwres.hist just wrapper ind.plots.wres.hist(object,wres=\"cwres\").","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"ind.plots.cwres.hist(): Histograms conditional  weighted residuals individual","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A plot of the first 16 individuals ind.plots.cwres.hist(xpdb, subset=\"ID<18\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"compound plot consisting QQ plots distribution weighted residuals (weighted residual produced NONMEM) every individual dataset.  function wrapper encapsulating arguments xpose.plot.qq function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"","code":"ind.plots.cwres.qq(object, wres = \"cwres\", ...)  ind.plots.wres.qq(   object,   main = \"Default\",   wres = \"wres\",   layout = c(4, 4),   inclZeroWRES = FALSE,   subset = xsubset(object),   scales = list(cex = 0.7, tck = 0.5),   aspect = \"fill\",   force.by.factor = TRUE,   ids = F,   as.table = TRUE,   type = \"o\",   pch = object@Prefs@Graph.prefs$pch,   col = object@Prefs@Graph.prefs$col,   cex = object@Prefs@Graph.prefs$cex,   abllty = object@Prefs@Graph.prefs$abllty,   abllwd = object@Prefs@Graph.prefs$abllwd,   ablcol = object@Prefs@Graph.prefs$ablcol,   prompt = FALSE,   main.cex = 0.9,   mirror = NULL,   max.plots.per.page = 1,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"object xpose.data object. wres weighted residual plot? Defaults WRES. ... arguments passed link{xpose.plot.qq}. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. layout list giving layout graphs plot, columns rows. default 4x4. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default FALSE. subset string giving subset expression applied data plotting. See xsubset. scales See xpose.plot.qq. aspect See xpose.plot.qq. force..factor See xpose.plot.qq. ids See xpose.plot.qq. .table See xpose.plot.qq. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. pch plotting character, symbol, use. Specified integer. See R help points. default open circle. col color lines points. Specified integer text string. full list obtained R command colours(). default blue (col=4). cex amount plotting text symbols scaled relative default. 'NULL' 'NA' equivalent '1.0'. abllty Line type line identity. abllwd Line width line identity. ablcol Line colour line identity. prompt Specifies whether user prompted press RETURN plot pages. Default FALSE. main.cex size title. mirror Mirror plots yet implemented function argument must contain value NULL max.plots.per.page Maximum number plots per page","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"Returns compound plot comprising QQ plots weighted residuals conditioned individual.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"Matrices Q-Q plots weighted residuals included individual displayed. wide array extra options controlling Q-Q plots available. See xpose.plot.qq details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"ind.plots.cwres.qq(): Q-Q plots conditional  weighted residuals individual","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"","code":"ind.plots.cwres.qq(simpraz.xpdb,subset=\"ID<18\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"plot Individual predictions (IPRED) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"","code":"ipred.vs.idv(object, smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"object xpose.data object. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"Returns xyplot IPRED vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ipred.vs.idv(xpdb)   ## A conditioning plot ipred.vs.idv(xpdb, by=\"HCTZ\")   ## Logarithmic Y-axis ipred.vs.idv(xpdb, logy=TRUE)   ## Custom colours and symbols, IDs ipred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"histogram distribution individual weighted residuals (IWRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.histogram function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"","code":"iwres.dist.hist(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"object xpose.data object. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"Returns histogram individual weighted residuals (IWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"Displays histogram individual weighted residuals (IWRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"","code":"iwres.dist.hist(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"QQ plot distribution individual weighted residuals (IWRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.qq function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"","code":"iwres.dist.qq(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"object xpose.data object. ... arguments passed link{xpose.plot.qq}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"Returns QQ plot individual weighted residuals (IWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"Displays QQ plot individual weighted residuals (IWRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"","code":"iwres.dist.qq(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"plot individual weighted residuals (IWRES) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"","code":"iwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. , default c(0,0), specifying horizontal line y=0. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"Returns xyplot IWRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  iwres.vs.idv(xpdb)   ## A conditioning plot iwres.vs.idv(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"Kaplan-Meier plots (repeated) time--event data.  Includes VPCs.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"","code":"kaplan.plot(   x = \"TIME\",   y = \"DV\",   id = \"ID\",   data = NULL,   evid = \"EVID\",   by = NULL,   xlab = \"Time\",   ylab = \"Default\",   object = NULL,   events.to.plot = \"All\",   sim.data = NULL,   sim.zip.file = NULL,   VPC = FALSE,   nsim.lab = \"simNumber\",   sim.evct.lab = \"counter\",   probs = c(0.025, 0.975),   add.baseline = T,   add.last.area = T,   subset = NULL,   main = \"Default\",   main.sub = \"Default\",   main.sub.cex = 0.8,   nbins = NULL,   real.type = \"l\",   real.lty = 1,   real.lwd = 1,   real.col = \"blue\",   real.se = if (!is.null(sim.data)) F else T,   real.se.type = \"l\",   real.se.lty = 2,   real.se.lwd = 0.5,   real.se.col = \"red\",   cens.type = \"l\",   cens.lty = 1,   cens.col = \"black\",   cens.lwd = 1,   cens.rll = 0.02,   inclZeroWRES = TRUE,   onlyfirst = FALSE,   samp = NULL,   poly.alpha = 1,   poly.fill = \"lightgreen\",   poly.line.col = \"darkgreen\",   poly.lty = 2,   censor.lines = TRUE,   ylim = c(-5, 105),   cov = NULL,   cov.fun = \"mean\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"x independent variable. y dependent variable. event (>0) event (0). id ID variable dataset. data dataset can used instead data Xpose object. Must form xpose data object xpdb@Data. evid EVID data item.  present rows considered events (can censored event). Otherwise, EVID!=0 dropped data set. vector conditioning variables. xlab X-axis label ylab Y-axis label object Xpose object. Needed data supplied. events..plot Vector events plotted.  \"\" means events plotted. sim.data simulated data file.  table file one header row , least, columns headers corresponding x, y, id, (used), nsim.lab sim.evct.lab. sim.zip.file sim.data can \\.zip format xpose unzip file reading data.  Must structure described sim.data. VPC TRUE FALSE.  TRUE Xpose search zipped file name paste(\"simtab\",object@Runno,\".zip\",sep=\"\"), example \"simtab42.zip\". nsim.lab column header sim.data contains simulation number row data. sim.evct.lab column header sim.data contains individual event counter information.  individual event counter increase one event (censored event) occurs. probs probabilities (non-parametric percentiles) use computation prediction intervals simulated data. add.baseline (x=0,y=1) baseline measurement added individual dataset. Otherwise plot begin first event dataset. add.last.area area added VPC extending last PI? subset subset data sim.data use. main title plot. Can also NULL \"Default\". main.sub title subplots. Must list, length number subplots (actual graphs), NULL \"Default\". main.sub.cex size title subplots. nbins number bins use VPC.  NULL, number unique x values sim.data used. real.type Type real data. real.lty Line type (lty) curve original (real) data. real.lwd Line width (lwd) real data. real.col Color curve original (real) data. real.se standard errors real (non simulated) data plotted?  Calculated using survfit. real.se.type Type standard errors. real.se.lty Line type (lty) standard error lines. real.se.lwd Line width (lwd) standard error lines. real.se.col Color standard error lines. cens.type Type censored lines. cens.lty Line type (lty) censored lines. cens.col Color censored lines. cens.lwd Line width censored lines. cens.rll relative line length censored line compared limits y-axis. inclZeroWRES Include WRES=0 rows real data set plots? onlyfirst Include first measurement real data plots? samp Simulated data xpose data object can used \"real\" data.  samp number selecting simulated data set use. poly.alpha transparency VPC shaded region. poly.fill fill color VPC shaded region. poly.line.col line colors VPC region. poly.lty line type VPC region. censor.lines censored observations marked plot? ylim Limits y-axes cov covariate dataset plot instead survival curve. cov.fun summary function covariate dataset plot instead survival curve. ... Additional arguments passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"returns object class \"xpose.multiple.plot\".","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"","code":"if (FALSE) { library(xpose4)  ## Read in the data runno <- \"57\" xpdb <- xpose.data(runno)  #################################### # here are the real data plots ####################################  kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,             events.to.plot=c(1,2),             by=c(\"DOSE==0\",\"DOSE!=0\")) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,             events.to.plot=c(1,2),             by=c(\"DOSE==0\",\"DOSE==10\",             \"DOSE==50\",\"DOSE==200\"))  ## make a PDF of the plots pdf(file=paste(\"run\",runno,\"_kaplan.pdf\",sep=\"\")) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,             by=c(\"DOSE==0\",\"DOSE==10\",             \"DOSE==50\",\"DOSE==200\")) dev.off()  #################################### ## VPC plots ####################################  kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,VPC=T,events.to.plot=c(1)) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,VPC=T,             events.to.plot=c(1,2,3),             by=c(\"DOSE==0\",\"DOSE!=0\")) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,VPC=T,             events.to.plot=c(1),             by=c(\"DOSE==0\",\"DOSE==10\",\"DOSE==50\",\"DOSE==200\"))  ## make a PDF of all plots pdf(file=paste(\"run\",runno,\"_kaplan.pdf\",sep=\"\")) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,VPC=T,             by=c(\"DOSE==0\",\"DOSE==10\",\"DOSE==50\",\"DOSE==200\")) dev.off() }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/make.sb.data.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stacked bar data set. — make.sb.data","title":"Make stacked bar data set. — make.sb.data","text":"Function make stacked bar data set categorical data plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/make.sb.data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stacked bar data set. — make.sb.data","text":"","code":"make.sb.data(data, idv, dv, nbins = 6, by = NULL, by.nbins = 6, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/make.sb.data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make stacked bar data set. — make.sb.data","text":"data Data set transform. idv independent variable. dv dependent variable. nbins number bins. Conditioning variable. .nbins .nbins. ... additional arguments.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/make.sb.data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Make stacked bar data set. — make.sb.data","text":"Xpose team.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"function takes output npc command Perl Speaks NONMEM (PsN) makes coverage plot.  coverage plot NPC looks different prediction intervals (PIs) data point calculates total number data points data set lying outside PIs. plot shows relative amount data points outside PI compared expected amount PI.  addition confidence interval around values computed based simulated data.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"","code":"npc.coverage(   npc.info = \"npc_results.csv\",   main = \"Default\",   main.sub = NULL,   main.sub.cex = 0.85,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"npc.info results file npc command PsN. example, npc_results.csv, file separate directory ./npc_dir1/npc_results.csv. main string giving plot title NULL none. \"Default\" creates default title. main.sub Used names plot using multiple plots. vector c(\"Group 1\",\"Group 2\") main.sub.cex size main.sub titles. ... arguments passed xpose.multiple.plot.default, xyplot others. Please see functions () descriptions can .","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"list plots","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"additional-arguments-for-the-npc-coverage-plots","dir":"Reference","previous_headings":"","what":"Additional arguments for the NPC coverage plots","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"Additional plot features CI Specifies whether confidence intervals (lines, shaded area ) added plot. Allowed values : \"area\",  \"lines\", \"\", NULL. mark.outside.data points outside CI marked different color identify . Allowed values TRUE FALSE. abline line mark value y=1? Possible values TRUE, FALSE NULL. Line area control. See plot, grid.polygon xyplot details. CI.area.col Color area CI. Defaults \"blue\" CI.area.alpha Transparency CI.area.col. Defaults 0.3. ab.lwd width abline. Default 1. ab.lty Line type abline. Default \"dashed\" CI.upper.lty Line type line upper edge CI. CI.upper.col Color line upper edge CI. CI.upper.lwd line width line upper edge CI. CI.lower.lty line type lower edge CI. CI.lower.col color line lower edge CI. CI.lower.lwd line width line lower edge CI. obs.col color observed values. obs.pch type point use observed values. obs.lty type line use observed values. obs.type combination lines points use observed values. Default \"b\" . obs.cex size points use observed values. obs.lwd line width use observed values. .col color observed values lie outside CI. used mark.outside.data=TRUE. .pch type point use observed values lie outside CI. used mark.outside.data = TRUE. .cex size points observed values lie outside CI. used mark.outside.data = TRUE. .lwd line width observed values lie outside CI. used mark.outside.data = TRUE.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"","code":"if (FALSE) { library(xpose4)  npc.coverage()  ## to read files in a directory different than the current working directory  npc.file <- \"./another_directory/npc_results.csv\" npc.coverage(npc.info=npc.file) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or set the value of the Nsim slot. — nsim","title":"Extract or set the value of the Nsim slot. — nsim","text":"Extract set value Nsim slot \"xpose.data\" object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or set the value of the Nsim slot. — nsim","text":"","code":"nsim(object)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or set the value of the Nsim slot. — nsim","text":"object \"xpose.data\" object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or set the value of the Nsim slot. — nsim","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or set the value of the Nsim slot. — nsim","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Report number of simulations nsim(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"functions plot parameter covariate values stored Xpose data object using histograms.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"","code":"cov.hist(object, onlyfirst = TRUE, main = \"Default\", ...)  parm.hist(object, onlyfirst = TRUE, main = \"Default\", ...)  ranpar.hist(object, onlyfirst = TRUE, main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"object xpose.data object. onlyfirst Logical value indicating first row per individual included plot. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"Delivers stack histograms.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"parameters covariates Xpose data object, specified object@Prefs@Xvardef$parms, object@Prefs@Xvardef$covariates object@Prefs@Xvardef$ranpar evaluated turn, creating stack histograms. wide array extra options controlling histograms available. See xpose.plot.histogram details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"cov.hist(): Covariate distributions parm.hist(): parameter distributions ranpar.hist(): random parameter distributions","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## Parameter histograms parm.hist(xpdb)    ## Covariate distribution, in green cov.hist(xpdb, hicol=11, hidcol=\"DarkGreen\", hiborder=\"White\")     ## Random parameter histograms ranpar.hist(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"functions plot parameter covariate values stored Xpose data object using Q-Q plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"","code":"cov.qq(object, onlyfirst = TRUE, main = \"Default\", ...)  parm.qq(object, onlyfirst = TRUE, main = \"Default\", ...)  ranpar.qq(object, onlyfirst = TRUE, main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"object xpose.data object. onlyfirst Logical value indicating first row per individual included plot. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.qq.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"Delivers stack Q-Q plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"parameters covariates Xpose data object, specified object@Prefs@Xvardef$parms, object@Prefs@Xvardef$ranpar object@Prefs@Xvardef$covariates, evaluated turn, creating stack Q-Q plots. wide array extra options controlling Q-Q plots available. See xpose.plot.qq details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"cov.qq(): Covariate distributions parm.qq(): parameter distributions ranpar.qq(): random parameter distributions","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## parameter histograms parm.qq(xpdb)    ## A stack of random parameter histograms ranpar.qq(xpdb)   ## Covariate distribution, in green with red line of identity cov.qq(xpdb, col=11, ablcol=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"functions plot scatterplot matrices parameters, random parameters covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"","code":"cov.splom(   object,   main = xpose.multiple.plot.title(object = object, plot.text =     \"Scatterplot matrix of covariates\", ...),   varnames = NULL,   onlyfirst = TRUE,   smooth = TRUE,   lmline = NULL,   ... )  parm.splom(   object,   main = xpose.multiple.plot.title(object = object, plot.text =     \"Scatterplot matrix of parameters\", ...),   varnames = NULL,   onlyfirst = TRUE,   smooth = TRUE,   lmline = NULL,   ... )  ranpar.splom(   object,   main = xpose.multiple.plot.title(object = object, plot.text =     \"Scatterplot matrix of random parameters\", ...),   varnames = NULL,   onlyfirst = TRUE,   smooth = TRUE,   lmline = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"object xpose.data object. main string giving plot title NULL none. varnames vector strings containing labels variables scatterplot matrix. onlyfirst Logical value indicating first row per individual included plot. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"Delivers scatterplot matrix.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"parameters covariates Xpose data object, specified object@Prefs@Xvardef$parms, object@Prefs@Xvardef$ranpar object@Prefs@Xvardef$covariates, plotted together scatterplot matrices. wide array extra options controlling scatterplot matrices available. See xpose.plot.splom details. control appearance labels names scatterplot matrix plots can try varname.cex=0.5 axis.text.cex=0.5 (changes tick labels variable names half large normal).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"cov.splom(): scatterplot matrix covariates parm.splom(): scatterplot matrix parameters ranpar.splom(): scatterplot matrix random parameters","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A scatterplot matrix of parameters, grouped by sex parm.splom(xpdb, groups=\"SEX\")   ## A scatterplot matrix of ETAs, grouped by sex ranpar.splom(xpdb, groups=\"SEX\")   ## Covariate scatterplots, with text customization cov.splom(xpdb, varname.cex=0.4, axis.text.cex=0.4, smooth=NULL, cex=0.4) #> SEX is categorical and will not be #>  shown in the scatterplot #> RACE is categorical and will not be #>  shown in the scatterplot #> SMOK is categorical and will not be #>  shown in the scatterplot #> HCTZ is categorical and will not be #>  shown in the scatterplot #> PROP is categorical and will not be #>  shown in the scatterplot #> CON is categorical and will not be #>  shown in the scatterplot #> OCC is categorical and will not be #>  shown in the scatterplot"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize individual parameter values and covariates — par_cov_summary","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"functions produce tables, printed screen, summarizing individual parameter values covariates dataset Xpose 4.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"","code":"cov.summary(   object,   onlyfirst = TRUE,   subset = xsubset(object),   inclZeroWRES = FALSE,   out.file = \".screen\",   main = \"Default\",   fill = \"gray\",   values.to.use = xvardef(\"covariates\", object),   value.name = \"Covariate\",   ... )  parm.summary(   object,   onlyfirst = TRUE,   subset = xsubset(object),   inclZeroWRES = FALSE,   out.file = \".screen\",   main = \"Default\",   fill = \"gray\",   values.to.use = xvardef(\"parms\", object),   value.name = \"Parameter\",   max.plots.per.page = 1,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"object xpose.data object. onlyfirst Logical value indicating first row per individual included plot. subset string giving subset expression applied data plotting. See xsubset. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default FALSE. .file results output .  Can \".screen\", \".ask\", \".graph\" filename quotes. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. fill color fill boxes table table printed \".graph\" values..use values summarized value.name name values ... arguments passed Data SData. max.plots.per.page Maximum plots per page.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"Returned matrix values table. parm.summary cov.summary produce summaries parameters covariates, respectively. parm.summary produces less attractive output supports mirror functionality. parm.summary cov.summary utilize print.char.matrix print information screen.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"cov.summary(): Covariate summary parm.summary(): Parameter summary","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"","code":"parm.summary(simpraz.xpdb) #>  #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |    |           Mean|    SD|        Q1|     Median|       Q3|           Range| N| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |ETA3|-0.073881265625|0.6318|-0.3498325|   0.035202|0.3588275|  -1.9354-1.0009|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |ETA2|-0.007861181875| 0.349|   -0.2862|   0.021967|0.1990975| -0.78827-0.7406|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |ETA1|0.0075852046875|0.4507|-0.4011075|-0.00122495|  0.37004|-0.70969-0.91423|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |  KA|   1.5882046875|0.8672| 1.0169925|    1.49455| 2.065375|   0.20826-3.925|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |   V|   80.900546875| 28.77|    57.683|     78.503|  93.7155|   34.914-161.06|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |  CL|   19.810528125| 9.374|    11.884|    17.7265| 25.69625|   8.7284-44.279|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"creates stack plots Bayesian parameter estimates plotted covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"","code":"parm.vs.cov(   object,   onlyfirst = TRUE,   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"object xpose.data object. onlyfirst Logical value indicating whether first row per individual included plot. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. type plot type - defaults points . main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"Returns stack xyplots histograms parameters covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"parameters Xpose data object, specified object@Prefs@Xvardef$parms, plotted covariate present, specified object@Prefs@Xvardef$covariates, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb <- xpose.data(5)  ## A vanilla plot parm.vs.cov(xpdb)  ## Custom colours and symbols, IDs parm.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot parameters vs other parameters — parm.vs.parm","title":"Plot parameters vs other parameters — parm.vs.parm","text":"function plots parameter values stored Xpose data object versus series graphs.  mirror functionality available function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot parameters vs other parameters — parm.vs.parm","text":"","code":"parm.vs.parm(   object,   onlyfirst = TRUE,   abline = FALSE,   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot parameters vs other parameters — parm.vs.parm","text":"object xpose.data object. onlyfirst Logical value indicating whether first row per individual included plot. abline Allows line identity. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. type plot type - defaults points . main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot parameters vs other parameters — parm.vs.parm","text":"Returns stack xyplots histograms parameters parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot parameters vs other parameters — parm.vs.parm","text":"parameters Xpose data object, specified object@Prefs@Xvardef$parms, plotted rest, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot parameters vs other parameters — parm.vs.parm","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot parameters vs other parameters — parm.vs.parm","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb <- xpose.data(5)   parm.vs.parm(xpdb)   parm.vs.parm(xpdb,mirror=3) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"plot population predictions (PRED) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"","code":"pred.vs.idv(object, smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"object xpose.data object. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"Returns xyplot PRED vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  pred.vs.idv(xpdb)   ## A conditioning plot pred.vs.idv(xpdb, by=\"HCTZ\")   ## Logarithmic Y-axis pred.vs.idv(xpdb, logy=TRUE)   ## Custom colours and symbols, IDs pred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"Print Xpose multiple plot object, output function xpose.multiple.plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"","code":"# S3 method for xpose.multiple.plot print(x, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"x Output object function xpose.multiple.plot. ... Additional options passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"Print method plot class.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"Reads results randtest tool PsN creates histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"","code":"randtest.hist(   results.file = \"raw_results_run1.csv\",   df = 1,   p.val = 0.05,   main = \"Default\",   xlim = NULL,   PCTSlcol = \"black\",   vlcol = c(\"red\", \"orange\"),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"results.file location results file randtest tool PsN df degrees freedom full reduced model used randomization test. p.val p-value like use. main title plot. xlim limits x-axis PCTSlcol Color empirical line vlcol Colors original nominal line ... Additional arguments can passed xpose.plot.histogram, xpose.panel.histogram, histogram lattice-package functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"lattice object","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"PsN","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"","code":"if (FALSE) { randtest.hist(results.file=\"randtest_dir1/raw_results_run1.csv\",df=2) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"creates stack plots Bayesian random parameter estimates plotted covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"","code":"ranpar.vs.cov(   object,   onlyfirst = TRUE,   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"object xpose.data object. onlyfirst Logical value indicating whether first row per individual included plot. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. type plot type - defaults points . main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"Returns stack xyplots histograms random parameters covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"random parameters (ETAs) Xpose data object, specified object@Prefs@Xvardef$ranpar, plotted covariate present, specified object@Prefs@Xvardef$covariates, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb <- xpose.data(5)  ## A vanilla plot ranpar.vs.cov(xpdb)  ## Custom colours and symbols, IDs ranpar.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.TTE.sim.data.html","id":null,"dir":"Reference","previous_headings":"","what":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","title":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","text":"Read (repeated) time--event simulation data files.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.TTE.sim.data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","text":"","code":"read.TTE.sim.data(   sim.file,   subset = NULL,   headers = c(\"REP\", \"ID\", \"DV\", \"TIME\", \"FLAG2\", \"DOSE\"),   xpose.table.file = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.TTE.sim.data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","text":"sim.file Name simulated file. subset subset extract. headers headers file. xpose.table.file xpose table files. ... Extra arguments passed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.TTE.sim.data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":null,"dir":"Reference","previous_headings":"","what":"Reading NONMEM table files — read.nm.tables","title":"Reading NONMEM table files — read.nm.tables","text":"Reads one NONMEM table files, removes duplicated columns merges data data.frame.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reading NONMEM table files — read.nm.tables","text":"","code":"read.nm.tables(   table.files = NULL,   runno = NULL,   tab.suffix = \"\",   table.names = c(\"sdtab\", \"mutab\", \"patab\", \"catab\", \"cotab\", \"mytab\", \"extra\", \"xptab\"),   cwres.name = c(\"cwtab\"),   cwres.suffix = \"\",   quiet = FALSE,   new_methods = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reading NONMEM table files — read.nm.tables","text":"table.files Exact names table files read.  provided exact names created using arguments function. runno Run-number identify sets table files. tab.suffix Table file name suffix. table.names Vector template table file names read. cwres.name Vector CWRES table file names read. cwres.suffix CWRES table file name suffix. quiet Logical value indicate whether warnings quiet . new_methods faster methods reading tables used (uses readr package)? ... Additional arguments passed function","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reading NONMEM table files — read.nm.tables","text":"dataframe.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reading NONMEM table files — read.nm.tables","text":"Reads one table files, removes duplicate columns merges data. function also checks see table files length (required). header lines table files (example data simulated NSUB>1), removed. table file names read constructed file name templates table.names. runno tab.suffix appended file name template checking file readable. Xpose expects, default, find following NONMEM tables working directory able create Xpose data object (using run number 5 example): sdtab5: 'standard' parameters, including IWRE, IPRE, TIME, NONMEM default items (DV, PRED, RES WRES) added NOAPPEND present $TABLE record. $TABLE ID TIME IPRE IWRE NOPRINT ONEHEADER FILE=sdtab5 patab5: empirical Bayes estimates individual model parameter values, posthoc estimates. model parameters, CL, V2, ETA1, etc. $TABLE ID CL V2 KA K F1 ETA1 ETA2 ETA3 NOPRINT NOAPPEND ONEHEADER FILE=patab5 catab5: Categorical covariates, e.g. SEX, RACE. $TABLE ID SEX HIV GRP NOPRINT NOAPPEND ONEHEADER FILE=catab5 cotab5: Continuous covariates, e.g. WT, AGE. $TABLE ID WT AGE BSA HT GGT HB NOPRINT NOAPPEND ONEHEADER FILE=cotab5 mutab5, mytab5, extra5, xptab5: Additional variables kind. might useful covariates can accommodated covariates tables, example, variables added, e.g. CMAX, AUC.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Reading NONMEM table files — read.nm.tables","text":"Niclas Jonsson, Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reading NONMEM table files — read.nm.tables","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory, and that the table files have ## a suffix of '.dat', e.g. sdtab5.dat  my.dataframe <- read.nm.tables(5, tab.suffix = \".dat\")  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":null,"dir":"Reference","previous_headings":"","what":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"function reads results file running either PsN command vpc npc.  function parses file passes result plotting functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"","code":"read.npc.vpc.results(   vpc.results = NULL,   npc.results = NULL,   verbose = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"vpc.results name results file running PsN command vcp.  Often named vpc_results.csv.  file directory different working directory can define relative absolute path file , example, ./vpc_strat_WT_4_mirror_5/vpc_results.csv. npc.results name results file running PsN command npc.  Often named npc_results.csv.  relative absolute paths file allowed vpc.results. verbose Text messages passed screen . ... arguments passed functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"list values returned. model.file model file PsN ran either npc vpc dv.var dependent variable used calculations. idv.var independent variable used calculations. NULL npc.results used. num.tables number separate tables results file. .interval conditioning interval stratification variable, returned vpc.results used. result.tables results tables results file.  list.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"One vpc.results npc.results necessary.  none defined function nothing NULL returned function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":null,"dir":"Reference","previous_headings":"","what":"Read the vpctab file from PsN into Xpose — read.vpctab","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"function read vpctab file created PsN gathers information needed make vpc plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"","code":"read.vpctab(   vpctab = NULL,   object = NULL,   vpc.name = \"vpctab\",   vpc.suffix = \"\",   tab.suffix = \"\",   inclZeroWRES = FALSE,   verbose = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"vpctab vpctab file 'vpc' run PsN. object xpose data object. Created xpose.data. One object vpctab required.  present information vpctab -ride xpose data object object (.e. values vpctab replace matching values object@Data portion xpose data object). object present function look vpctab run number one associated object. vpc.name default name vpctab file. Used object supplied. vpc.suffix suffix vpctab file. Used object supplied. tab.suffix table suffix vpctab file. Used object supplied. Final order file paste(vpc.name,object@Runno,vpc.suffix,tab.suffix) inclZeroWRES zero valued weighted residuals object TRUE. verbose Text messages passed screen . ... arguments passed functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"Returned xpose data object vpctab information included.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":null,"dir":"Reference","previous_headings":"","what":"Read NONMEM output files into Xpose 4 — read_NM_output","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"functions read NONMEM output file ('*.lst' file) format input.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"","code":"calc.npar(object)  create.parameter.list(listfile)  read.lst(filename)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"object return value read.lst(filename) listfile NONMEM output file. filename NONMEM output file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"lists read values.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"calc.npar(): calculates number type parameters included NONMEM output file create.parameter.list(): Reads parameters, uncertainty termination messages included NONMEM output file read.lst(): parses information NONMEM output.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"Niclas Jonsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Read NONMEM table files produced from simulation. — read_nm_table","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"function reads NONMEM table files produced $SIM line  NONMEM model file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"","code":"read_nm_table(   nm_table,   only_obs = FALSE,   method = \"default\",   quiet = TRUE,   sim_num = FALSE,   sim_name = \"NSIM\" )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"nm_table NONMEM table file read. text string. only_obs non-observation lines data set removed?  Currently filtered using expected MDV column. TRUE  FALSE. method methods use reading tables,  Can \"readr_1\", \"readr_2\", readr_3\" \"slow\". quiet error message verbose ? sim_num simulation number added simulation tables? sim_name name one use name column simulation number?","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"Returns data frame simulated table added column    simulation number. data frame given class c(\"tbl_df\",    \"tbl\", \"data.frame\") easy use dplyr.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"Currently function expects $TABLE header  new simulation. means NOHEADER option  ONEHEADER option table file allowed.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":null,"dir":"Reference","previous_headings":"","what":"Resets Xpose variable definitions to factory settings — reset.graph.par","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"Function reset Xpose's graphics parameters definitions default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"","code":"reset.graph.par(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"xpose.data object (classic == FALSE) null (classic == TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"functions used reset Xpose's graphic settings definitions default values. Graphical settings read file 'xpose.ini' root 'xpose4' package.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Import graphics preferences you saved earlier using export.graph.par xpdb5 <- import.graph.par(xpdb5)  ## Reset to default values xpdb5 <- reset.graph.par(xpdb5)  ## Change WRES definition xpdb5 <- change.wres(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Print run summary in Xpose 4 — runsum","title":"Print run summary in Xpose 4 — runsum","text":"Function build Xpose run summaries.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print run summary in Xpose 4 — runsum","text":"","code":"runsum(   object,   dir = \"\",   modfile = paste(dir, \"run\", object@Runno, \".mod\", sep = \"\"),   listfile = paste(dir, \"run\", object@Runno, \".lst\", sep = \"\"),   main = NULL,   subset = xsubset(object),   show.plots = TRUE,   txt.cex = 0.7,   txt.font = 1,   show.ids = FALSE,   param.table = TRUE,   txt.columns = 2,   force.wres = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print run summary in Xpose 4 — runsum","text":"object xpose.data object. dir directory look model output file NONMEM run. modfile name NONMEM control stream associated current run. listfile name NONMEM output file associated current run. main string giving main heading. NULL none. subset string giving subset expression applied data plotting. See xsubset. show.plots Logical indicating GOF plots shown run summary. txt.cex Number indicating size txt run summary. txt.font Font text run summary. show.ids Logical indicating IDs plotted plots run summary. param.table Logical indicating parameter table shown run summary. txt.columns number text columns run summary. force.wres Plot WRES even residuals available. ... arguments passed various functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print run summary in Xpose 4 — runsum","text":"compound plot containing Xpose run summary created.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print run summary in Xpose 4 — runsum","text":"Niclas Jonsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print run summary in Xpose 4 — runsum","text":"","code":"od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-b4e0a141bd7a6d87d4e27f8e112db7d2\" #> [2] \"downlit\"                                #> [3] \"file1775638e0fef\"                        simprazExample(overwrite=TRUE) # write files (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"run1.ext\"    \"run1.lst\"    \"run1.mod\"    \"simpraz.dta\" \"xptab1\"       xpdb <- xpose.data(1) #>  #> Looking for NONMEM table files. #>     Reading ./xptab1  #> Table files read. #>  #> Looking for NONMEM simulation table files. #> No simulated table files read. #>  runsum(xpdb)    file.remove(new.files) # remove these files #> [1] TRUE TRUE TRUE TRUE TRUE setwd(od)  # restore working directory"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated prazosin Xpose database. — simpraz.xpdb","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"Xpose database NONMEM output model prazosin using simulated data (NONMEM 7.3).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"","code":"simpraz.xpdb"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"xpose.data object","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"database can used test functions Xpose 4.  database slightly different database created reading files created simprazExample using xpose.data.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"","code":"xpose.print(simpraz.xpdb) #> The database contains the following observed items: #> ID TIME IPRED IWRES CWRES CL V KA ETA1 ETA2 ETA3 AGE HT WT  #> SECR SEX RACE SMOK HCTZ PROP CON OCC DV PRED RES WRES #>  #> The following variables are defined: #>  #> ID variable: ID  #> Label variable: ID  #> Independent variable: TIME  #> Occasion variable: OCC  #> Dependent variable: DV  #> Population prediction variable: PRED  #> Individual prediction variable: IPRED  #> Weighted population residual variable: WRES  #> Weighted individual residual variable: IWRES  #> Population residual variable: RES  #> Parameters: ETA3 ETA2 ETA1 KA V CL #> Covariates: SEX RACE SMOK HCTZ PROP CON OCC AGE HT WT SECR #>   ( Continuous: AGE HT WT SECR ) #>   ( Categorical: SEX RACE SMOK HCTZ PROP CON OCC ) #> Variability parameters: ETA1 ETA2 ETA3 #> Missing value label: -99  Data(simpraz.xpdb) #>     ID TIME      IPRED       IWRES      CWRES      CL       V      KA #> 2    1    1 6.9193e+01  0.03681300 -0.0646440 13.5790  93.640 1.22490 #> 3    1    2 8.0181e+01 -0.09442400 -0.9411300 13.5790  93.640 1.22490 #> 4    1    3 7.5330e+01  0.16833000  1.1911000 13.5790  93.640 1.22490 #> 5    1    4 6.6916e+01 -0.20602000 -1.5154000 13.5790  93.640 1.22490 #> 6    1    5 5.8398e+01 -0.02685300 -0.0596340 13.5790  93.640 1.22490 #> 7    1    6 5.0666e+01  0.02513800  0.4099300 13.5790  93.640 1.22490 #> 8    1    7 4.3871e+01  0.20557000  1.8304000 13.5790  93.640 1.22490 #> 9    1    9 3.2841e+01 -0.17939000 -1.0219000 13.5790  93.640 1.22490 #> 10   1   11 2.4575e+01  0.06492000  0.8373000 13.5790  93.640 1.22490 #> 12   2    1 9.6073e+01  0.13195000  0.8119500  8.7284  92.501 3.01420 #> 13   2    2 9.2138e+01  0.04842400 -0.0866630  8.7284  92.501 3.01420 #> 14   2    3 8.4073e+01 -0.03655400 -0.5405500  8.7284  92.501 3.01420 #> 15   2    4 7.6514e+01  0.00726360  0.0058889  8.7284  92.501 3.01420 #> 16   2    5 6.9625e+01 -0.07260500 -0.4024500  8.7284  92.501 3.01420 #> 17   2    6 6.3356e+01 -0.20749000 -1.2486000  8.7284  92.501 3.01420 #> 18   2    7 5.7651e+01  0.12019000  1.3843000  8.7284  92.501 3.01420 #> 19   2    8 5.2460e+01 -0.03659500  0.3323700  8.7284  92.501 3.01420 #> 20   2   10 4.3438e+01 -0.04322700  0.4971900  8.7284  92.501 3.01420 #> 21   2   12 3.5967e+01  0.18052000  2.3422000  8.7284  92.501 3.01420 #> 23   3    1 8.7842e+00  0.06440900  0.3927500 11.0240  93.942 2.23050 #> 24   3    2 8.7557e+00 -0.01093200 -0.4033600 11.0240  93.942 2.23050 #> 25   3    3 7.8877e+00  0.03705600  0.0613170 11.0240  93.942 2.23050 #> 26   3    4 7.0253e+00 -0.11889000 -0.9633300 11.0240  93.942 2.23050 #> 27   3    5 6.2486e+00  0.13306000  1.0866000 11.0240  93.942 2.23050 #> 28   3    6 5.5568e+00 -0.15779000 -0.9918700 11.0240  93.942 2.23050 #> 29   3    7 4.9416e+00  0.07253700  0.8514900 11.0240  93.942 2.23050 #> 30   3    8 4.3944e+00 -0.04423900  0.0490370 11.0240  93.942 2.23050 #> 31   3   10 3.4751e+00  0.12801000  1.4633000 11.0240  93.942 2.23050 #> 32   3   12 2.7482e+00  0.00066343  0.5552500 11.0240  93.942 2.23050 #> 34   4    1 6.3799e+01 -0.01017500  0.4475200 19.6070  49.991 1.67760 #> 35   4    2 5.5019e+01  0.13433000  1.2202000 19.6070  49.991 1.67760 #> 36   4    3 3.9396e+01  0.04198600  0.2016600 19.6070  49.991 1.67760 #> 37   4    4 2.7031e+01 -0.10065000 -1.0776000 19.6070  49.991 1.67760 #> 38   4    5 1.8339e+01 -0.00920360 -0.4784300 19.6070  49.991 1.67760 #> 39   4    6 1.2404e+01  0.06662200  0.0711510 19.6070  49.991 1.67760 #> 40   4    7 8.3822e+00  0.01166300 -0.3334000 19.6070  49.991 1.67760 #> 41   4    9 3.8260e+00  0.02455700 -0.1799200 19.6070  49.991 1.67760 #> 42   4   11 1.7462e+00  0.00790240 -0.2506900 19.6070  49.991 1.67760 #> 44   5    1 1.9492e+01  0.10248000  0.9678200 12.4700  84.706 2.43710 #> 45   5    2 1.8528e+01 -0.19743000 -1.5134000 12.4700  84.706 2.43710 #> 46   5    3 1.6141e+01 -0.06013000 -0.4224300 12.4700  84.706 2.43710 #> 47   5    4 1.3944e+01 -0.01893700 -0.0316990 12.4700  84.706 2.43710 #> 48   5    5 1.2036e+01  0.23210000  1.9295000 12.4700  84.706 2.43710 #> 49   5    6 1.0389e+01 -0.05090000 -0.1700800 12.4700  84.706 2.43710 #> 50   5    7 8.9667e+00 -0.02416300  0.0563140 12.4700  84.706 2.43710 #> 51   5    9 6.6798e+00  0.16321000  1.4828000 12.4700  84.706 2.43710 #> 52   5   11 4.9761e+00 -0.12382000 -0.7134000 12.4700  84.706 2.43710 #> 54   6    1 7.1066e+01  0.04875000  0.2570900 14.3950  73.084 0.86832 #> 55   6    2 8.8184e+01 -0.28535000 -2.1306000 14.3950  73.084 0.86832 #> 56   6    3 8.4934e+01  0.18280000  1.5438000 14.3950  73.084 0.86832 #> 57   6    4 7.5002e+01  0.13224000  1.2209000 14.3950  73.084 0.86832 #> 58   6    5 6.3797e+01 -0.07503500 -0.3469000 14.3950  73.084 0.86832 #> 59   6    6 5.3316e+01  0.01038200  0.2649900 14.3950  73.084 0.86832 #> 60   6    7 4.4173e+01  0.02212200  0.3031300 14.3950  73.084 0.86832 #> 61   6    8 3.6439e+01 -0.07021700 -0.4513400 14.3950  73.084 0.86832 #> 62   6   10 2.4659e+01  0.06532200  0.4657100 14.3950  73.084 0.86832 #> 64   7    1 1.6349e+02  0.13020000  1.8578000 14.0930  46.925 2.98340 #> 65   7    2 1.2936e+02  0.10339000  0.9682000 14.0930  46.925 2.98340 #> 66   7    3 9.6217e+01 -0.10431000 -0.7247200 14.0930  46.925 2.98340 #> 67   7    4 7.1277e+01 -0.07894400 -0.5392700 14.0930  46.925 2.98340 #> 68   7    5 5.2787e+01  0.04324800  0.4085000 14.0930  46.925 2.98340 #> 69   7    6 3.9093e+01 -0.02923600 -0.1127600 14.0930  46.925 2.98340 #> 70   7    8 2.1441e+01  0.08438900  0.7694000 14.0930  46.925 2.98340 #> 72   8    1 3.4260e+01  0.10099000 -0.1437400 42.1500 105.580 3.54630 #> 73   8    2 2.3971e+01 -0.20112000 -2.5728000 42.1500 105.580 3.54630 #> 74   8    3 1.6110e+01 -0.10550000 -1.5138000 42.1500 105.580 3.54630 #> 75   8    4 1.0808e+01  0.10012000  0.3458000 42.1500 105.580 3.54630 #> 76   8    5 7.2505e+00 -0.24419000 -2.0503000 42.1500 105.580 3.54630 #> 77   8    6 4.8640e+00  0.01356100  0.0123830 42.1500 105.580 3.54630 #> 78   8    7 3.2630e+00  0.22891000  1.6775000 42.1500 105.580 3.54630 #> 79   8    9 1.4685e+00  0.04868300  0.2684800 42.1500 105.580 3.54630 #> 80   8   11 6.6089e-01 -0.09213400 -0.8524200 42.1500 105.580 3.54630 #> 82   9    1 5.5324e+01 -0.04960600 -0.0015166 21.3300  57.540 1.60740 #> 83   9    2 4.9276e+01  0.17117000  1.2600000 21.3300  57.540 1.60740 #> 84   9    3 3.6235e+01 -0.00013445 -0.3256500 21.3300  57.540 1.60740 #> 85   9    4 2.5457e+01  0.02644900 -0.2390900 21.3300  57.540 1.60740 #> 86   9    5 1.7661e+01  0.02316400 -0.2412900 21.3300  57.540 1.60740 #> 87   9    6 1.2208e+01 -0.11291000 -1.1700000 21.3300  57.540 1.60740 #> 88   9    8 5.8200e+00 -0.05669500 -0.5009300 21.3300  57.540 1.60740 #> 89   9   10 2.7731e+00  0.09986200  0.8232500 21.3300  57.540 1.60740 #> 91  10    1 5.6258e+01  0.09779200 -0.4076800 44.2790  34.914 3.55980 #> 92  10    2 1.7428e+01  0.05579700 -2.5276000 44.2790  34.914 3.55980 #> 93  10    3 4.9485e+00 -0.02596300 -1.8592000 44.2790  34.914 3.55980 #> 94  10    6 1.1030e-01 -0.27472000 -0.4042000 44.2790  34.914 3.55980 #> 95  10    8 8.7302e-03  0.14545000  2.4362000 44.2790  34.914 3.55980 #> 97  12    1 1.6198e+01  0.02666600  0.0415360 18.6950  92.607 1.92540 #> 98  12    2 1.5599e+01  0.05648300  0.1090400 18.6950  92.607 1.92540 #> 99  12    3 1.3092e+01 -0.15138000 -1.4208000 18.6950  92.607 1.92540 #> 100 12    4 1.0749e+01  0.11454000  0.6792500 18.6950  92.607 1.92540 #> 101 12    5 8.7913e+00 -0.12072000 -1.0203000 18.6950  92.607 1.92540 #> 102 12    6 7.1853e+00  0.12451000  0.8966700 18.6950  92.607 1.92540 #> 103 12    7 5.8720e+00 -0.02248100 -0.1728200 18.6950  92.607 1.92540 #> 104 12    9 3.9214e+00 -0.01312000 -0.0634180 18.6950  92.607 1.92540 #> 105 12   11 2.6188e+00  0.04245900  0.3511400 18.6950  92.607 1.92540 #> 107 13    1 1.3355e+01  0.04678600 -0.3388700 34.6730  78.320 1.12170 #> 108 13    2 1.2928e+01 -0.06558300 -1.0392000 34.6730  78.320 1.12170 #> 109 13    3 9.7203e+00 -0.05969700 -0.9090900 34.6730  78.320 1.12170 #> 110 13    4 6.7047e+00  0.07535900  0.1448000 34.6730  78.320 1.12170 #> 111 13    5 4.4567e+00  0.06805200  0.0926130 34.6730  78.320 1.12170 #> 112 13    6 2.9115e+00 -0.14133000 -1.5011000 34.6730  78.320 1.12170 #> 113 13    7 1.8860e+00 -0.05088300 -0.8349800 34.6730  78.320 1.12170 #> 114 13    8 1.2165e+00  0.08504900  0.1676700 34.6730  78.320 1.12170 #> 115 13   10 5.0351e-01  0.23136000  1.2465000 34.6730  78.320 1.12170 #> 116 13   12 2.0789e-01 -0.23037000 -2.1245000 34.6730  78.320 1.12170 #> 118 14    1 2.5038e+01  0.08035500  1.1038000 20.2700  57.701 2.48000 #> 119 14    2 1.9718e+01 -0.01916900 -0.1943000 20.2700  57.701 2.48000 #> 120 14    3 1.4053e+01 -0.01868900 -0.3506400 20.2700  57.701 2.48000 #> 121 14    4 9.9046e+00  0.01366900 -0.1361900 20.2700  57.701 2.48000 #> 122 14    5 6.9719e+00  0.07862100  0.3699900 20.2700  57.701 2.48000 #> 123 14    6 4.9067e+00 -0.22963000 -1.9322000 20.2700  57.701 2.48000 #> 124 14    7 3.4533e+00  0.00485050 -0.1257800 20.2700  57.701 2.48000 #> 125 14    8 2.4303e+00  0.27555000  1.9526000 20.2700  57.701 2.48000 #> 126 14   10 1.2037e+00 -0.16095000 -1.2921000 20.2700  57.701 2.48000 #> 127 14   12 5.9621e-01  0.00635350  0.0374170 20.2700  57.701 2.48000 #> 129 15    1 9.2441e+00 -0.03938900 -0.9602000 17.3920 118.440 0.90406 #> 130 15    2 1.1725e+01 -0.05584300 -1.1138000 17.3920 118.440 0.90406 #> 131 15    3 1.1639e+01  0.22775000  1.1714000 17.3920 118.440 0.90406 #> 132 15    4 1.0663e+01 -0.16442000 -1.6265000 17.3920 118.440 0.90406 #> 133 15    5 9.4555e+00  0.04912800  0.1525200 17.3920 118.440 0.90406 #> 134 15    6 8.2647e+00  0.06476500  0.4188800 17.3920 118.440 0.90406 #> 135 15    7 7.1767e+00 -0.05388800 -0.3485000 17.3920 118.440 0.90406 #> 136 15    8 6.2131e+00 -0.07292500 -0.3804600 17.3920 118.440 0.90406 #> 137 15   10 4.6404e+00 -0.13800000 -0.7021300 17.3920 118.440 0.90406 #> 138 15   12 3.4608e+00  0.16158000  1.6700000 17.3920 118.440 0.90406 #> 140 16    1 1.0618e+01 -0.07795800 -1.3419000 12.6540  70.520 0.53141 #> 141 16    2 1.5114e+01  0.05859200 -0.1268400 12.6540  70.520 0.53141 #> 142 16    3 1.6300e+01 -0.03250400 -0.5043700 12.6540  70.520 0.53141 #> 143 16    4 1.5778e+01  0.08439700  0.6561500 12.6540  70.520 0.53141 #> 144 16    5 1.4454e+01  0.14572000  1.3152000 12.6540  70.520 0.53141 #> 145 16    6 1.2824e+01 -0.10093000 -0.4396400 12.6540  70.520 0.53141 #> 146 16    7 1.1156e+01  0.10349000  1.1475000 12.6540  70.520 0.53141 #> 147 16    8 9.5804e+00 -0.06997600 -0.1771000 12.6540  70.520 0.53141 #> 148 16   10 6.9067e+00  0.02508600  0.4263100 12.6540  70.520 0.53141 #> 149 16   12 4.8984e+00  0.01257600  0.1745800 12.6540  70.520 0.53141 #> 151 17    1 1.5908e+02  0.10746000  1.7128000 10.2860  50.261 2.52330 #> 152 17    2 1.4240e+02 -0.02634200  0.3253200 10.2860  50.261 2.52330 #> 153 17    3 1.1707e+02 -0.03690100  0.1315800 10.2860  50.261 2.52330 #> 154 17    4 9.5486e+01 -0.02194900  0.1821900 10.2860  50.261 2.52330 #> 155 17    5 7.7821e+01  0.11513000  1.1642000 10.2860  50.261 2.52330 #> 156 17    6 6.3419e+01  0.05347000  0.6449400 10.2860  50.261 2.52330 #> 157 17    7 5.1682e+01 -0.09736600 -0.5463300 10.2860  50.261 2.52330 #> 158 17    8 4.2117e+01  0.07865600  0.7392000 10.2860  50.261 2.52330 #> 159 17   10 2.7970e+01  0.13906000  1.1194000 10.2860  50.261 2.52330 #> 160 17   12 1.8575e+01 -0.11604000 -0.8623000 10.2860  50.261 2.52330 #> 162 18    1 5.2404e+01  0.07530700  0.7263000 12.9160  76.863 2.32250 #> 163 18    2 4.9435e+01 -0.04561100 -0.4767900 12.9160  76.863 2.32250 #> 164 18    3 4.2292e+01  0.06191100  0.4013300 12.9160  76.863 2.32250 #> 165 18    4 3.5799e+01  0.17153000  1.3563000 12.9160  76.863 2.32250 #> 166 18    5 3.0267e+01 -0.04052900 -0.1307100 12.9160  76.863 2.32250 #> 167 18    6 2.5585e+01 -0.32149000 -2.1582000 12.9160  76.863 2.32250 #> 168 18    7 2.1628e+01  0.02414000  0.5351100 12.9160  76.863 2.32250 #> 169 18    8 1.8282e+01 -0.01271600  0.3156300 12.9160  76.863 2.32250 #> 170 18   10 1.3064e+01  0.04409100  0.8130900 12.9160  76.863 2.32250 #> 171 18   12 9.3351e+00  0.08837000  1.1591000 12.9160  76.863 2.32250 #> 173 19    1 5.2559e+01  0.07251400  0.7654100 10.6970  67.987 1.54970 #> 174 19    2 5.6065e+01 -0.16134000 -0.9023800 10.6970  67.987 1.54970 #> 175 19    3 5.0272e+01  0.16128000  1.5843000 10.6970  67.987 1.54970 #> 176 19    4 4.3456e+01 -0.15385000 -0.7835300 10.6970  67.987 1.54970 #> 177 19    5 3.7236e+01  0.04603700  0.7231100 10.6970  67.987 1.54970 #> 178 19    6 3.1837e+01 -0.08943100 -0.3239100 10.6970  67.987 1.54970 #> 179 19    7 2.7207e+01  0.20668000  1.8826000 10.6970  67.987 1.54970 #> 180 19    8 2.3247e+01  0.06595600  0.7757300 10.6970  67.987 1.54970 #> 181 19   10 1.6971e+01 -0.02655500 -0.0218010 10.6970  67.987 1.54970 #> 182 19   12 1.2389e+01 -0.02815600 -0.1358400 10.6970  67.987 1.54970 #> 184 20    1 9.9678e+00  0.09152000  0.2013600 19.2850  86.198 0.66435 #> 185 20    2 1.3099e+01 -0.09229900 -1.0564000 19.2850  86.198 0.66435 #> 186 20    3 1.3113e+01 -0.28162000 -2.2840000 19.2850  86.198 0.66435 #> 187 20    4 1.1842e+01  0.15432000  1.1607000 19.2850  86.198 0.66435 #> 188 20    5 1.0168e+01  0.07892700  0.6561800 19.2850  86.198 0.66435 #> 189 20    6 8.4890e+00  0.08493300  0.6992200 19.2850  86.198 0.66435 #> 190 20    7 6.9724e+00 -0.09213100 -0.6898500 19.2850  86.198 0.66435 #> 191 20    9 4.5823e+00  0.08024000  0.4516500 19.2850  86.198 0.66435 #> 192 20   11 2.9624e+00 -0.03794900 -0.6078200 19.2850  86.198 0.66435 #> 194 21    1 2.5587e+00 -0.12846000 -1.8527000 29.9420  96.831 0.34333 #> 195 21    3 3.9986e+00 -0.11719000 -1.9728000 29.9420  96.831 0.34333 #> 196 21    4 3.8485e+00  0.07314200 -0.3671200 29.9420  96.831 0.34333 #> 197 21    5 3.4729e+00  0.10570000  0.0636740 29.9420  96.831 0.34333 #> 198 21    6 3.0089e+00 -0.03619000 -0.8487000 29.9420  96.831 0.34333 #> 199 21    7 2.5347e+00  0.02970500 -0.2268900 29.9420  96.831 0.34333 #> 200 21    8 2.0919e+00  0.19510000  1.1100000 29.9420  96.831 0.34333 #> 201 21   10 1.3640e+00  0.04841300  0.0903160 29.9420  96.831 0.34333 #> 202 21   12 8.5410e-01 -0.22726000 -1.9483000 29.9420  96.831 0.34333 #> 204 22    1 2.1425e+01 -0.07772000 -1.1746000 34.2390  91.155 1.18500 #> 205 22    2 2.1267e+01  0.12806000  0.2941700 34.2390  91.155 1.18500 #> 206 22    3 1.6610e+01 -0.04515000 -1.0190000 34.2390  91.155 1.18500 #> 207 22    4 1.2021e+01 -0.00092257 -0.6403800 34.2390  91.155 1.18500 #> 208 22    5 8.4441e+00 -0.08811800 -1.2237000 34.2390  91.155 1.18500 #> 209 22    6 5.8572e+00  0.07389400  0.0907280 34.2390  91.155 1.18500 #> 210 22    7 4.0406e+00  0.01717400 -0.2559100 34.2390  91.155 1.18500 #> 212 23    1 1.4522e+01  0.06253400 -0.2408300 10.7110 151.960 1.07010 #> 213 23    2 1.8514e+01 -0.05045100 -1.2076000 10.7110 151.960 1.07010 #> 214 23    3 1.8962e+01 -0.05865200 -1.1808000 10.7110 151.960 1.07010 #> 215 23    4 1.8257e+01 -0.04367900 -0.9107800 10.7110 151.960 1.07010 #> 216 23    5 1.7216e+01 -0.15601000 -1.5863000 10.7110 151.960 1.07010 #> 217 23    6 1.6113e+01  0.08731700  0.4262700 10.7110 151.960 1.07010 #> 218 23    7 1.5040e+01  0.05917100  0.3738600 10.7110 151.960 1.07010 #> 219 23    8 1.4025e+01  0.12089000  0.9872800 10.7110 151.960 1.07010 #> 220 23   10 1.2184e+01  0.03577600  0.5918700 10.7110 151.960 1.07010 #> 221 23   12 1.0582e+01  0.01204800  0.6048500 10.7110 151.960 1.07010 #> 223 24    1 1.5286e+01  0.00418570 -0.6209700 11.0160  84.570 0.50433 #> 224 24    2 2.2651e+01 -0.26139000 -2.5830000 11.0160  84.570 0.50433 #> 225 24    3 2.5459e+01  0.16500000  0.8928200 11.0160  84.570 0.50433 #> 226 24    4 2.5717e+01  0.15061000  1.0648000 11.0160  84.570 0.50433 #> 227 24    5 2.4609e+01 -0.01094000  0.0918400 11.0160  84.570 0.50433 #> 228 24    6 2.2832e+01 -0.13234000 -0.6281900 11.0160  84.570 0.50433 #> 229 24    7 2.0785e+01  0.23215000  2.2760000 11.0160  84.570 0.50433 #> 230 24    8 1.8694e+01 -0.30995000 -1.7253000 11.0160  84.570 0.50433 #> 231 24   10 1.4808e+01 -0.02414600  0.5301000 11.0160  84.570 0.50433 #> 232 24   12 1.1558e+01  0.09450900  1.4236000 11.0160  84.570 0.50433 #> 234 25    1 2.7108e+01  0.04507300  0.0620610 25.5610  78.202 1.33080 #> 235 25    2 2.6713e+01 -0.10495000 -1.0418000 25.5610  78.202 1.33080 #> 236 25    3 2.1158e+01  0.06104900  0.1865600 25.5610  78.202 1.33080 #> 237 25    4 1.5759e+01 -0.01327900 -0.4268800 25.5610  78.202 1.33080 #> 238 25    5 1.1498e+01  0.07936300  0.2288400 25.5610  78.202 1.33080 #> 239 25    6 8.3268e+00  0.05323100  0.0056514 25.5610  78.202 1.33080 #> 240 25    7 6.0144e+00 -0.03065300 -0.6326900 25.5610  78.202 1.33080 #> 241 25    8 4.3399e+00 -0.06679800 -0.8923200 25.5610  78.202 1.33080 #> 242 25    9 3.1305e+00  0.07969600  0.2409600 25.5610  78.202 1.33080 #> 243 25   10 2.2578e+00  0.00095464 -0.3248500 25.5610  78.202 1.33080 #> 245 26    1 1.0933e+01 -0.19237000 -2.3438000 12.9680  85.946 0.34318 #> 246 26    2 1.7159e+01 -0.01860000 -1.1484000 12.9680  85.946 0.34318 #> 247 26    3 2.0260e+01  0.23743000  0.9561300 12.9680  85.946 0.34318 #> 248 26    4 2.1327e+01 -0.06645400 -1.0793000 12.9680  85.946 0.34318 #> 249 26    5 2.1111e+01 -0.02041200 -0.4553600 12.9680  85.946 0.34318 #> 250 26    6 2.0120e+01  0.09393600  0.6571500 12.9680  85.946 0.34318 #> 251 26    7 1.8697e+01  0.06649000  0.6510200 12.9680  85.946 0.34318 #> 252 26    8 1.7068e+01 -0.08307000 -0.3302900 12.9680  85.946 0.34318 #> 253 26   10 1.3724e+01  0.05365400  0.8603600 12.9680  85.946 0.34318 #> 254 26   12 1.0703e+01 -0.00499180  0.4230700 12.9680  85.946 0.34318 #> 256 27    1 4.3322e+01 -0.05960300 -0.6760900 19.9050  58.701 0.95710 #> 257 27    2 4.7499e+01  0.14360000  1.1242000 19.9050  58.701 0.95710 #> 258 27    3 4.0227e+01 -0.06929200 -0.4189000 19.9050  58.701 0.95710 #> 259 27    4 3.1112e+01 -0.04216300 -0.2965000 19.9050  58.701 0.95710 #> 260 27    5 2.3106e+01  0.14080000  0.9369300 19.9050  58.701 0.95710 #> 261 27    6 1.6823e+01  0.01289000 -0.1821700 19.9050  58.701 0.95710 #> 262 27    7 1.2124e+01  0.08380300  0.2425000 19.9050  58.701 0.95710 #> 263 27    8 8.6906e+00 -0.08867500 -1.1212000 19.9050  58.701 0.95710 #> 264 27   10 4.4333e+00 -0.05036200 -0.8184200 19.9050  58.701 0.95710 #> 265 27   12 2.2534e+00  0.05620500  0.0758110 19.9050  58.701 0.95710 #> 267 28    1 8.2497e+01  0.02051600 -0.0042412 17.2100  84.631 1.56870 #> 268 28    2 8.4503e+01  0.02008500 -0.0169360 17.2100  84.631 1.56870 #> 269 28    3 7.2533e+01  0.02063200  0.0211160 17.2100  84.631 1.56870 #> 270 28    4 5.9932e+01 -0.02890700 -0.3122100 17.2100  84.631 1.56870 #> 271 28    5 4.9060e+01 -0.04198300 -0.3787200 17.2100  84.631 1.56870 #> 272 28    6 4.0065e+01  0.01261400  0.0544760 17.2100  84.631 1.56870 #> 273 28    7 3.2699e+01 -0.03636500 -0.3066400 17.2100  84.631 1.56870 #> 274 28    8 2.6684e+01  0.13965000  1.0269000 17.2100  84.631 1.56870 #> 275 28   10 1.7767e+01  0.13523000  0.9863600 17.2100  84.631 1.56870 #> 276 28   12 1.1830e+01 -0.13865000 -1.0903000 17.2100  84.631 1.56870 #> 278 29    1 2.3035e+01 -0.04234200 -1.6466000 30.4780 127.080 0.40331 #> 279 29    3 3.6649e+01  0.10262000 -0.5420700 30.4780 127.080 0.40331 #> 280 29    4 3.5703e+01 -0.02782400 -1.2294000 30.4780 127.080 0.40331 #> 281 29    5 3.2680e+01 -0.20164000 -2.2283000 30.4780 127.080 0.40331 #> 282 29    6 2.8777e+01 -0.00268240 -0.4339300 30.4780 127.080 0.40331 #> 283 29    7 2.4689e+01  0.21794000  1.4808000 30.4780 127.080 0.40331 #> 284 29    8 2.0793e+01 -0.04007100 -0.2717400 30.4780 127.080 0.40331 #> 285 29   10 1.4201e+01 -0.29513000 -1.9231000 30.4780 127.080 0.40331 #> 286 29   12 9.3842e+00  0.13063000  1.4558000 30.4780 127.080 0.40331 #> 288 30    1 6.7041e+01 -0.13769000 -1.3960000 12.1630  85.934 0.98067 #> 289 30    2 8.3337e+01  0.24255000  1.6200000 12.1630  85.934 0.98067 #> 290 30    3 8.1768e+01 -0.07054600 -0.5672200 12.1630  85.934 0.98067 #> 291 30    4 7.4514e+01 -0.02044600 -0.0456080 12.1630  85.934 0.98067 #> 292 30    5 6.6006e+01 -0.14356000 -0.8761800 12.1630  85.934 0.98067 #> 293 30    6 5.7792e+01  0.03959500  0.5783300 12.1630  85.934 0.98067 #> 294 30    7 5.0351e+01  0.13285000  1.3292000 12.1630  85.934 0.98067 #> 295 30    8 4.3775e+01 -0.13445000 -0.6652600 12.1630  85.934 0.98067 #> 296 30   10 3.3016e+01  0.17520000  1.6969000 12.1630  85.934 0.98067 #> 297 30   12 2.4880e+01 -0.06713500 -0.1487900 12.1630  85.934 0.98067 #> 299 31    1 1.3951e+01 -0.04378200 -0.7529000 15.1090  83.503 1.04040 #> 300 31    2 1.6571e+01  0.08081500  0.3780700 15.1090  83.503 1.04040 #> 301 31    3 1.5570e+01  0.10664000  0.7438700 15.1090  83.503 1.04040 #> 302 31    4 1.3608e+01 -0.13727000 -0.9921300 15.1090  83.503 1.04040 #> 303 31    5 1.1573e+01 -0.10568000 -0.6911400 15.1090  83.503 1.04040 #> 304 31    6 9.7344e+00  0.10330000  0.9216500 15.1090  83.503 1.04040 #> 305 31    7 8.1504e+00  0.15332000  1.3131000 15.1090  83.503 1.04040 #> 306 31    8 6.8110e+00 -0.05740100 -0.2795700 15.1090  83.503 1.04040 #> 307 31   10 4.7470e+00 -0.10258000 -0.6375700 15.1090  83.503 1.04040 #> 308 31   12 3.3061e+00  0.07678800  0.6893800 15.1090  83.503 1.04040 #> 310 32    1 1.6890e+01  0.02543100 -0.6048700 22.2730 161.060 1.89500 #> 311 32    2 1.7248e+01  0.01692800 -0.8933800 22.2730 161.060 1.89500 #> 312 32    3 1.5402e+01 -0.17284000 -2.1920000 22.2730 161.060 1.89500 #> 313 32    4 1.3470e+01  0.09352900  0.0384960 22.2730 161.060 1.89500 #> 314 32    5 1.1739e+01  0.10912000  0.3648300 22.2730 161.060 1.89500 #> 315 32    6 1.0224e+01 -0.09528100 -0.9989300 22.2730 161.060 1.89500 #> 316 32    7 8.9039e+00 -0.11836000 -1.0185000 22.2730 161.060 1.89500 #> 317 32    8 7.7539e+00  0.08332400  0.6366200 22.2730 161.060 1.89500 #> 318 32   10 5.8803e+00 -0.12250000 -0.7301400 22.2730 161.060 1.89500 #> 319 32   12 4.4595e+00  0.13018000  1.2982000 22.2730 161.060 1.89500 #> 321 33    1 1.0497e+02  0.00922680  0.1798900  9.8142  71.098 1.68210 #> 322 33    2 1.1096e+02  0.08543500  0.6514100  9.8142  71.098 1.68210 #> 323 33    3 1.0028e+02  0.03824500  0.3469400  9.8142  71.098 1.68210 #> 324 33    4 8.8030e+01  0.03498900  0.4304400  9.8142  71.098 1.68210 #> 325 33    5 7.6805e+01  0.09966100  1.0321000  9.8142  71.098 1.68210 #> 326 33    6 6.6926e+01 -0.12456000 -0.5643600  9.8142  71.098 1.68210 #> 327 33    7 5.8301e+01 -0.07446500 -0.1016700  9.8142  71.098 1.68210 #> 328 33    8 5.0785e+01 -0.14837000 -0.5933100  9.8142  71.098 1.68210 #> 329 33   10 3.8534e+01  0.19454000  2.0928000  9.8142  71.098 1.68210 #> 330 33   12 2.9238e+01  0.01854600  0.8048600  9.8142  71.098 1.68210 #> 332 34    2 8.0560e+00  0.05263100  0.1739500 27.2370  67.039 1.45910 #> 333 34    3 5.8506e+00 -0.07873000 -0.7393500 27.2370  67.039 1.45910 #> 334 34    4 4.0098e+00 -0.22938000 -1.9093000 27.2370  67.039 1.45910 #> 335 34    5 2.6972e+00  0.28283000  1.8805000 27.2370  67.039 1.45910 #> 336 34    6 1.8027e+00 -0.00704600 -0.4093900 27.2370  67.039 1.45910 #> 337 34    7 1.2022e+00 -0.10166000 -1.2036000 27.2370  67.039 1.45910 #> 338 34    9 5.3374e-01  0.10541000  0.2932500 27.2370  67.039 1.45910 #> 339 34   10 3.5555e-01 -0.07185700 -1.0503000 27.2370  67.039 1.45910 #> 341 35    1 3.0587e+01 -0.08491100 -1.2655000 26.1020  65.982 0.69311 #> 342 35    2 3.5888e+01  0.19317000  1.0618000 26.1020  65.982 0.69311 #> 343 35    3 3.1810e+01 -0.19333000 -1.6674000 26.1020  65.982 0.69311 #> 344 35    4 2.5240e+01 -0.02260100 -0.3011200 26.1020  65.982 0.69311 #> 345 35    5 1.8906e+01 -0.00823790 -0.2389600 26.1020  65.982 0.69311 #> 346 35    6 1.3685e+01  0.03399500 -0.0551100 26.1020  65.982 0.69311 #> 347 35    7 9.6917e+00  0.18246000  0.8894200 26.1020  65.982 0.69311 #> 348 35   10 3.2064e+00 -0.10180000 -1.6228000 26.1020  65.982 0.69311 #> 350 36    1 1.3023e+02  0.03580100  0.7723900 23.1080  48.141 1.89000 #> 351 36    2 1.0026e+02  0.06935300  0.5669600 23.1080  48.141 1.89000 #> 352 36    3 6.5010e+01 -0.02706900 -0.5179300 23.1080  48.141 1.89000 #> 353 36    4 4.0676e+01 -0.04932000 -0.8703500 23.1080  48.141 1.89000 #> 354 36    5 2.5238e+01  0.04407300 -0.2350300 23.1080  48.141 1.89000 #> 355 36    6 1.5627e+01 -0.09643500 -1.3136000 23.1080  48.141 1.89000 #> 356 36    7 9.6713e+00  0.21390000  1.0438000 23.1080  48.141 1.89000 #> 357 36    8 5.9847e+00  0.01592100 -0.4273000 23.1080  48.141 1.89000 #> 358 36   10 2.2916e+00 -0.08359200 -1.0714000 23.1080  48.141 1.89000 #> 359 36   12 8.7743e-01  0.01432700 -0.1877100 23.1080  48.141 1.89000 #> 361 37    1 2.2057e+01  0.05728300 -0.2077900 15.9890  75.885 0.46891 #> 362 37    2 3.1667e+01 -0.18653000 -1.9498000 15.9890  75.885 0.46891 #> 363 37    3 3.4285e+01  0.07830500  0.3151700 15.9890  75.885 0.46891 #> 364 37    8 1.9366e+01  0.14685000  1.3637000 15.9890  75.885 0.46891 #> 365 37   12 9.1165e+00 -0.06872400 -0.6587500 15.9890  75.885 0.46891 #> 367 38    1 6.5378e+01  0.08476700  1.2398000 19.9830  54.656 2.46260 #> 368 38    2 5.0928e+01 -0.06241000 -0.5106100 19.9830  54.656 2.46260 #> 369 38    3 3.5807e+01  0.14138000  0.8040500 19.9830  54.656 2.46260 #> 370 38    4 2.4883e+01 -0.14639000 -1.4329000 19.9830  54.656 2.46260 #> 371 38    6 1.1979e+01  0.04765000  0.0828920 19.9830  54.656 2.46260 #> 373 39    1 6.3145e+00  0.00087708 -0.4685700 17.7880 110.330 1.46490 #> 374 39    2 6.8335e+00  0.06680700  0.0167570 17.7880 110.330 1.46490 #> 375 39    3 6.1532e+00 -0.09315500 -1.0872000 17.7880 110.330 1.46490 #> 376 39    4 5.3149e+00  0.07621300  0.3247200 17.7880 110.330 1.46490 #> 377 39    5 4.5416e+00 -0.16108000 -1.3518000 17.7880 110.330 1.46490 #> 378 39    7 3.2943e+00  0.05637000  0.4637400 17.7880 110.330 1.46490 #> 379 39    8 2.8040e+00  0.13052000  1.0776000 17.7880 110.330 1.46490 #> 380 39   10 2.0312e+00 -0.04982800 -0.2315300 17.7880 110.330 1.46490 #> 382 40    1 2.9012e+01  0.16710000  0.9159600 22.9910  98.438 3.50040 #> 383 40    2 2.3845e+01 -0.23003000 -2.3360000 22.9910  98.438 3.50040 #> 384 40    3 1.8905e+01  0.02989100 -0.1934200 22.9910  98.438 3.50040 #> 385 40    4 1.4968e+01  0.05892000  0.2017100 22.9910  98.438 3.50040 #> 386 40    5 1.1850e+01 -0.09876900 -0.8478100 22.9910  98.438 3.50040 #> 387 40    6 9.3822e+00 -0.16437000 -1.2339000 22.9910  98.438 3.50040 #> 388 40    7 7.4280e+00  0.05142800  0.4781700 22.9910  98.438 3.50040 #> 389 40    8 5.8808e+00 -0.00694570  0.0886480 22.9910  98.438 3.50040 #> 390 40   10 3.6862e+00  0.24790000  2.0591000 22.9910  98.438 3.50040 #> 391 40   12 2.3105e+00 -0.13873000 -0.8731000 22.9910  98.438 3.50040 #> 393 41    1 4.2425e+01  0.07012900  0.8266800  9.3156  55.911 1.38500 #> 394 41    2 4.6534e+01 -0.05295400  0.0891360  9.3156  55.911 1.38500 #> 395 41    3 4.2051e+01 -0.11346000 -0.3104600  9.3156  55.911 1.38500 #> 396 41    4 3.6263e+01  0.13918000  1.5993000  9.3156  55.911 1.38500 #> 397 41    5 3.0864e+01  0.21047000  2.1102000  9.3156  55.911 1.38500 #> 398 41    6 2.6169e+01 -0.07409700 -0.0830530  9.3156  55.911 1.38500 #> 399 41    7 2.2163e+01  0.01023100  0.5099300  9.3156  55.911 1.38500 #> 400 41    8 1.8764e+01 -0.17610000 -0.9463200  9.3156  55.911 1.38500 #> 401 41   10 1.3447e+01  0.12735000  1.2584000  9.3156  55.911 1.38500 #> 402 41   12 9.6366e+00 -0.00898410  0.1424100  9.3156  55.911 1.38500 #> 404 42    1 1.1185e+02 -0.00774830  0.0406750 37.2660  47.824 2.10400 #> 405 42    2 6.4951e+01  0.11376000 -0.3242400 37.2660  47.824 2.10400 #> 406 42    3 3.1460e+01 -0.01749700 -1.6388000 37.2660  47.824 2.10400 #> 407 42    4 1.4636e+01 -0.04342600 -1.5594000 37.2660  47.824 2.10400 #> 408 42    6 3.0945e+00  0.01146600 -0.3731500 37.2660  47.824 2.10400 #> 409 42    8 6.5147e-01  0.02843600  0.0301440 37.2660  47.824 2.10400 #> 411 43    1 4.8318e+00 -0.02934900 -0.9231600 19.0340 126.070 1.09440 #> 412 43    2 5.7721e+00  0.00482860 -0.7521000 19.0340 126.070 1.09440 #> 413 43    3 5.5047e+00  0.07181300 -0.1372700 19.0340 126.070 1.09440 #> 414 43    4 4.9146e+00  0.06418700 -0.0269800 19.0340 126.070 1.09440 #> 415 43    5 4.2865e+00  0.09178900  0.3631100 19.0340 126.070 1.09440 #> 416 43    6 3.7062e+00 -0.03944200 -0.4556700 19.0340 126.070 1.09440 #> 417 43    7 3.1936e+00 -0.22345000 -1.6905000 19.0340 126.070 1.09440 #> 418 43    8 2.7484e+00 -0.09037400 -0.5471300 19.0340 126.070 1.09440 #> 419 43   10 2.0330e+00  0.05263100  0.7423900 19.0340 126.070 1.09440 #> 420 43   12 1.5033e+00  0.11092000  1.3102000 19.0340 126.070 1.09440 #> 422 44    1 2.9118e+01 -0.00749780 -0.3942700 40.0860  57.104 1.23880 #> 423 44    2 2.2868e+01 -0.07817300 -1.2920000 40.0860  57.104 1.23880 #> 424 44    3 1.3778e+01  0.09526100 -0.2329800 40.0860  57.104 1.23880 #> 425 44    4 7.5363e+00  0.16370000  0.1880900 40.0860  57.104 1.23880 #> 426 44    5 3.9402e+00 -0.13456000 -1.9837000 40.0860  57.104 1.23880 #> 427 44    6 2.0122e+00 -0.08557600 -1.3889000 40.0860  57.104 1.23880 #> 428 44    7 1.0145e+00  0.07446100  0.0680140 40.0860  57.104 1.23880 #> 429 44    8 5.0775e-01 -0.05466100 -0.7355000 40.0860  57.104 1.23880 #> 430 44   10 1.2585e-01  0.11245000  0.5970300 40.0860  57.104 1.23880 #> 431 44   12 3.1006e-02 -0.03243100 -0.5368500 40.0860  57.104 1.23880 #> 433 45    1 5.1880e+01  0.02159500  0.2926200 27.7580  59.022 1.71030 #> 434 45    4 1.7683e+01  0.01680300 -0.9072300 27.7580  59.022 1.71030 #> 435 45    8 2.7139e+00  0.01331400 -0.6966400 27.7580  59.022 1.71030 #> 436 45   11 6.6200e-01  0.01207800 -0.3429100 27.7580  59.022 1.71030 #> 438 46    1 1.5517e+02  0.04311500  1.0758000 11.6740  44.455 1.67430 #> 439 46    2 1.4842e+02  0.06167000  1.2032000 11.6740  44.455 1.67430 #> 440 46    3 1.1959e+02 -0.02258700  0.3841300 11.6740  44.455 1.67430 #> 441 46    4 9.2993e+01  0.09878900  1.1093000 11.6740  44.455 1.67430 #> 442 46    5 7.1708e+01 -0.02814000 -0.0160700 11.6740  44.455 1.67430 #> 443 46    6 5.5183e+01 -0.12491000 -0.8766500 11.6740  44.455 1.67430 #> 444 46    7 4.2445e+01  0.12145000  0.8934700 11.6740  44.455 1.67430 #> 445 46    8 3.2644e+01  0.19411000  1.3792000 11.6740  44.455 1.67430 #> 446 46    9 2.5105e+01 -0.15474000 -1.2994000 11.6740  44.455 1.67430 #> 447 46   11 1.4848e+01 -0.01938900 -0.2983900 11.6740  44.455 1.67430 #> 449 47    1 7.0883e+01  0.04002800 -0.2106100 11.2530 113.650 1.96010 #> 450 47    4 6.2328e+01  0.00709510 -0.3372900 11.2530 113.650 1.96010 #> 451 47    5 5.6480e+01 -0.07436300 -0.7245400 11.2530 113.650 1.96010 #> 452 47    8 4.1969e+01 -0.00331600  0.3640700 11.2530 113.650 1.96010 #> 453 47   12 2.8244e+01  0.09191500  1.5126000 11.2530 113.650 1.96010 #> 455 48    1 9.1965e+01  0.30452000  3.3693000 10.2800  44.757 3.92500 #> 456 48    2 7.4907e+01 -0.26162000 -1.4146000 10.2800  44.757 3.92500 #> 457 48    3 5.9571e+01 -0.12675000 -0.4617800 10.2800  44.757 3.92500 #> 458 48    4 4.7346e+01  0.04358900  0.7934400 10.2800  44.757 3.92500 #> 459 48    5 3.7630e+01  0.07786900  1.0297000 10.2800  44.757 3.92500 #> 460 48    6 2.9907e+01  0.13450000  1.4412000 10.2800  44.757 3.92500 #> 461 48    7 2.3770e+01 -0.02312900  0.2345100 10.2800  44.757 3.92500 #> 462 48    8 1.8892e+01 -0.19436000 -1.0742000 10.2800  44.757 3.92500 #> 463 48    9 1.5015e+01 -0.18414000 -1.0108000 10.2800  44.757 3.92500 #> 464 48   10 1.1933e+01  0.14301000  1.4484000 10.2800  44.757 3.92500 #> 465 48   11 9.4844e+00 -0.18603000 -1.0598000 10.2800  44.757 3.92500 #> 466 48   12 7.5380e+00  0.21783000  1.9742000 10.2800  44.757 3.92500 #> 468 49    1 1.9858e+01 -0.03866100 -1.1673000 18.4130 150.760 1.02910 #> 469 49    2 2.4671e+01  0.07253100 -0.3768900 18.4130 150.760 1.02910 #> 470 49    3 2.4370e+01  0.00410960 -0.7504300 18.4130 150.760 1.02910 #> 471 49    4 2.2474e+01 -0.09095300 -1.2758000 18.4130 150.760 1.02910 #> 472 49    5 2.0214e+01  0.19126000  1.0555000 18.4130 150.760 1.02910 #> 473 49    6 1.8006e+01 -0.24524000 -2.0661000 18.4130 150.760 1.02910 #> 474 49    7 1.5977e+01  0.05402700  0.3575500 18.4130 150.760 1.02910 #> 475 49    8 1.4155e+01 -0.08510900 -0.5588300 18.4130 150.760 1.02910 #> 476 49   10 1.1094e+01 -0.03457500  0.0292160 18.4130 150.760 1.02910 #> 477 49   12 8.6901e+00  0.12196000  1.3404000 18.4130 150.760 1.02910 #> 479 50    1 6.4334e+01  0.02154400  0.4775500 31.1660  39.413 1.70950 #> 480 50    2 4.0817e+01  0.03216600 -0.2933900 31.1660  39.413 1.70950 #> 481 50    3 2.0617e+01 -0.00956410 -1.2297000 31.1660  39.413 1.70950 #> 482 50    4 9.7311e+00  0.18589000 -0.0508730 31.1660  39.413 1.70950 #> 483 50    5 4.4821e+00 -0.02053800 -1.5527000 31.1660  39.413 1.70950 #> 484 50    6 2.0451e+00 -0.05628000 -1.4799000 31.1660  39.413 1.70950 #> 485 50    7 9.2971e-01  0.07560000 -0.0285050 31.1660  39.413 1.70950 #> 486 50    8 4.2203e-01 -0.14699000 -1.2748000 31.1660  39.413 1.70950 #> 487 50   10 8.6844e-02 -0.07880900 -0.1962200 31.1660  39.413 1.70950 #> 488 50   12 1.7862e-02  0.11968000  1.2797000 31.1660  39.413 1.70950 #> 490 51    1 4.1926e+01  0.01631500 -0.0527550 10.5210  80.379 1.30620 #> 491 51    2 4.8138e+01 -0.00951580 -0.2385700 10.5210  80.379 1.30620 #> 492 51    3 4.5308e+01  0.05169300  0.3256300 10.5210  80.379 1.30620 #> 493 51    4 4.0582e+01  0.10787000  0.8809500 10.5210  80.379 1.30620 #> 494 51    5 3.5829e+01  0.02040400  0.3517000 10.5210  80.379 1.30620 #> 495 51    6 3.1494e+01  0.02780700  0.5304000 10.5210  80.379 1.30620 #> 496 51    8 2.4259e+01 -0.18506000 -0.8791500 10.5210  80.379 1.30620 #> 497 51   10 1.8673e+01 -0.08692700  0.0045329 10.5210  80.379 1.30620 #> 498 51   12 1.4372e+01  0.16751000  2.0196000 10.5210  80.379 1.30620 #> 500 52    1 2.9898e+01 -0.06982700 -0.9819300 11.7460 101.520 1.04760 #> 501 52    2 3.7118e+01  0.20049000  1.1901000 11.7460 101.520 1.04760 #> 502 52    3 3.6741e+01 -0.24580000 -2.0019000 11.7460 101.520 1.04760 #> 503 52    4 3.4017e+01  0.04918300  0.3934300 11.7460 101.520 1.04760 #> 504 52    5 3.0753e+01  0.06136400  0.6158800 11.7460 101.520 1.04760 #> 505 52    6 2.7552e+01 -0.27155000 -1.8036000 11.7460 101.520 1.04760 #> 506 52    7 2.4597e+01  0.07777400  0.9120700 11.7460 101.520 1.04760 #> 507 52    8 2.1929e+01  0.12681000  1.3332000 11.7460 101.520 1.04760 #> 508 52   10 1.7407e+01  0.03691100  0.6973700 11.7460 101.520 1.04760 #> 509 52   12 1.3812e+01  0.00054528  0.4086800 11.7460 101.520 1.04760 #> 511 53    1 1.3954e+01  0.02907300  0.7636400 14.3100  46.439 1.52420 #> 512 53    2 1.3293e+01  0.10737000  1.4841000 14.3100  46.439 1.52420 #> 513 53    3 1.0429e+01 -0.13514000 -0.5282400 14.3100  46.439 1.52420 #> 514 53    4 7.8078e+00 -0.02660900  0.0348030 14.3100  46.439 1.52420 #> 515 53    5 5.7685e+00  0.11813000  0.8853300 14.3100  46.439 1.52420 #> 516 53    6 4.2456e+00  0.14472000  0.8928400 14.3100  46.439 1.52420 #> 517 53    7 3.1211e+00  0.03487700 -0.0721810 14.3100  46.439 1.52420 #> 518 53    8 2.2937e+00 -0.08010700 -1.0210000 14.3100  46.439 1.52420 #> 519 53   10 1.2385e+00 -0.06341000 -0.9310700 14.3100  46.439 1.52420 #> 520 53   12 6.6873e-01  0.04676100 -0.0510060 14.3100  46.439 1.52420 #> 522 54    1 1.6696e+01 -0.10158000 -1.5380000 41.2260  90.404 0.48267 #> 523 54    2 2.0886e+01 -0.01319800 -1.0871000 41.2260  90.404 0.48267 #> 524 54    3 1.9596e+01 -0.06103600 -1.4117000 41.2260  90.404 0.48267 #> 525 54    4 1.6344e+01 -0.02105800 -1.0055000 41.2260  90.404 0.48267 #> 526 54    5 1.2781e+01  0.19712000  0.7455100 41.2260  90.404 0.48267 #> 527 54    6 9.5949e+00 -0.01302200 -0.7668500 41.2260  90.404 0.48267 #> 528 54    7 7.0036e+00  0.09372200  0.0975670 41.2260  90.404 0.48267 #> 529 54    8 5.0081e+00 -0.12741000 -1.5299000 41.2260  90.404 0.48267 #> 530 54   10 2.4512e+00  0.18310000  0.9160200 41.2260  90.404 0.48267 #> 531 54   12 1.1520e+00 -0.21007000 -1.9179000 41.2260  90.404 0.48267 #> 533 55    1 9.8934e+01 -0.00630550 -0.0512920 31.5160  57.635 1.58060 #> 534 55    2 7.7626e+01  0.03289900 -0.1628500 31.5160  57.635 1.58060 #> 535 55    3 4.9121e+01  0.10056000  0.0809450 31.5160  57.635 1.58060 #> 536 55    4 2.9293e+01 -0.07725500 -1.3093000 31.5160  57.635 1.58060 #> 537 55    5 1.7132e+01 -0.16705000 -1.8756000 31.5160  57.635 1.58060 #> 538 55    6 9.9522e+00  0.26203000  1.5477000 31.5160  57.635 1.58060 #> 539 55    7 5.7677e+00 -0.23886000 -2.0685000 31.5160  57.635 1.58060 #> 540 55    9 1.9333e+00  0.06035500  0.4120400 31.5160  57.635 1.58060 #> 541 55   11 6.4769e-01  0.00356880  0.0143560 31.5160  57.635 1.58060 #> 543 56    1 1.3860e+01  0.07936600  1.2735000 10.2410  57.740 2.31850 #> 544 56    4 9.2232e+00 -0.05455800 -0.0300320 10.2410  57.740 2.31850 #> 545 56    5 7.7254e+00  0.12097000  1.3176000 10.2410  57.740 2.31850 #> 546 56    8 4.5377e+00 -0.03476200  0.1492300 10.2410  57.740 2.31850 #> 547 56   10 3.1826e+00 -0.04793900  0.0228800 10.2410  57.740 2.31850 #> 548 56   12 2.2321e+00  0.08865700  1.0122000 10.2410  57.740 2.31850 #> 550 57    1 6.9227e+00 -0.13473000 -1.9686000 24.4530 122.680 0.20826 #> 551 57    2 1.1293e+01  0.17418000  0.0249840 24.4530 122.680 0.20826 #> 552 57    3 1.3817e+01 -0.04535400 -1.6880000 24.4530 122.680 0.20826 #> 553 57    4 1.5026e+01 -0.05830900 -1.6810000 24.4530 122.680 0.20826 #> 554 57    5 1.5320e+01  0.06394800 -0.5701300 24.4530 122.680 0.20826 #> 555 57    6 1.4995e+01 -0.04037900 -1.1387000 24.4530 122.680 0.20826 #> 556 57    7 1.4270e+01 -0.11492000 -1.4757000 24.4530 122.680 0.20826 #> 557 57    8 1.3302e+01  0.11408000  0.4727400 24.4530 122.680 0.20826 #> 558 57    9 1.2207e+01  0.10101000  0.5682600 24.4530 122.680 0.20826 #> 559 57   10 1.1063e+01 -0.07171300 -0.5710700 24.4530 122.680 0.20826 #> 561 58    1 2.5711e+01  0.00773680 -0.5293700 31.1610  96.146 2.05250 #> 562 58    4 1.1812e+01  0.10058000 -0.1497500 31.1610  96.146 2.05250 #> 563 58    5 8.5492e+00 -0.02797600 -0.7956400 31.1610  96.146 2.05250 #> 564 58    6 6.1835e+00 -0.26579000 -2.2875000 31.1610  96.146 2.05250 #> 565 58    7 4.4719e+00  0.08902800  0.6540600 31.1610  96.146 2.05250 #> 566 58    8 3.2340e+00 -0.09399600 -0.5368000 31.1610  96.146 2.05250 #> 567 58   10 1.6913e+00  0.12337000  1.3084000 31.1610  96.146 2.05250 #> 569 59    1 3.3550e+01  0.07867700  0.2457100 10.2520  81.606 0.88692 #> 570 59    2 4.3410e+01 -0.07762100 -0.7281200 10.2520  81.606 0.88692 #> 571 59    3 4.3977e+01 -0.18504000 -1.3119000 10.2520  81.606 0.88692 #> 572 59    4 4.1130e+01  0.19036000  1.7016000 10.2520  81.606 0.88692 #> 573 59    6 3.3242e+01 -0.07616000 -0.1300400 10.2520  81.606 0.88692 #> 574 59    7 2.9481e+01  0.01997700  0.6330200 10.2520  81.606 0.88692 #> 575 59    8 2.6068e+01  0.13434000  1.5092000 10.2520  81.606 0.88692 #> 576 59   12 1.5805e+01 -0.01298600  0.2944900 10.2520  81.606 0.88692 #> 578 60    1 6.5817e+01  0.10990000  1.3867000 17.6650  57.699 2.83310 #> 579 60    2 5.2331e+01 -0.01550400 -0.1181400 17.6650  57.699 2.83310 #> 580 60    3 3.8758e+01  0.03101200  0.1366400 17.6650  57.699 2.83310 #> 581 60    4 2.8550e+01 -0.17933000 -1.4640000 17.6650  57.699 2.83310 #> 582 60    5 2.1021e+01  0.07461900  0.4641100 17.6650  57.699 2.83310 #> 583 60    6 1.5478e+01 -0.05411500 -0.5045200 17.6650  57.699 2.83310 #> 584 60    7 1.1396e+01  0.08110500  0.5174700 17.6650  57.699 2.83310 #> 585 60    8 8.3904e+00  0.14178000  0.9693200 17.6650  57.699 2.83310 #> 586 60   10 4.5484e+00 -0.07001200 -0.6543900 17.6650  57.699 2.83310 #> 587 60   12 2.4657e+00  0.00579400 -0.0951060 17.6650  57.699 2.83310 #> 589 61    1 1.0173e+01  0.04297900  0.3955400 26.5870  68.930 2.38350 #> 590 61    2 7.8553e+00  0.02860000 -0.3127000 26.5870  68.930 2.38350 #> 591 61    4 3.6987e+00 -0.10240000 -1.3165000 26.5870  68.930 2.38350 #> 592 61    5 2.5157e+00  0.08119400  0.1839600 26.5870  68.930 2.38350 #> 593 61    6 1.7107e+00 -0.12900000 -1.3038000 26.5870  68.930 2.38350 #> 594 61    7 1.1632e+00  0.08321600  0.3805600 26.5870  68.930 2.38350 #> 595 61    8 7.9094e-01  0.12525000  0.7525200 26.5870  68.930 2.38350 #> 596 61    9 5.3781e-01 -0.08889300 -0.8335900 26.5870  68.930 2.38350 #> 597 61   10 3.6569e-01  0.03913400  0.1555100 26.5870  68.930 2.38350 #> 598 61   11 2.4866e-01  0.00540810 -0.0877290 26.5870  68.930 2.38350 #> 600 62    1 4.8825e+01  0.04207900  0.3835100 11.9300  78.686 1.87730 #> 601 62    2 4.9427e+01  0.02899800  0.1362800 11.9300  78.686 1.87730 #> 602 62    3 4.3616e+01 -0.04439000 -0.3760400 11.9300  78.686 1.87730 #> 603 62    4 3.7655e+01  0.02961200  0.2764900 11.9300  78.686 1.87730 #> 604 62    5 3.2384e+01  0.09960600  0.8946100 11.9300  78.686 1.87730 #> 605 62    6 2.7833e+01 -0.10536000 -0.5843200 11.9300  78.686 1.87730 #> 606 62    7 2.3918e+01  0.03772900  0.5524200 11.9300  78.686 1.87730 #> 607 62    8 2.0553e+01  0.01640100  0.4290800 11.9300  78.686 1.87730 #> 608 62   10 1.5177e+01  0.05226200  0.7367700 11.9300  78.686 1.87730 #> 610 63    1 3.6355e+01  0.08981200  0.7463700 16.9870  56.371 1.19920 #> 611 63    2 3.7855e+01 -0.09363600 -0.2939700 16.9870  56.371 1.19920 #> 612 63    3 3.1309e+01 -0.17276000 -0.8692900 16.9870  56.371 1.19920 #> 613 63    4 2.4159e+01  0.20660000  1.8465000 16.9870  56.371 1.19920 #> 614 63    5 1.8174e+01  0.03557200  0.3308000 16.9870  56.371 1.19920 #> 615 63    7 1.0041e+01  0.05662200  0.0921260 16.9870  56.371 1.19920 #> 616 63    8 7.4372e+00 -0.04936900 -0.8380600 16.9870  56.371 1.19920 #> 617 63   10 4.0733e+00  0.10476000  0.2104300 16.9870  56.371 1.19920 #> 618 63   11 3.0138e+00 -0.09415700 -1.3027000 16.9870  56.371 1.19920 #> 620 64    1 2.3523e+01  0.11166000  0.1218500 20.3520  88.502 0.63827 #> 621 64    2 3.1116e+01 -0.11653000 -1.4177000 20.3520  88.502 0.63827 #> 622 64    3 3.1287e+01 -0.13158000 -1.2630000 20.3520  88.502 0.63827 #> 623 64    4 2.8326e+01  0.07393100  0.5058100 20.3520  88.502 0.63827 #> 624 64    5 2.4338e+01  0.01981500  0.2278000 20.3520  88.502 0.63827 #> 625 64    6 2.0305e+01  0.01994300  0.2857100 20.3520  88.502 0.63827 #> 626 64    8 1.3495e+01 -0.23305000 -1.6602000 20.3520  88.502 0.63827 #> 627 64    9 1.0865e+01  0.30509000  2.3606000 20.3520  88.502 0.63827 #> 628 64   11 6.9591e+00  0.04899200  0.3208300 20.3520  88.502 0.63827 #> 629 64   12 5.5504e+00 -0.18745000 -1.5037000 20.3520  88.502 0.63827 #> 631 65    1 2.2273e+01 -0.10476000 -1.4684000  9.5706 141.070 1.06080 #> 632 65    2 2.8523e+01  0.18992000  0.6221200  9.5706 141.070 1.06080 #> 633 65    3 2.9321e+01  0.02860900 -0.5076000  9.5706 141.070 1.06080 #> 634 65    4 2.8322e+01 -0.17131000 -1.8497000  9.5706 141.070 1.06080 #> 635 65    5 2.6784e+01  0.01702400 -0.2295400  9.5706 141.070 1.06080 #> 636 65    6 2.5138e+01  0.02116600 -0.0012811  9.5706 141.070 1.06080 #> 637 65    7 2.3527e+01 -0.10105000 -0.7373800  9.5706 141.070 1.06080 #> 638 65    9 2.0559e+01 -0.00774140  0.3066900  9.5706 141.070 1.06080 #> 639 65   10 1.9212e+01 -0.05633000  0.0869140  9.5706 141.070 1.06080 #> 640 65   12 1.6775e+01  0.20296000  2.3031000  9.5706 141.070 1.06080 #>           ETA1        ETA2      ETA3 AGE  HT     WT SECR SEX RACE SMOK HCTZ #> 2   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 3   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 4   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 5   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 6   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 7   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 8   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 9   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 10  -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 12  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 13  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 14  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 15  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 16  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 17  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 18  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 19  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 20  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 21  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 23  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 24  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 25  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 26  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 27  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 28  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 29  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 30  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 31  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 32  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 34   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 35   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 36   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 37   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 38   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 39   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 40   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 41   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 42   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 44  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 45  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 46  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 47  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 48  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 49  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 50  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 51  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 52  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 54  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 55  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 56  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 57  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 58  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 59  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 60  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 61  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 62  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 64  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 65  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 66  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 67  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 68  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 69  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 70  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 72   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 73   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 74   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 75   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 76   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 77   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 78   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 79   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 80   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 82   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 83   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 84   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 85   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 86   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 87   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 88   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 89   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 91   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 92   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 93   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 94   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 95   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 97   0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 98   0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 99   0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 100  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 101  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 102  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 103  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 104  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 105  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 107  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 108  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 109  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 110  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 111  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 112  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 113  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 114  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 115  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 116  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 118  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 119  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 120  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 121  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 122  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 123  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 124  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 125  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 126  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 127  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 129 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 130 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 131 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 132 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 133 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 134 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 135 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 136 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 137 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 138 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 140 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 141 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 142 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 143 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 144 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 145 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 146 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 147 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 148 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 149 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 151 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 152 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 153 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 154 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 155 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 156 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 157 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 158 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 159 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 160 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 162 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 163 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 164 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 165 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 166 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 167 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 168 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 169 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 170 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 171 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 173 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 174 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 175 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 176 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 177 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 178 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 179 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 180 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 181 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 182 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 184  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 185  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 186  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 187  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 188  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 189  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 190  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 191  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 192  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 194  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 195  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 196  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 197  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 198  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 199  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 200  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 201  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 202  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 204  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 205  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 206  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 207  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 208  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 209  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 210  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 212 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 213 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 214 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 215 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 216 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 217 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 218 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 219 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 220 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 221 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 223 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 224 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 225 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 226 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 227 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 228 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 229 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 230 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 231 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 232 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 234  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 235  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 236  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 237  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 238  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 239  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 240  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 241  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 242  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 243  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 245 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 246 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 247 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 248 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 249 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 250 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 251 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 252 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 253 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 254 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 256  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 257  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 258  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 259  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 260  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 261  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 262  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 263  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 264  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 265  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 267 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 268 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 269 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 270 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 271 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 272 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 273 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 274 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 275 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 276 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 278  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 279  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 280  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 281  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 282  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 283  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 284  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 285  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 286  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 288 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 289 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 290 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 291 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 292 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 293 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 294 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 295 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 296 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 297 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 299 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 300 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 301 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 302 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 303 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 304 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 305 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 306 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 307 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 308 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 310  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 311  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 312  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 313  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 314  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 315  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 316  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 317  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 318  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 319  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 321 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 322 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 323 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 324 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 325 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 326 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 327 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 328 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 329 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 330 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 332  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 333  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 334  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 335  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 336  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 337  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 338  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 339  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 341  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 342  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 343  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 344  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 345  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 346  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 347  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 348  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 350  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 351  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 352  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 353  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 354  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 355  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 356  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 357  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 358  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 359  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 361 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 362 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 363 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 364 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 365 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 367  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 368  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 369  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 370  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 371  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 373  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 374  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 375  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 376  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 377  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 378  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 379  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 380  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 382  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 383  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 384  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 385  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 386  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 387  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 388  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 389  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 390  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 391  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 393 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 394 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 395 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 396 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 397 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 398 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 399 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 400 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 401 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 402 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 404  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 405  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 406  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 407  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 408  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 409  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 411  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 412  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 413  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 414  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 415  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 416  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 417  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 418  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 419  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 420  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 422  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 423  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 424  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 425  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 426  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 427  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 428  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 429  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 430  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 431  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 433  0.4472500 -0.26326000  0.170240  51 170  83.14  1.2   1    2    1    1 #> 434  0.4472500 -0.26326000  0.170240  51 170  83.14  1.2   1    2    1    1 #> 435  0.4472500 -0.26326000  0.170240  51 170  83.14  1.2   1    2    1    1 #> 436  0.4472500 -0.26326000  0.170240  51 170  83.14  1.2   1    2    1    1 #> 438 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 439 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 440 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 441 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 442 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 443 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 444 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 445 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 446 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 447 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 449 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 450 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 451 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 452 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 453 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 455 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 456 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 457 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 458 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 459 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 460 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 461 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 462 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 463 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 464 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 465 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 466 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 468  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 469  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 470  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 471  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 472  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 473  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 474  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 475  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 476  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 477  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 479  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 480  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 481  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 482  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 483  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 484  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 485  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 486  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 487  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 488  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 490 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 491 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 492 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 493 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 494 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 495 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 496 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 497 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 498 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 500 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 501 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 502 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 503 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 504 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 505 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 506 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 507 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 508 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 509 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 511 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 512 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 513 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 514 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 515 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 516 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 517 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 518 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 519 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 520 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 522  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 523  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 524  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 525  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 526  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 527  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 528  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 529  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 530  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 531  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 533  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 534  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 535  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 536  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 537  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 538  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 539  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 540  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 541  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 543 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 544 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 545 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 546 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 547 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 548 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 550  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 551  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 552  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 553  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 554  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 555  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 556  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 557  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 558  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 559  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 561  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 562  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 563  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 564  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 565  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 566  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 567  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 569 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 570 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 571 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 572 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 573 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 574 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 575 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 576 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 578 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 579 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 580 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 581 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 582 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 583 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 584 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 585 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 586 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 587 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 589  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 590  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 591  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 592  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 593  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 594  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 595  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 596  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 597  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 598  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 600 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 601 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 602 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 603 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 604 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 605 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 606 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 607 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 608 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 610 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 611 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 612 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 613 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 614 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 615 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 616 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 617 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 618 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 620  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 621  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 622  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 623  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 624  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 625  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 626  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 627  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 628  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 629  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 631 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 632 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 633 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 634 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 635 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 636 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 637 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 638 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 639 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 640 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #>     PROP CON OCC     DV     PRED         RES       WRES #> 2      1   1   0  71.74 86.41800 -14.6780000 -0.1046800 #> 3      1   1   0  72.61 89.00800 -16.3980000 -0.7589000 #> 4      1   1   0  88.01 75.46800  12.5420000  1.2075000 #> 5      1   1   0  53.13 61.03600  -7.9060000 -1.5386000 #> 6      1   1   0  56.83 48.71100   8.1188000 -0.1152000 #> 7      1   1   0  51.94 38.72400  13.2160000  0.3580400 #> 8      1   1   0  52.89 30.74800  22.1420000  2.0247000 #> 9      1   1   0  26.95 19.37200   7.5781000 -1.3045000 #> 10     1   1   0  26.17 12.20200  13.9680000  1.3377000 #> 12     0   0   0 108.75 86.41800  22.3320000  0.7885000 #> 13     0   0   0  96.60 89.00800   7.5921000  0.2056300 #> 14     0   0   0  81.00 75.46800   5.5322000 -0.1610800 #> 15     0   0   0  77.07 61.03600  16.0340000  0.3367900 #> 16     0   0   0  64.57 48.71100  15.8590000 -0.4293500 #> 17     0   0   0  50.21 38.72400  11.4860000 -1.9657000 #> 18     0   0   0  64.58 30.74800  33.8320000  1.7130000 #> 19     0   0   0  50.54 24.40700  26.1330000 -0.0245470 #> 20     0   0   0  41.56 15.37500  26.1850000  0.4600500 #> 21     0   0   0  42.46  9.68460  32.7750000  5.4087000 #> 23     0   0   0   9.35  8.64180   0.7082200  0.4329000 #> 24     0   0   0   8.66  8.90080  -0.2407900 -0.2000400 #> 25     0   0   0   8.18  7.54680   0.6332200  0.2378000 #> 26     0   0   0   6.19  6.10360   0.0863950 -0.9516600 #> 27     0   0   0   7.08  4.87110   2.2089000  1.2177000 #> 28     0   0   0   4.68  3.87240   0.8076300 -1.4248000 #> 29     0   0   0   5.30  3.07480   2.2252000  0.8501900 #> 30     0   0   0   4.20  2.44070   1.7593000 -0.2344000 #> 31     0   0   0   3.92  1.53750   2.3825000  2.1772000 #> 32     0   0   0   2.75  0.96846   1.7815000  1.4402000 #> 34     0   0   0  63.15 43.20900  19.9410000  0.4473600 #> 35     0   0   0  62.41 44.50400  17.9060000  1.5121000 #> 36     0   0   0  41.05 37.73400   3.3161000  0.2790000 #> 37     0   0   0  24.31 30.51800  -6.2080000 -0.9520100 #> 38     0   0   0  18.17 24.35600  -6.1856000 -0.5277300 #> 39     0   0   0  13.23 19.36200  -6.1319000 -0.2349800 #> 40     0   0   0   8.48 15.37400  -6.8942000 -0.4056700 #> 41     0   0   0   3.92  9.68590  -5.7659000 -0.0433470 #> 42     0   0   0   1.76  6.10120  -4.3412000  0.3823900 #> 44     0   1   0  21.49 17.28400   4.2064000  1.1866000 #> 45     0   1   0  14.87 17.80200  -2.9316000 -1.4188000 #> 46     0   1   0  15.17 15.09400   0.0764420 -0.3857700 #> 47     0   1   0  13.68 12.20700   1.4728000 -0.0473140 #> 48     0   1   0  14.83  9.74220   5.0878000  2.0694000 #> 49     0   1   0   9.86  7.74470   2.1153000 -0.3700300 #> 50     0   1   0   8.75  6.14970   2.6003000 -0.1412700 #> 51     0   1   0   7.77  3.87440   3.8956000  1.7438000 #> 52     0   1   0   4.36  2.44050   1.9195000 -0.7673200 #> 54     1   1   0  74.53 86.41800 -11.8880000  0.1878600 #> 55     1   1   0  63.02 89.00800 -25.9880000 -2.0608000 #> 56     1   1   0 100.46 75.46800  24.9920000  1.6366000 #> 57     1   1   0  84.92 61.03600  23.8840000  1.3909000 #> 58     1   1   0  59.01 48.71100  10.2990000 -0.4326400 #> 59     1   1   0  53.87 38.72400  15.1460000  0.2855400 #> 60     1   1   0  45.15 30.74800  14.4020000  0.3112900 #> 61     1   1   0  33.88 24.40700   9.4727000 -0.5882100 #> 62     1   1   0  26.27 15.37500  10.8950000  0.4373300 #> 64     1   1   0 184.78 86.41800  98.3620000  2.2384000 #> 65     1   1   0 142.73 89.00800  53.7220000  1.2010000 #> 66     1   1   0  86.18 75.46800  10.7120000 -0.6598200 #> 67     1   1   0  65.65 61.03600   4.6140000 -0.2778000 #> 68     1   1   0  55.07 48.71100   6.3588000  0.6249000 #> 69     1   1   0  37.95 38.72400  -0.7737400 -0.0320410 #> 70     1   1   0  23.25 24.40700  -1.1573000  0.3174400 #> 72     0   0   0  37.72 43.20900  -5.4889000  0.3581300 #> 73     0   0   0  19.15 44.50400 -25.3540000 -1.6325000 #> 74     0   0   0  14.41 37.73400 -23.3240000 -1.1230000 #> 75     0   0   0  11.89 30.51800 -18.6280000 -0.4161400 #> 76     0   0   0   5.48 24.35600 -18.8760000 -1.0534000 #> 77     0   0   0   4.93 19.36200 -14.4320000 -0.4265600 #> 78     0   0   0   4.01 15.37400 -11.3640000 -0.0415680 #> 79     0   0   0   1.54  9.68590  -8.1459000 -0.0933550 #> 80     0   0   0   0.60  6.10120  -5.5012000  0.0182380 #> 82     0   1   0  52.58 43.20900   9.3711000 -0.0440400 #> 83     0   1   0  57.71 44.50400  13.2060000  1.4433000 #> 84     0   1   0  36.23 37.73400  -1.5039000 -0.2411200 #> 85     0   1   0  26.13 30.51800  -4.3880000 -0.2810900 #> 86     0   1   0  18.07 24.35600  -6.2856000 -0.3939600 #> 87     0   1   0  10.83 19.36200  -8.5319000 -0.9388200 #> 88     0   1   0   5.49 12.20400  -6.7137000 -0.3228700 #> 89     0   1   0   3.05  7.68740  -4.6374000  0.4733600 #> 91     1   1   0  61.76 43.20900  18.5510000  1.9490000 #> 92     1   1   0  18.40 44.50400 -26.1040000 -2.2177000 #> 93     1   1   0   4.82 37.73400 -32.9140000 -2.3910000 #> 94     1   1   0   0.08 19.36200 -19.2820000 -0.5936900 #> 95     1   1   0   0.01 12.20400 -12.1940000  0.1593100 #> 97     0   1   0  16.63 17.28400  -0.6535600  0.0527370 #> 98     0   1   0  16.48 17.80200  -1.3216000  0.0625720 #> 99     0   1   0  11.11 15.09400  -3.9836000 -1.2067000 #> 100    0   1   0  11.98 12.20700  -0.2272100  0.5516600 #> 101    0   1   0   7.73  9.74220  -2.0122000 -0.8340600 #> 102    0   1   0   8.08  7.74470   0.3352500  0.7322900 #> 103    0   1   0   5.74  6.14970  -0.4096900 -0.1247500 #> 104    0   1   0   3.87  3.87440  -0.0043778 -0.0254460 #> 105    0   1   0   2.73  2.44050   0.2895200  0.2845800 #> 107    0   0   0  13.98 17.28400  -3.3036000 -0.2248100 #> 108    0   0   0  12.08 17.80200  -5.7216000 -0.6398200 #> 109    0   0   0   9.14 15.09400  -5.9536000 -0.6147700 #> 110    0   0   0   7.21 12.20700  -4.9972000 -0.1663700 #> 111    0   0   0   4.76  9.74220  -4.9822000 -0.3438600 #> 112    0   0   0   2.50  7.74470  -5.2447000 -0.9313700 #> 113    0   0   0   1.79  6.14970  -4.3597000 -0.6245000 #> 114    0   0   0   1.32  4.88150  -3.5615000 -0.2851000 #> 115    0   0   0   0.62  3.07500  -2.4550000  0.1267500 #> 116    0   0   0   0.16  1.93690  -1.7769000  0.1974400 #> 118    0   1   0  27.05 17.28400   9.7664000  1.3162000 #> 119    0   1   0  19.34 17.80200   1.5384000 -0.2032300 #> 120    0   1   0  13.79 15.09400  -1.3036000 -0.2773100 #> 121    0   1   0  10.04 12.20700  -2.1672000 -0.1149000 #> 122    0   1   0   7.52  9.74220  -2.2222000  0.1497800 #> 123    0   1   0   3.78  7.74470  -3.9647000 -1.2185000 #> 124    0   1   0   3.47  6.14970  -2.6797000 -0.2328100 #> 125    0   1   0   3.10  4.88150  -1.7815000  0.6045400 #> 126    0   1   0   1.01  3.07500  -2.0650000 -0.3626600 #> 127    0   1   0   0.60  1.93690  -1.3369000  0.1924700 #> 129    0   1   0   8.88 17.28400  -8.4036000 -0.7630900 #> 130    0   1   0  11.07 17.80200  -6.7316000 -0.9374600 #> 131    0   1   0  14.29 15.09400  -0.8035600  0.7729700 #> 132    0   1   0   8.91 12.20700  -3.2972000 -1.3204000 #> 133    0   1   0   9.92  9.74220   0.1777500  0.1982200 #> 134    0   1   0   8.80  7.74470   1.0553000  0.4668600 #> 135    0   1   0   6.79  6.14970   0.6403100 -0.2555600 #> 136    0   1   0   5.76  4.88150   0.8785400 -0.2871200 #> 137    0   1   0   4.00  3.07500   0.9250300 -0.5526400 #> 138    0   1   0   4.02  1.93690   2.0831000  2.1753000 #> 140    0   1   0   9.79 17.28400  -7.4936000 -0.7231900 #> 141    0   1   0  16.00 17.80200  -1.8016000 -0.2099000 #> 142    0   1   0  15.77 15.09400   0.6764400 -0.5643600 #> 143    0   1   0  17.11 12.20700   4.9028000  0.7863200 #> 144    0   1   0  16.56  9.74220   6.8178000  1.7317000 #> 145    0   1   0  11.53  7.74470   3.7853000 -0.7409200 #> 146    0   1   0  12.31  6.14970   6.1603000  1.5476000 #> 147    0   1   0   8.91  4.88150   4.0285000 -0.4824800 #> 148    0   1   0   7.08  3.07500   4.0050000  0.4530500 #> 149    0   1   0   4.96  1.93690   3.0231000  0.1648600 #> 151    0   0   0 176.18 86.41800  89.7620000  2.3031000 #> 152    0   0   0 138.65 89.00800  49.6420000  0.2759400 #> 153    0   0   0 112.75 75.46800  37.2820000  0.2054100 #> 154    0   0   0  93.39 61.03600  32.3540000  0.3705000 #> 155    0   0   0  86.78 48.71100  38.0690000  1.7808000 #> 156    0   0   0  66.81 38.72400  28.0860000  0.9480500 #> 157    0   0   0  46.65 30.74800  15.9020000 -0.8026900 #> 158    0   0   0  45.43 24.40700  21.0230000  0.8812200 #> 159    0   0   0  31.86 15.37500  16.4850000  1.1844000 #> 160    0   0   0  16.42  9.68460   6.7354000 -1.2682000 #> 162    0   1   0  56.35 43.20900  13.1410000  0.8167300 #> 163    0   1   0  47.18 44.50400   2.6760000 -0.4904200 #> 164    0   1   0  44.91 37.73400   7.1761000  0.4632300 #> 165    0   1   0  41.94 30.51800  11.4220000  1.5409000 #> 166    0   1   0  29.04 24.35600   4.6844000 -0.1086600 #> 167    0   1   0  17.36 19.36200  -2.0019000 -2.4648000 #> 168    0   1   0  22.15 15.37400   6.7758000  0.6181100 #> 169    0   1   0  18.05 12.20400   5.8463000  0.3571800 #> 170    0   1   0  13.64  7.68740   5.9526000  0.9791300 #> 171    0   1   0  10.16  4.84230   5.3177000  1.5056000 #> 173    0   1   0  56.37 43.20900  13.1610000  0.9526300 #> 174    0   1   0  47.02 44.50400   2.5160000 -0.9587600 #> 175    0   1   0  58.38 37.73400  20.6460000  2.0735000 #> 176    0   1   0  36.77 30.51800   6.2520000 -1.1225000 #> 177    0   1   0  38.95 24.35600  14.5940000  0.7999500 #> 178    0   1   0  28.99 19.36200   9.6281000 -0.7622200 #> 179    0   1   0  32.83 15.37400  17.4560000  2.4129000 #> 180    0   1   0  24.78 12.20400  12.5760000  0.8336600 #> 181    0   1   0  16.52  7.68740   8.8326000 -0.1377700 #> 182    0   1   0  12.04  4.84230   7.1977000  0.0893640 #> 184    0   1   0  10.88 17.28400  -6.4036000 -0.3056700 #> 185    0   1   0  11.89 17.80200  -5.9116000 -0.9129400 #> 186    0   1   0   9.42 15.09400  -5.6736000 -1.8347000 #> 187    0   1   0  13.67 12.20700   1.4628000  1.2351000 #> 188    0   1   0  10.97  9.74220   1.2278000  0.8110100 #> 189    0   1   0   9.21  7.74470   1.4653000  0.8004800 #> 190    0   1   0   6.33  6.14970   0.1803100 -0.6032500 #> 191    0   1   0   4.95  3.87440   1.0756000  0.2100400 #> 192    0   1   0   2.85  2.44050   0.4095200 -0.8834200 #> 194    0   1   0   2.23  8.64180  -6.4118000 -1.3363000 #> 195    0   1   0   3.53  7.54680  -4.0168000 -1.5215000 #> 196    0   1   0   4.13  6.10360  -1.9736000 -0.3549100 #> 197    0   1   0   3.84  4.87110  -1.0311000  0.1707500 #> 198    0   1   0   2.90  3.87240  -0.9723700 -0.3125800 #> 199    0   1   0   2.61  3.07480  -0.4648500  0.1279000 #> 200    0   1   0   2.50  2.44070   0.0592690  0.9663800 #> 201    0   1   0   1.43  1.53750  -0.1074800  0.0325670 #> 202    0   1   0   0.66  0.96846  -0.3084600 -1.3870000 #> 204    0   1   0  19.76 30.24600 -10.4860000 -0.7882200 #> 205    0   1   0  23.99 31.15300  -7.1628000  0.1410700 #> 206    0   1   0  15.86 26.41400 -10.5540000 -0.7553300 #> 207    0   1   0  12.01 21.36300  -9.3526000 -0.5786700 #> 208    0   1   0   7.70 17.04900  -9.3489000 -0.8464900 #> 209    0   1   0   6.29 13.55300  -7.2633000 -0.2496300 #> 210    0   1   0   4.11 10.76200  -6.6520000 -0.2844400 #> 212    0   1   0  15.43 30.24600 -14.8160000 -0.5491600 #> 213    0   1   0  17.58 31.15300 -13.5730000 -0.6875500 #> 214    0   1   0  17.85 26.41400  -8.5637000 -0.6179800 #> 215    0   1   0  17.46 21.36300  -3.9026000 -0.5683400 #> 216    0   1   0  14.53 17.04900  -2.5189000 -1.4462000 #> 217    0   1   0  17.52 13.55300   3.9667000  0.2799700 #> 218    0   1   0  15.93 10.76200   5.1680000  0.1594100 #> 219    0   1   0  15.72  8.54260   7.1774000  0.9870900 #> 220    0   1   0  12.62  5.38120   7.2388000  1.0645000 #> 221    0   1   0  10.71  3.38960   7.3204000  2.3721000 #> 223    0   0   0  15.35 30.24600 -14.8960000 -0.3807800 #> 224    0   0   0  16.73 31.15300 -14.4230000 -1.9738000 #> 225    0   0   0  29.66 26.41400   3.2463000  0.7666200 #> 226    0   0   0  29.59 21.36300   8.2274000  1.2045000 #> 227    0   0   0  24.34 17.04900   7.2911000  0.0760030 #> 228    0   0   0  19.81 13.55300   6.2567000 -1.0712000 #> 229    0   0   0  25.61 10.76200  14.8480000  3.3746000 #> 230    0   0   0  12.90  8.54260   4.3574000 -3.2514000 #> 231    0   0   0  14.45  5.38120   9.0688000  0.8283500 #> 232    0   0   0  12.65  3.38960   9.2604000  3.2901000 #> 234    0   1   0  28.33 30.24600  -1.9162000  0.0596700 #> 235    0   1   0  23.91 31.15300  -7.2428000 -0.8256700 #> 236    0   1   0  22.45 26.41400  -3.9637000  0.1329300 #> 237    0   1   0  15.55 21.36300  -5.8126000 -0.3655400 #> 238    0   1   0  12.41 17.04900  -4.6389000  0.0159090 #> 239    0   1   0   8.77 13.55300  -4.7833000 -0.1556000 #> 240    0   1   0   5.83 10.76200  -4.9320000 -0.4580100 #> 241    0   1   0   4.05  8.54260  -4.4926000 -0.4987500 #> 242    0   1   0   3.38  6.78020  -3.4002000 -0.0191180 #> 243    0   1   0   2.26  5.38120  -3.1212000 -0.1289100 #> 245    0   0   0   8.83 30.24600 -21.4160000 -1.0485000 #> 246    0   0   0  16.84 31.15300 -14.3130000 -1.0352000 #> 247    0   0   0  25.07 26.41400  -1.3437000  0.4787100 #> 248    0   0   0  19.91 21.36300  -1.4526000 -1.0219000 #> 249    0   0   0  20.68 17.04900   3.6311000 -0.4199200 #> 250    0   0   0  22.01 13.55300   8.4567000  0.9411800 #> 251    0   0   0  19.94 10.76200   9.1780000  0.9748000 #> 252    0   0   0  15.65  8.54260   7.1074000 -0.5216600 #> 253    0   0   0  14.46  5.38120   9.0788000  1.5548000 #> 254    0   0   0  10.65  3.38960   7.2604000  1.2194000 #> 256    0   0   0  40.74 43.20900  -2.4689000 -0.5567100 #> 257    0   0   0  54.32 44.50400   9.8160000  1.2218000 #> 258    0   0   0  37.44 37.73400  -0.2938900 -0.3753300 #> 259    0   0   0  29.80 30.51800  -0.7180200 -0.2547000 #> 260    0   0   0  26.36 24.35600   2.0044000  0.8036100 #> 261    0   0   0  17.04 19.36200  -2.3219000 -0.1913100 #> 262    0   0   0  13.14 15.37400  -2.2342000  0.0482940 #> 263    0   0   0   7.92 12.20400  -4.2837000 -0.7763400 #> 264    0   0   0   4.21  7.68740  -3.4774000 -0.4802900 #> 265    0   0   0   2.38  4.84230  -2.4623000 -0.0035721 #> 267    0   1   0  84.19 86.41800  -2.2278000 -0.0087724 #> 268    0   1   0  86.20 89.00800  -2.8079000 -0.0150950 #> 269    0   1   0  74.03 75.46800  -1.4378000  0.0250920 #> 270    0   1   0  58.20 61.03600  -2.8360000 -0.2802300 #> 271    0   1   0  47.00 48.71100  -1.7112000 -0.3401600 #> 272    0   1   0  40.57 38.72400   1.8463000  0.0539970 #> 273    0   1   0  31.51 30.74800   0.7615500 -0.2643100 #> 274    0   1   0  30.41 24.40700   6.0027000  0.8814800 #> 275    0   1   0  20.17 15.37500   4.7952000  0.7985900 #> 276    0   1   0  10.19  9.68460   0.5054500 -0.7755900 #> 278    1   1   0  22.06 86.41800 -64.3580000 -1.4038000 #> 279    1   1   0  40.41 75.46800 -35.0580000 -0.7718800 #> 280    1   1   0  34.71 61.03600 -26.3260000 -0.7881300 #> 281    1   1   0  26.09 48.71100 -22.6210000 -1.1968000 #> 282    1   1   0  28.70 38.72400 -10.0240000  0.0137490 #> 283    1   1   0  30.07 30.74800  -0.6784500  1.2974000 #> 284    1   1   0  19.96 24.40700  -4.4473000  0.0819480 #> 285    1   1   0  10.01 15.37500  -5.3648000 -1.1756000 #> 286    1   1   0  10.61  9.68460   0.9254500  0.6298800 #> 288    1   1   0  57.81 86.41800 -28.6080000 -1.0171000 #> 289    1   1   0 103.55 89.00800  14.5420000  1.5037000 #> 290    1   1   0  76.00 75.46800   0.5322100 -0.5235000 #> 291    1   1   0  72.99 61.03600  11.9540000 -0.0428260 #> 292    1   1   0  56.53 48.71100   7.8188000 -1.1581000 #> 293    1   1   0  60.08 38.72400  21.3560000  0.5892500 #> 294    1   1   0  57.04 30.74800  26.2920000  1.5879000 #> 295    1   1   0  37.89 24.40700  13.4830000 -1.1351000 #> 296    1   1   0  38.80 15.37500  23.4250000  2.4769000 #> 297    1   1   0  23.21  9.68460  13.5250000  0.1597400 #> 299    0   0   0  13.34 17.28400  -3.9436000 -0.5981800 #> 300    0   0   0  17.91 17.80200   0.1084200  0.3275900 #> 301    0   0   0  17.23 15.09400   2.1364000  0.7439700 #> 302    0   0   0  11.74 12.20700  -0.4672100 -1.0323000 #> 303    0   0   0  10.35  9.74220   0.6077500 -0.7557100 #> 304    0   0   0  10.74  7.74470   2.9953000  0.9828100 #> 305    0   0   0   9.40  6.14970   3.2503000  1.4048000 #> 306    0   0   0   6.42  4.88150   1.5385000 -0.3303800 #> 307    0   0   0   4.26  3.07500   1.1850000 -0.6478000 #> 308    0   0   0   3.56  1.93690   1.6231000  0.8438100 #> 310    0   1   0  17.32 30.24600 -12.9260000 -0.5028900 #> 311    0   1   0  17.54 31.15300 -13.6130000 -0.7219100 #> 312    0   1   0  12.74 26.41400 -13.6740000 -1.4278000 #> 313    0   1   0  14.73 21.36300  -6.6326000 -0.1076800 #> 314    0   1   0  13.02 17.04900  -4.0289000  0.1655500 #> 315    0   1   0   9.25 13.55300  -4.3033000 -0.6620100 #> 316    0   1   0   7.85 10.76200  -2.9120000 -0.6499700 #> 317    0   1   0   8.40  8.54260  -0.1425600  0.5523300 #> 318    0   1   0   5.16  5.38120  -0.2212000 -0.2945200 #> 319    0   1   0   5.04  3.38960   1.6504000  1.5090000 #> 321    0   0   0 105.94 86.41800  19.5220000  0.2722700 #> 322    0   0   0 120.44 89.00800  31.4320000  0.9209500 #> 323    0   0   0 104.12 75.46800  28.6520000  0.5226400 #> 324    0   0   0  91.11 61.03600  30.0740000  0.5458800 #> 325    0   0   0  84.46 48.71100  35.7490000  1.3160000 #> 326    0   0   0  58.59 38.72400  19.8660000 -1.1113000 #> 327    0   0   0  53.96 30.74800  23.2120000 -0.4733800 #> 328    0   0   0  43.25 24.40700  18.8430000 -1.2681000 #> 329    0   0   0  46.03 15.37500  30.6550000  3.4980000 #> 330    0   0   0  29.78  9.68460  20.0950000  1.8245000 #> 332    0   1   0   8.48  8.90080  -0.4207900  0.3139300 #> 333    0   1   0   5.39  7.54680  -2.1568000 -0.6127600 #> 334    0   1   0   3.09  6.10360  -3.0136000 -1.4096000 #> 335    0   1   0   3.46  4.87110  -1.4111000  0.6260700 #> 336    0   1   0   1.79  3.87240  -2.0824000 -0.4989700 #> 337    0   1   0   1.08  3.07480  -1.9948000 -0.6730500 #> 338    0   1   0   0.59  1.93720  -1.3472000  0.0034753 #> 339    0   1   0   0.33  1.53750  -1.2075000 -0.0664710 #> 341    0   1   0  27.99 43.20900 -15.2190000 -0.9792600 #> 342    0   1   0  42.82 44.50400  -1.6840000  0.8669000 #> 343    0   1   0  25.66 37.73400 -12.0740000 -1.2151000 #> 344    0   1   0  24.67 30.51800  -5.8480000 -0.1331500 #> 345    0   1   0  18.75 24.35600  -5.6056000 -0.1523800 #> 346    0   1   0  14.15 19.36200  -5.2119000 -0.1195700 #> 347    0   1   0  11.46 15.37400  -3.9142000  0.2751800 #> 348    0   1   0   2.88  7.68740  -4.8074000 -0.8732800 #> 350    1   1   0 134.89 86.41800  48.4720000  1.0059000 #> 351    1   1   0 107.21 89.00800  18.2020000  0.7409900 #> 352    1   1   0  63.25 75.46800 -12.2180000 -0.4192800 #> 353    1   1   0  38.67 61.03600 -22.3660000 -0.7827000 #> 354    1   1   0  26.35 48.71100 -22.3610000 -0.5380100 #> 355    1   1   0  14.12 38.72400 -24.6040000 -0.9451500 #> 356    1   1   0  11.74 30.74800 -19.0080000 -0.1392400 #> 357    1   1   0   6.08 24.40700 -18.3270000 -0.3232800 #> 358    1   1   0   2.10 15.37500 -13.2750000  0.1292200 #> 359    1   1   0   0.89  9.68460  -8.7946000  0.7481900 #> 361    1   1   0  23.32 43.20900 -19.8890000 -0.4608500 #> 362    1   1   0  25.76 44.50400 -18.7440000 -1.5352000 #> 363    1   1   0  36.97 37.73400  -0.7638900  0.3634600 #> 364    1   1   0  22.21 12.20400  10.0060000  1.7239000 #> 365    1   1   0   8.49  4.84230   3.6477000 -1.1486000 #> 367    0   1   0  70.92 43.20900  27.7110000  1.5130000 #> 368    0   1   0  47.75 44.50400   3.2460000 -0.5589000 #> 369    0   1   0  40.87 37.73400   3.1361000  0.7627200 #> 370    0   1   0  21.24 30.51800  -9.2780000 -1.1218000 #> 371    0   1   0  12.55 19.36200  -6.8119000 -0.0939310 #> 373    0   1   0   6.32  8.64180  -2.3218000 -0.4219400 #> 374    0   1   0   7.29  8.90080  -1.6108000 -0.0486050 #> 375    0   1   0   5.58  7.54680  -1.9668000 -0.8715900 #> 376    0   1   0   5.72  6.10360  -0.3836000  0.2607500 #> 377    0   1   0   3.81  4.87110  -1.0611000 -1.1438000 #> 378    0   1   0   3.48  3.07480   0.4051500  0.4334100 #> 379    0   1   0   3.17  2.44070   0.7292700  1.0190000 #> 380    0   1   0   1.93  1.53750   0.3925200 -0.0340300 #> 382    0   1   0  33.86 30.24600   3.6138000  0.9143500 #> 383    0   1   0  18.36 31.15300 -12.7930000 -1.8667000 #> 384    0   1   0  19.47 26.41400  -6.9437000 -0.2195900 #> 385    0   1   0  15.85 21.36300  -5.5126000  0.1283900 #> 386    0   1   0  10.68 17.04900  -6.3689000 -0.5156900 #> 387    0   1   0   7.84 13.55300  -5.7133000 -0.7397400 #> 388    0   1   0   7.81 10.76200  -2.9520000  0.2422500 #> 389    0   1   0   5.84  8.54260  -2.7026000 -0.0206860 #> 390    0   1   0   4.60  5.38120  -0.7812000  0.8291700 #> 391    0   1   0   1.99  3.38960  -1.3996000 -0.5577000 #> 393    0   1   0  45.40 30.24600  15.1540000  1.1901000 #> 394    0   1   0  44.07 31.15300  12.9170000  0.1748600 #> 395    0   1   0  37.28 26.41400  10.8660000 -0.5761700 #> 396    0   1   0  41.31 21.36300  19.9470000  2.2912000 #> 397    0   1   0  37.36 17.04900  20.3110000  3.0956000 #> 398    0   1   0  24.23 13.55300  10.6770000 -0.5784000 #> 399    0   1   0  22.39 10.76200  11.6280000  0.4265800 #> 400    0   1   0  15.46  8.54260   6.9174000 -2.0041000 #> 401    0   1   0  15.16  5.38120   9.7788000  2.0242000 #> 402    0   1   0   9.55  3.38960   6.1604000  0.5050800 #> 404    0   1   0 110.98 86.41800  24.5620000  0.8377100 #> 405    0   1   0  72.34 89.00800 -16.6680000 -0.1691800 #> 406    0   1   0  30.91 75.46800 -44.5580000 -1.4801000 #> 407    0   1   0  14.00 61.03600 -47.0360000 -1.5545000 #> 408    0   1   0   3.13 38.72400 -35.5940000 -0.7535500 #> 409    0   1   0   0.67 24.40700 -23.7370000  0.2648300 #> 411    0   1   0   4.69  8.64180  -3.9518000 -0.7363700 #> 412    0   1   0   5.80  8.90080  -3.1008000 -0.6896900 #> 413    0   1   0   5.90  7.54680  -1.6468000 -0.2073600 #> 414    0   1   0   5.23  6.10360  -0.8736000 -0.0407330 #> 415    0   1   0   4.68  4.87110  -0.1911200  0.3268300 #> 416    0   1   0   3.56  3.87240  -0.3123700 -0.3100800 #> 417    0   1   0   2.48  3.07480  -0.5948500 -1.3559000 #> 418    0   1   0   2.50  2.44070   0.0592690 -0.3571200 #> 419    0   1   0   2.14  1.53750   0.6025200  0.8950800 #> 420    0   1   0   1.67  0.96846   0.7015400  1.6255000 #> 422    0   0   0  28.90 30.24600  -1.3462000  0.0715470 #> 423    0   0   0  21.08 31.15300 -10.0730000 -0.6122900 #> 424    0   0   0  15.09 26.41400 -11.3240000 -0.3348000 #> 425    0   0   0   8.77 21.36300 -12.5930000 -0.6169300 #> 426    0   0   0   3.41 17.04900 -13.6390000 -1.3182000 #> 427    0   0   0   1.84 13.55300 -11.7130000 -1.0482000 #> 428    0   0   0   1.09 10.76200  -9.6720000 -0.6426900 #> 429    0   0   0   0.48  8.54260  -8.0626000 -0.3625300 #> 430    0   0   0   0.14  5.38120  -5.2412000  0.3663000 #> 431    0   0   0   0.03  3.38960  -3.3596000  0.9289900 #> 433    0   0   0  53.00 43.20900   9.7911000  0.4838700 #> 434    0   0   0  17.98 30.51800 -12.5380000 -0.9709500 #> 435    0   0   0   2.75 12.20400  -9.4537000 -0.6910300 #> 436    0   0   0   0.67  6.10120  -5.4312000  0.1809200 #> 438    0   1   0 161.86 86.41800  75.4420000  1.3232000 #> 439    0   1   0 157.57 89.00800  68.5620000  1.6001000 #> 440    0   1   0 116.89 75.46800  41.4220000  0.4487300 #> 441    0   1   0 102.18 61.03600  41.1440000  1.5771000 #> 442    0   1   0  69.69 48.71100  20.9790000  0.0291150 #> 443    0   1   0  48.29 38.72400   9.5663000 -1.0206000 #> 444    0   1   0  47.60 30.74800  16.8520000  1.0753000 #> 445    0   1   0  38.98 24.40700  14.5730000  1.4718000 #> 446    0   1   0  21.22 19.37200   1.8481000 -1.2219000 #> 447    0   1   0  14.56 12.20200   2.3576000 -0.2632600 #> 449    1   1   0  73.72 86.41800 -12.6980000 -0.1209400 #> 450    1   1   0  62.77 61.03600   1.7340000 -0.1419800 #> 451    1   1   0  52.28 48.71100   3.5688000 -0.7044700 #> 452    1   1   0  41.83 24.40700  17.4230000  0.1947900 #> 453    1   1   0  30.84  9.68460  21.1550000  2.9455000 #> 455    0   1   0 119.97 43.20900  76.7610000  4.9655000 #> 456    0   1   0  55.31 44.50400  10.8060000 -2.7221000 #> 457    0   1   0  52.02 37.73400  14.2860000 -0.3673400 #> 458    0   1   0  49.41 30.51800  18.8920000  1.7875000 #> 459    0   1   0  40.56 24.35600  16.2040000  2.0447000 #> 460    0   1   0  33.93 19.36200  14.5680000  2.3457000 #> 461    0   1   0  23.22 15.37400   7.8458000  0.4696400 #> 462    0   1   0  15.22 12.20400   3.0163000 -1.3597000 #> 463    0   1   0  12.25  9.68590   2.5641000 -1.4216000 #> 464    0   1   0  13.64  7.68740   5.9526000  1.0879000 #> 465    0   1   0   7.72  6.10120   1.6188000 -1.6642000 #> 466    0   1   0   9.18  4.84230   4.3377000  1.1272000 #> 468    0   1   0  19.09 43.20900 -24.1190000 -0.9170000 #> 469    0   1   0  26.46 44.50400 -18.0440000 -0.4975300 #> 470    0   1   0  24.47 37.73400 -13.2640000 -0.5911800 #> 471    0   1   0  20.43 30.51800 -10.0880000 -0.8948400 #> 472    0   1   0  24.08 24.35600  -0.2756200  0.8406500 #> 473    0   1   0  13.59 19.36200  -5.7719000 -1.6002000 #> 474    0   1   0  16.84 15.37400   1.4658000  0.4026500 #> 475    0   1   0  12.95 12.20400   0.7463500 -0.3675000 #> 476    0   1   0  10.71  7.68740   3.0226000  0.3477300 #> 477    0   1   0   9.75  4.84230   4.9077000  2.1237000 #> 479    0   1   0  65.72 43.20900  22.5110000  1.2341000 #> 480    0   1   0  42.13 44.50400  -2.3740000  0.0473630 #> 481    0   1   0  20.42 37.73400 -17.3140000 -1.0224000 #> 482    0   1   0  11.54 30.51800 -18.9780000 -0.8857400 #> 483    0   1   0   4.39 24.35600 -19.9660000 -1.2817000 #> 484    0   1   0   1.93 19.36200 -17.4320000 -1.0146000 #> 485    0   1   0   1.00 15.37400 -14.3740000 -0.5565500 #> 486    0   1   0   0.36 12.20400 -11.8440000 -0.1967700 #> 487    0   1   0   0.08  7.68740  -7.6074000  0.5830400 #> 488    0   1   0   0.02  4.84230  -4.8223000  1.1530000 #> 490    1   1   0  42.61 43.20900  -0.5988900  0.0577150 #> 491    1   1   0  47.68 44.50400   3.1760000 -0.1116400 #> 492    1   1   0  47.65 37.73400   9.9161000  0.4419800 #> 493    1   1   0  44.96 30.51800  14.4420000  1.0462000 #> 494    1   1   0  36.56 24.35600  12.2040000  0.2868600 #> 495    1   1   0  32.37 19.36200  13.0080000  0.4651900 #> 496    1   1   0  19.77 12.20400   7.5663000 -1.6853000 #> 497    1   1   0  17.05  7.68740   9.3626000 -0.0898180 #> 498    1   1   0  16.78  4.84230  11.9380000  4.0157000 #> 500    0   1   0  27.81 43.20900 -15.3990000 -0.7768300 #> 501    0   1   0  44.56 44.50400   0.0560410  1.0472000 #> 502    0   1   0  27.71 37.73400 -10.0240000 -1.6992000 #> 503    0   1   0  35.69 30.51800   5.1720000  0.5004700 #> 504    0   1   0  32.64 24.35600   8.2844000  0.6479400 #> 505    0   1   0  20.07 19.36200   0.7081300 -2.4367000 #> 506    0   1   0  26.51 15.37400  11.1360000  0.9104500 #> 507    0   1   0  24.71 12.20400  12.5060000  1.5632000 #> 508    0   1   0  18.05  7.68740  10.3630000  1.0647000 #> 509    0   1   0  13.82  4.84230   8.9777000  1.3942000 #> 511    0   1   0  14.36  8.64180   5.7182000  0.8826800 #> 512    0   1   0  14.72  8.90080   5.8192000  1.9432000 #> 513    0   1   0   9.02  7.54680   1.4732000 -0.7363200 #> 514    0   1   0   7.60  6.10360   1.4964000  0.0942140 #> 515    0   1   0   6.45  4.87110   1.5789000  1.0280000 #> 516    0   1   0   4.86  3.87240   0.9876300  0.9027700 #> 517    0   1   0   3.23  3.07480   0.1551500 -0.0544550 #> 518    0   1   0   2.11  2.44070  -0.3307300 -0.7851500 #> 519    0   1   0   1.16  1.53750  -0.3774800 -0.5854900 #> 520    0   1   0   0.70  0.96846  -0.2684600 -0.0266090 #> 522    0   1   0  15.00 43.20900 -28.2090000 -1.0821000 #> 523    0   1   0  20.61 44.50400 -23.8940000 -0.9548000 #> 524    0   1   0  18.40 37.73400 -19.3340000 -0.9368400 #> 525    0   1   0  16.00 30.51800 -14.5180000 -0.5804400 #> 526    0   1   0  15.30 24.35600  -9.0556000  0.3365000 #> 527    0   1   0   9.47 19.36200  -9.8919000 -0.3709900 #> 528    0   1   0   7.66 15.37400  -7.7142000 -0.0645240 #> 529    0   1   0   4.37 12.20400  -7.8337000 -0.6603100 #> 530    0   1   0   2.90  7.68740  -4.7874000 -0.0700970 #> 531    0   1   0   0.91  4.84230  -3.9323000 -0.5516600 #> 533    0   1   0  98.31 86.41800  11.8920000  0.2379000 #> 534    0   1   0  80.18 89.00800  -8.8279000  0.0857670 #> 535    0   1   0  54.06 75.46800 -21.4080000 -0.0943810 #> 536    0   1   0  27.03 61.03600 -34.0060000 -1.1052000 #> 537    0   1   0  14.27 48.71100 -34.4410000 -1.3095000 #> 538    0   1   0  12.56 38.72400 -26.1640000 -0.3468900 #> 539    0   1   0   4.39 30.74800 -26.3580000 -0.8313800 #> 540    0   1   0   2.05 19.37200 -17.3220000  0.1240400 #> 541    0   1   0   0.65 12.20200 -11.5520000  0.7394800 #> 543    0   1   0  14.96  8.64180   6.3182000  1.6118000 #> 544    0   1   0   8.72  6.10360   2.6164000 -0.0301420 #> 545    0   1   0   8.66  4.87110   3.7889000  1.8568000 #> 546    0   1   0   4.38  2.44070   1.9393000  0.0263380 #> 547    0   1   0   3.03  1.53750   1.4925000 -0.1252700 #> 548    0   1   0   2.43  0.96846   1.4615000  1.3828000 #> 550    0   1   0   5.99 43.20900 -37.2190000 -1.2801000 #> 551    0   1   0  13.26 44.50400 -31.2440000 -1.0617000 #> 552    0   1   0  13.19 37.73400 -24.5440000 -1.3239000 #> 553    0   1   0  14.15 30.51800 -16.3680000 -1.0914000 #> 554    0   1   0  16.30 24.35600  -8.0556000 -0.2757500 #> 555    0   1   0  14.39 19.36200  -4.9719000 -0.4439900 #> 556    0   1   0  12.63 15.37400  -2.7442000 -0.6194200 #> 557    0   1   0  14.82 12.20400   2.6163000  1.0388000 #> 558    0   1   0  13.44  9.68590   3.7541000  1.2499000 #> 559    0   1   0  10.27  7.68740   2.5826000  0.2346500 #> 561    0   1   0  25.91 30.24600  -4.3362000 -0.3109500 #> 562    0   1   0  13.00 21.36300  -8.3626000 -0.4001300 #> 563    0   1   0   8.31 17.04900  -8.7389000 -0.7156300 #> 564    0   1   0   4.54 13.55300  -9.0133000 -1.2258000 #> 565    0   1   0   4.87 10.76200  -5.8920000 -0.0065673 #> 566    0   1   0   2.93  8.54260  -5.6126000 -0.2676700 #> 567    0   1   0   1.90  5.38120  -3.4812000  0.4590200 #> 569    0   1   0  36.19 43.20900  -7.0189000  0.0874200 #> 570    0   1   0  40.04 44.50400  -4.4640000 -0.4862900 #> 571    0   1   0  35.84 37.73400  -1.8939000 -1.2469000 #> 572    0   1   0  48.96 30.51800  18.4420000  2.1969000 #> 573    0   1   0  30.71 19.36200  11.3480000 -0.6482500 #> 574    0   1   0  30.07 15.37400  14.6960000  0.3818100 #> 575    0   1   0  29.57 12.20400  17.3660000  1.8318000 #> 576    0   1   0  15.60  4.84230  10.7580000  0.8741700 #> 578    0   1   0  73.05 43.20900  29.8410000  1.6030000 #> 579    0   1   0  51.52 44.50400   7.0160000 -0.1923400 #> 580    0   1   0  39.96 37.73400   2.2261000  0.2353700 #> 581    0   1   0  23.43 30.51800  -7.0880000 -1.1412000 #> 582    0   1   0  22.59 24.35600  -1.7656000  0.4611200 #> 583    0   1   0  14.64 19.36200  -4.7219000 -0.3316100 #> 584    0   1   0  12.32 15.37400  -3.0542000  0.2655800 #> 585    0   1   0   9.58 12.20400  -2.6237000  0.4182900 #> 586    0   1   0   4.23  7.68740  -3.4574000 -0.4033600 #> 587    0   1   0   2.48  4.84230  -2.3623000 -0.1445200 #> 589    0   1   0  10.61  8.64180   1.9682000  0.5627700 #> 590    0   1   0   8.08  8.90080  -0.8207900 -0.2251000 #> 591    0   1   0   3.32  6.10360  -2.7836000 -1.0066000 #> 592    0   1   0   2.72  4.87110  -2.1511000 -0.2507500 #> 593    0   1   0   1.49  3.87240  -2.3824000 -0.8504700 #> 594    0   1   0   1.26  3.07480  -1.8148000 -0.1793900 #> 595    0   1   0   0.89  2.44070  -1.5507000 -0.0099798 #> 596    0   1   0   0.49  1.93720  -1.4472000 -0.2413300 #> 597    0   1   0   0.38  1.53750  -1.1575000  0.0962790 #> 598    0   1   0   0.25  1.22020  -0.9702400  0.2072600 #> 600    0   1   0  50.88 43.20900   7.6711000  0.4675800 #> 601    0   1   0  50.86 44.50400   6.3560000  0.2393600 #> 602    0   1   0  41.68 37.73400   3.9461000 -0.3567800 #> 603    0   1   0  38.77 30.51800   8.2520000  0.3178200 #> 604    0   1   0  35.61 24.35600  11.2540000  1.0120000 #> 605    0   1   0  24.90 19.36200   5.5381000 -0.8488200 #> 606    0   1   0  24.82 15.37400   9.4458000  0.5769800 #> 607    0   1   0  20.89 12.20400   8.6863000  0.4577500 #> 608    0   1   0  15.97  7.68740   8.2826000  1.0815000 #> 610    0   1   0  39.62 30.24600   9.3738000  0.7999400 #> 611    0   1   0  34.31 31.15300   3.1572000 -0.3034300 #> 612    0   1   0  25.90 26.41400  -0.5137300 -0.9329500 #> 613    0   1   0  29.15 21.36300   7.7874000  2.0214000 #> 614    0   1   0  18.82 17.04900   1.7711000  0.3739400 #> 615    0   1   0  10.61 10.76200  -0.1519600  0.0408840 #> 616    0   1   0   7.07  8.54260  -1.4726000 -0.6580500 #> 617    0   1   0   4.50  5.38120  -0.8812000 -0.0747730 #> 618    0   1   0   2.73  4.27080  -1.5408000 -0.7953800 #> 620    0   1   0  26.15 43.20900 -17.0590000 -0.3798200 #> 621    0   1   0  27.49 44.50400 -17.0140000 -1.1552000 #> 622    0   1   0  27.17 37.73400 -10.5640000 -0.9538400 #> 623    0   1   0  30.42 30.51800  -0.0980230  0.6314000 #> 624    0   1   0  24.82 24.35600   0.4643800  0.4346800 #> 625    0   1   0  20.71 19.36200   1.3481000  0.4559700 #> 626    0   1   0  10.35 12.20400  -1.8537000 -1.3981000 #> 627    0   1   0  14.18  9.68590   4.4941000  1.8678000 #> 628    0   1   0   7.30  6.10120   1.1988000 -0.0470870 #> 629    0   1   0   4.51  4.84230  -0.3322800 -1.4289000 #> 631    0   1   0  19.94 43.20900 -23.2690000 -1.0476000 #> 632    0   1   0  33.94 44.50400 -10.5640000  0.4024500 #> 633    0   1   0  30.16 37.73400  -7.5739000 -0.1806400 #> 634    0   1   0  23.47 30.51800  -7.0480000 -1.4358000 #> 635    0   1   0  27.24 24.35600   2.8844000 -0.1906500 #> 636    0   1   0  25.67 19.36200   6.3081000 -0.1221800 #> 637    0   1   0  21.15 15.37400   5.7758000 -1.1615000 #> 638    0   1   0  20.40  9.68590  10.7140000  0.3550800 #> 639    0   1   0  18.13  7.68740  10.4430000  0.2946700 #> 640    0   1   0  20.18  4.84230  15.3380000  5.9987000 str(simpraz.xpdb) #> Formal class 'xpose.data' [package \".GlobalEnv\"] with 8 slots #>   ..@ Data           :'data.frame':\t640 obs. of  26 variables: #>   .. ..$ ID   : num [1:640] 1 1 1 1 1 1 1 1 1 1 ... #>   .. ..$ TIME : num [1:640] 0 1 2 3 4 5 6 7 9 11 ... #>   .. ..$ IPRED: num [1:640] 0 69.2 80.2 75.3 66.9 ... #>   .. ..$ IWRES: num [1:640] 0 0.0368 -0.0944 0.1683 -0.206 ... #>   .. ..$ CWRES: num [1:640] 0 -0.0646 -0.9411 1.1911 -1.5154 ... #>   .. ..$ CL   : num [1:640] 13.6 13.6 13.6 13.6 13.6 ... #>   .. ..$ V    : num [1:640] 93.6 93.6 93.6 93.6 93.6 ... #>   .. ..$ KA   : num [1:640] 1.22 1.22 1.22 1.22 1.22 ... #>   .. ..$ ETA1 : num [1:640] -0.268 -0.268 -0.268 -0.268 -0.268 ... #>   .. ..$ ETA2 : num [1:640] 0.198 0.198 0.198 0.198 0.198 ... #>   .. ..$ ETA3 : num [1:640] -0.164 -0.164 -0.164 -0.164 -0.164 ... #>   .. ..$ AGE  : num [1:640] 55 55 55 55 55 55 55 55 55 55 ... #>   .. ..$ HT   : num [1:640] 154 154 154 154 154 154 154 154 154 154 ... #>   .. ..$ WT   : num [1:640] 81 81 81 81 81 ... #>   .. ..$ SECR : num [1:640] 1 1 1 1 1 1 1 1 1 1 ... #>   .. ..$ SEX  : Factor w/ 2 levels \"1\",\"2\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ RACE : Factor w/ 3 levels \"1\",\"2\",\"3\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ SMOK : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ... #>   .. ..$ HCTZ : Factor w/ 2 levels \"0\",\"1\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ PROP : Factor w/ 2 levels \"0\",\"1\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ CON  : Factor w/ 2 levels \"0\",\"1\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ OCC  : Factor w/ 1 level \"0\": 1 1 1 1 1 1 1 1 1 1 ... #>   .. ..$ DV   : num [1:640] 0 71.7 72.6 88 53.1 ... #>   .. ..$ PRED : num [1:640] 0 86.4 89 75.5 61 ... #>   .. ..$ RES  : num [1:640] 0 -14.68 -16.4 12.54 -7.91 ... #>   .. ..$ WRES : num [1:640] 0 -0.105 -0.759 1.208 -1.539 ... #>   ..@ SData          : NULL #>   ..@ Data.firstonly :'data.frame':\t64 obs. of  12 variables: #>   .. ..$ SUBJECT_NO: int [1:64] 1 2 3 4 5 6 7 8 9 10 ... #>   .. ..$ ID        : int [1:64] 1 2 3 4 5 6 7 8 9 10 ... #>   .. ..$ ETA.1.    : num [1:64] -0.2677 -0.7097 -0.4762 0.0996 -0.3529 ... #>   .. ..$ ETA.2.    : num [1:64] 0.198 0.186 0.202 -0.429 0.098 ... #>   .. ..$ ETA.3.    : num [1:64] -0.164 0.737 0.436 0.151 0.524 ... #>   .. ..$ ETC.1.1.  : num [1:64] 0.00412 0.00646 0.00401 0.00321 0.00328 ... #>   .. ..$ ETC.2.1.  : num [1:64] -0.002413 -0.002923 -0.001677 0.003449 -0.000594 ... #>   .. ..$ ETC.2.2.  : num [1:64] 0.00971 0.00622 0.00661 0.00605 0.00691 ... #>   .. ..$ ETC.3.1.  : num [1:64] -0.00947 -0.01828 -0.01274 -0.00658 -0.01295 ... #>   .. ..$ ETC.3.2.  : num [1:64] 0.01757 0.00998 0.01247 0.00237 0.01117 ... #>   .. ..$ ETC.3.3.  : num [1:64] 0.0821 0.3497 0.1956 0.0754 0.2295 ... #>   .. ..$ OBJ       : num [1:64] 54.04 60.73 9.13 27.62 25.53 ... #>   ..@ SData.firstonly: NULL #>   ..@ Runno          : num 1 #>   ..@ Nsim           : NULL #>   ..@ Doc            : NULL #>   ..@ Prefs          :Formal class 'xpose.prefs' [package \".GlobalEnv\"] with 9 slots #>   .. .. ..@ Xvardef      :List of 14 #>   .. .. .. ..$ id        : chr \"ID\" #>   .. .. .. ..$ idlab     : chr \"ID\" #>   .. .. .. ..$ idv       : chr \"TIME\" #>   .. .. .. ..$ occ       : chr \"OCC\" #>   .. .. .. ..$ dv        : chr \"DV\" #>   .. .. .. ..$ pred      : chr \"PRED\" #>   .. .. .. ..$ ipred     : chr \"IPRED\" #>   .. .. .. ..$ iwres     : chr \"IWRES\" #>   .. .. .. ..$ wres      : chr \"WRES\" #>   .. .. .. ..$ cwres     : chr \"CWRES\" #>   .. .. .. ..$ res       : chr \"RES\" #>   .. .. .. ..$ parms     : chr [1:6] \"ETA3\" \"ETA2\" \"ETA1\" \"KA\" ... #>   .. .. .. ..$ covariates: chr [1:11] \"SEX\" \"RACE\" \"SMOK\" \"HCTZ\" ... #>   .. .. .. ..$ ranpar    : chr [1:3] \"ETA1\" \"ETA2\" \"ETA3\" #>   .. .. ..@ Labels       :List of 29 #>   .. .. .. ..$ OCC  : chr \"Occasion\" #>   .. .. .. ..$ TIME : chr \"Time\" #>   .. .. .. ..$ PRED : chr \"Population predictions\" #>   .. .. .. ..$ IPRED: chr \"Individual predictions\" #>   .. .. .. ..$ IPRE : chr \"Individual predictions\" #>   .. .. .. ..$ WRES : chr \"Weighted residuals\" #>   .. .. .. ..$ CWRES: chr \"Conditional weighted residuals\" #>   .. .. .. ..$ IWRES: chr \"Individual weighted residuals\" #>   .. .. .. ..$ IWRE : chr \"Individual weighted residuals\" #>   .. .. .. ..$ DV   : chr \"Observations\" #>   .. .. .. ..$ RES  : chr \"Residuals\" #>   .. .. .. ..$ CL   : chr \"Clearance\" #>   .. .. .. ..$ V    : chr \"Volume\" #>   .. .. .. ..$ TAD  : chr \"Time after dose\" #>   .. .. .. ..$ ID   : chr \"ID\" #>   .. .. .. ..$ KA   : chr \"KA\" #>   .. .. .. ..$ ETA1 : chr \"ETA1\" #>   .. .. .. ..$ ETA2 : chr \"ETA2\" #>   .. .. .. ..$ ETA3 : chr \"ETA3\" #>   .. .. .. ..$ AGE  : chr \"AGE\" #>   .. .. .. ..$ HT   : chr \"HT\" #>   .. .. .. ..$ WT   : chr \"WT\" #>   .. .. .. ..$ SECR : chr \"SECR\" #>   .. .. .. ..$ SEX  : chr \"SEX\" #>   .. .. .. ..$ RACE : chr \"RACE\" #>   .. .. .. ..$ SMOK : chr \"SMOK\" #>   .. .. .. ..$ HCTZ : chr \"HCTZ\" #>   .. .. .. ..$ PROP : chr \"PROP\" #>   .. .. .. ..$ CON  : chr \"CON\" #>   .. .. ..@ Graph.prefs  :List of 102 #>   .. .. .. ..$ type     : chr \"b\" #>   .. .. .. ..$ pch      : num 1 #>   .. .. .. ..$ cex      : num 0.8 #>   .. .. .. ..$ lty      : num 1 #>   .. .. .. ..$ lwd      : num 1 #>   .. .. .. ..$ col      : num 4 #>   .. .. .. ..$ fill     : chr \"lightblue\" #>   .. .. .. ..$ grid     : logi FALSE #>   .. .. .. ..$ aspect   : chr \"fill\" #>   .. .. .. ..$ condvar  : NULL #>   .. .. .. ..$ byordfun : chr \"median\" #>   .. .. .. ..$ ordby    : NULL #>   .. .. .. ..$ shingnum : num 6 #>   .. .. .. ..$ shingol  : num 0.5 #>   .. .. .. ..$ abline   : NULL #>   .. .. .. ..$ abllwd   : num 1 #>   .. .. .. ..$ ablcol   : num 1 #>   .. .. .. ..$ abllty   : num 1 #>   .. .. .. ..$ smlwd    : num 2 #>   .. .. .. ..$ smcol    : num 2 #>   .. .. .. ..$ smlty    : num 1 #>   .. .. .. ..$ smspan   : num 0.667 #>   .. .. .. ..$ smdegr   : num 1 #>   .. .. .. ..$ lmline   : NULL #>   .. .. .. ..$ lmlwd    : num 2 #>   .. .. .. ..$ lmcol    : num 2 #>   .. .. .. ..$ lmlty    : num 1 #>   .. .. .. ..$ suline   : NULL #>   .. .. .. ..$ sulwd    : num 2 #>   .. .. .. ..$ sucol    : num 3 #>   .. .. .. ..$ sulty    : num 1 #>   .. .. .. ..$ suspan   : num 0.667 #>   .. .. .. ..$ sudegr   : num 1 #>   .. .. .. ..$ ids      : logi FALSE #>   .. .. .. ..$ idsmode  : NULL #>   .. .. .. ..$ idsext   : num 0.05 #>   .. .. .. ..$ idscex   : num 0.7 #>   .. .. .. ..$ idsdir   : chr \"both\" #>   .. .. .. ..$ dilfrac  : num 0.7 #>   .. .. .. ..$ diltype  : NULL #>   .. .. .. ..$ dilci    : num 0.95 #>   .. .. .. ..$ PIuplty  : num 2 #>   .. .. .. ..$ PIdolty  : num 2 #>   .. .. .. ..$ PImelty  : num 1 #>   .. .. .. ..$ PIuptyp  : chr \"l\" #>   .. .. .. ..$ PIdotyp  : chr \"l\" #>   .. .. .. ..$ PImetyp  : chr \"l\" #>   .. .. .. ..$ PIupcol  : chr \"black\" #>   .. .. .. ..$ PIdocol  : chr \"black\" #>   .. .. .. ..$ PImecol  : chr \"black\" #>   .. .. .. ..$ PIuplwd  : num 2 #>   .. .. .. ..$ PIdolwd  : num 2 #>   .. .. .. ..$ PImelwd  : num 2 #>   .. .. .. ..$ PIupltyR : num 1 #>   .. .. .. ..$ PIdoltyR : num 1 #>   .. .. .. ..$ PImeltyR : num 2 #>   .. .. .. ..$ PIuptypR : chr \"l\" #>   .. .. .. ..$ PIdotypR : chr \"l\" #>   .. .. .. ..$ PImetypR : chr \"l\" #>   .. .. .. ..$ PIupcolR : chr \"blue\" #>   .. .. .. ..$ PIdocolR : chr \"blue\" #>   .. .. .. ..$ PImecolR : chr \"blue\" #>   .. .. .. ..$ PIuplwdR : num 2 #>   .. .. .. ..$ PIdolwdR : num 2 #>   .. .. .. ..$ PImelwdR : num 2 #>   .. .. .. ..$ PIupltyM : num 1 #>   .. .. .. ..$ PIdoltyM : num 1 #>   .. .. .. ..$ PImeltyM : num 2 #>   .. .. .. ..$ PIuptypM : chr \"l\" #>   .. .. .. ..$ PIdotypM : chr \"l\" #>   .. .. .. ..$ PImetypM : chr \"l\" #>   .. .. .. ..$ PIupcolM : chr \"darkgreen\" #>   .. .. .. ..$ PIdocolM : chr \"darkgreen\" #>   .. .. .. ..$ PImecolM : chr \"darkgreen\" #>   .. .. .. ..$ PIuplwdM : num 0.5 #>   .. .. .. ..$ PIdolwdM : num 0.5 #>   .. .. .. ..$ PImelwdM : num 0.5 #>   .. .. .. ..$ PIarcol  : chr \"lightgreen\" #>   .. .. .. ..$ PIlimits : num [1:2] 0.025 0.975 #>   .. .. .. ..$ bwhoriz  : logi FALSE #>   .. .. .. ..$ bwratio  : num 1.5 #>   .. .. .. ..$ bwvarwid : logi FALSE #>   .. .. .. ..$ bwdotpch : num 16 #>   .. .. .. ..$ bwdotcol : chr \"black\" #>   .. .. .. ..$ bwdotcex : num 1 #>   .. .. .. ..$ bwreccol : chr \"blue\" #>   .. .. .. ..$ bwrecfill: chr \"transparent\" #>   .. .. .. ..$ bwreclty : num 1 #>   .. .. .. ..$ bwreclwd : num 1 #>   .. .. .. ..$ bwumbcol : chr \"blue\" #>   .. .. .. ..$ bwumblty : num 1 #>   .. .. .. ..$ bwumblwd : num 1 #>   .. .. .. ..$ bwoutcol : chr \"blue\" #>   .. .. .. ..$ bwoutcex : num 0.8 #>   .. .. .. ..$ bwoutpch : num 1 #>   .. .. .. ..$ hicol    : num 5 #>   .. .. .. ..$ hiborder : chr \"black\" #>   .. .. .. ..$ hilty    : num 1 #>   .. .. .. ..$ hilwd    : num 1 #>   .. .. .. .. [list output truncated] #>   .. .. ..@ Miss         : num -99 #>   .. .. ..@ Cat.levels   : num 4 #>   .. .. ..@ DV.Cat.levels: num 7 #>   .. .. ..@ Subset       : NULL #>   .. .. ..@ Gam.prefs    :List of 21 #>   .. .. .. ..$ onlyfirst : logi TRUE #>   .. .. .. ..$ wts       : logi FALSE #>   .. .. .. ..$ start.mod : NULL #>   .. .. .. ..$ steppit   : logi TRUE #>   .. .. .. ..$ disp      : NULL #>   .. .. .. ..$ nmods     : num 3 #>   .. .. .. ..$ smoother1 : num 0 #>   .. .. .. ..$ smoother2 : num 1 #>   .. .. .. ..$ smoother3 : chr \"ns\" #>   .. .. .. ..$ smoother4 : chr \"ns\" #>   .. .. .. ..$ arg1      : NULL #>   .. .. .. ..$ arg2      : NULL #>   .. .. .. ..$ arg3      : chr \"df=2\" #>   .. .. .. ..$ arg4      : chr \"df=3\" #>   .. .. .. ..$ excl1     : NULL #>   .. .. .. ..$ excl2     : NULL #>   .. .. .. ..$ excl3     : NULL #>   .. .. .. ..$ excl4     : NULL #>   .. .. .. ..$ extra     : NULL #>   .. .. .. ..$ plot.ids  : logi TRUE #>   .. .. .. ..$ medianNorm: logi TRUE #>   .. .. ..@ Bootgam.prefs:List of 10 #>   .. .. .. ..$ n             : num 100 #>   .. .. .. ..$ algo          : chr \"fluct.ratio\" #>   .. .. .. ..$ conv.value    : num 1.04 #>   .. .. .. ..$ check.interval: num 20 #>   .. .. .. ..$ start.check   : num 50 #>   .. .. .. ..$ liif          : num 0.2 #>   .. .. .. ..$ ljif.conv     : num 25 #>   .. .. .. ..$ seed          : NULL #>   .. .. .. ..$ start.mod     : NULL #>   .. .. .. ..$ excluded.ids  : NULL"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to create files for the simulated prazosin example in Xpose — simprazExample","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"Creates NONMEM data, model output files model prazosin using simulated data.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"","code":"simprazExample(overwrite = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"overwrite Logical. function overwrite files names already current working directory?","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"Creates files current working directory named: run1.ext run1.lst run1.mod simpraz.dta xptab1","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"Niclas Jonsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"","code":"od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-b4e0a141bd7a6d87d4e27f8e112db7d2\" #> [2] \"downlit\"                                #> [3] \"file1775638e0fef\"                        simprazExample(overwrite=TRUE) # write files  (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"run1.ext\"    \"run1.lst\"    \"run1.mod\"    \"simpraz.dta\" \"xptab1\"       file.remove(new.files) # remove these files #> [1] TRUE TRUE TRUE TRUE TRUE setwd(od)  # restore working directory"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate the population parameter estimates — tabulate.parameters","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"function provides summary model's parameter estimates precision.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"","code":"tabulate.parameters(object, prompt = FALSE, outfile = NULL, dir = \"\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"object xpose.data object. prompt Ask printing. outfile file output (NULL means screen). dir directory NONMEM output file located. \"\" means current working directory getwd().","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"table summarizing parameters precision.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"Niclas Jonsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"","code":"od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-b4e0a141bd7a6d87d4e27f8e112db7d2\" #> [2] \"downlit\"                                #> [3] \"file1775638e0fef\"                        simprazExample(overwrite=TRUE) # write files (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"run1.ext\"    \"run1.lst\"    \"run1.mod\"    \"simpraz.dta\" \"xptab1\"      xpdb <- xpose.data(1) # read in files to xpose database #>  #> Looking for NONMEM table files. #>     Reading ./xptab1  #> Table files read. #>  #> Looking for NONMEM simulation table files. #> No simulated table files read. #>   tabulate.parameters(xpdb) #> +---------+-----+-----+ #> |Parameter|Value| RSE | #> +---------+-----+-----+ #> |    TH1  | 17.7|0.059| #> +---------+-----+-----+ #> |    TH2  | 76.8|0.052| #> +---------+-----+-----+ #> |    TH3  |  1.4|0.108| #> +---------+-----+-----+ #> |  OM1:1  | 0.45| 0.13| #> +---------+-----+-----+ #> |  OM2:2  | 0.37| 0.19| #> +---------+-----+-----+ #> |  OM3:3  | 0.77| 0.29| #> +---------+-----+-----+ #> |  SI1:1  | 0.13|0.065| #> +---------+-----+-----+  file.remove(new.files) # remove these files #> [1] TRUE TRUE TRUE TRUE TRUE setwd(od)  # restore working directory"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"histogram distribution weighted residuals (WRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.histogram function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"","code":"wres.dist.hist(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"object xpose.data object. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"Returns histogram weighted residuals (WRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"Displays histogram weighted residuals (WRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.dist.hist(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"QQ plot distribution weighted residuals (WRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.qq function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"","code":"wres.dist.qq(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"object xpose.data object. ... arguments passed link{xpose.plot.qq}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"Returns QQ plot weighted residuals (WRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"Displays QQ plot weighted residuals (WRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.dist.qq(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"creates stack plots weighted residuals (WRES) plotted covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default xpose.plot.histogram functions. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"","code":"wres.vs.cov(   object,   ylb = \"WRES\",   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"object xpose.data object. ylb string giving label y-axis. NULL none. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default} link{xpose.plot.histogram}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"Returns stack xyplots histograms CWRES versus covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"Weighted residuals (WRES) plotted covariate present, specified object@Prefs@Xvardef$covariates, creating stack plots. wide array extra options controlling xyplots histograms available. See xpose.plot.default xpose.plot.histogram details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot wres.vs.cov(xpdb)  ## Custom colours and symbols, IDs wres.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"creates box whisker plot weighted residuals (WRES) vs independent variable (IDV), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"","code":"wres.vs.idv.bw(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"object xpose.data object. ... arguments passed link{xpose.plot.bw}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"Returns stack box--whisker plots WRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"creates box whisker plot weighted residuals (WRES) vs independent variable (IDV), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments. wide array extra options controlling bwplots available. See xpose.plot.bw xpose.panel.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.vs.idv.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"plot population weighted residuals (WRES) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"","code":"wres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"Returns xyplot WRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"Weighted residuals (WRES) plotted independent variable, specified object@Prefs@Xvardef$idv. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.vs.idv(xpdb)   ## A conditioning plot wres.vs.idv(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"creates box whisker plot weighted residuals (WRES) vs population predictions (PRED), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"","code":"wres.vs.pred.bw(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"object xpose.data object. ... arguments passed link{xpose.plot.bw}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"Returns box--whisker plot WRES vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"creates box whisker plot weighted residuals (WRES) vs population predictions (PRED), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments. wide array extra options controlling bwplots available. See xpose.plot.bw xpose.panel.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.vs.pred.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"plot population weighted residuals (WRES) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"","code":"wres.vs.pred(object, smooth = TRUE, abline = c(0, 0), ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"object xpose.data object. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. abline Vector arguments panel.abline function. abline drawn NULL. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"Returns xyplot WRES vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.vs.pred(xpdb)   ## A conditioning plot wres.vs.pred(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract and set labels for Xpose data items. — xlabel","title":"Extract and set labels for Xpose data items. — xlabel","text":"function extracts sets label definitions Xpose data objects.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract and set labels for Xpose data items. — xlabel","text":"","code":"xlabel(x, object)  xlabel(object) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract and set labels for Xpose data items. — xlabel","text":"x Name variable assign label . object xpose.data object. value two element vector first element name variable second label","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract and set labels for Xpose data items. — xlabel","text":"label specified column.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract and set labels for Xpose data items. — xlabel","text":"x string exactly matching name column data.frame Data slot xpose.data object. name columns defined xpose variable definitions (see xpose.data) can extracted using xvardef function used xlabel function, e.g. xlabel(xvardef(\"dv\",object),object), give label dv variable.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract and set labels for Xpose data items. — xlabel","text":"xlabel(object) <- value: sets label definitions Xpose data objects.  assigned value two-element vector  first element name variable second label","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract and set labels for Xpose data items. — xlabel","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract and set labels for Xpose data items. — xlabel","text":"","code":"xpdb <- simpraz.xpdb  ## Display label for dependent variable in the Xpose data object xlabel(\"DV\", xpdb) #> [1] \"Observations\"  ## Set label for dependent variable xlabel(xpdb) <- c(\"DV\", \"Concentration (mg/L)\") xlabel(\"DV\", xpdb) # how has this chnaged? #> [1] \"Concentration (mg/L)\""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlations between covariate coefficients — xp.boot.par.est.corr","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"function creates plot showing correlations estimates covariate coefficients, obtained first step (univariate testing) scm performed bootscm.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"","code":"xp.boot.par.est.corr(   bootgam.obj = NULL,   sd.norm = TRUE,   by.cov.type = FALSE,   cov.plot = NULL,   ask.covs = FALSE,   dotpch = 19,   col = rgb(0.2, 0.2, 0.9, 0.75),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"bootgam.obj object created using bootscm.import(), hold data plotting. sd.norm Perform normalization covariate coefficients (default TRUE). TRUE, estimated covariate coefficients multiplied standard deviation specific covariate (continuous categorical covariates). .cov.type Split plot continuous dichotomous covariates. Default FALSE. cov.plot character vector lists covariates include plot. none specified (NULL), covariate coefficients included plot. ask.covs Ask user covariates include plot. Default FALSE. dotpch character used plotting. col colors used plotting. ... Additional plotting arguments may passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"value returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"","code":"if (FALSE) { xp.boot.par.est.corr(current.bootscm, sd.norm = TRUE,                           cov.plot = c(\"CLSEX\", \"VSEX\", \"CLWT\"))  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"function creates plot estimates covariate coefficients, obtained first step (univariate testing) scm performed bootscm. normalized standard deviation, plots can used compare strength covariate relationship. Coloring based covariate included final model (blue) included (red).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"","code":"xp.boot.par.est(   bootgam.obj = NULL,   sd.norm = TRUE,   by.cov.type = FALSE,   abs.values = FALSE,   show.data = TRUE,   show.means = TRUE,   show.bias = TRUE,   dotpch = c(1, 19),   labels = NULL,   pch.mean = \"|\",   xlab = NULL,   ylab = NULL,   col = c(rgb(0.8, 0.5, 0.5), rgb(0.2, 0.2, 0.7), rgb(0.2, 0.2, 0.7), rgb(0.6, 0.6, 0.6)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"bootgam.obj object created using bootscm.import(), hold data plotting. sd.norm Perform normalization covariate coefficients (default TRUE). TRUE, estimated covariate coefficients multiplied standard deviation specific covariate (continuous categorical covariates). .cov.type Split plot continuous dichotomous covariates. Default FALSE. abs.values Show covariate coefficient absolute values. Default FALSE. show.data Show actual covariate coefficients plot. Default TRUE. show.means Show means included covariates (blue) covariates (grey) plot. Default TRUE. show.bias Show estimated bias text plot. Default TRUE. dotpch character used plotting. labels Custom labels parameter-covariate relationships, (character vector) pch.mean character used plotting mean. xlab Custom x-axis label ylab Custom y-axis label col color scheme. ... Additional plotting arguments may passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"value returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"Optionally, estimated bias plotted graph (text). Bias also shown difference mean parameter estimates covariate included (blue diamond), opposed mean parameter estimates (grey diamond) Note: dichotomous covariates, default PsN implementation use common covariate value base, effect value, estimated theta. Xpose (bootscm.import) however recalculates estimated parameters, parametrization lowest value dichotomous covariate base (e.g. 0), estimated THETA denotes proportional change, covariate value (e.g. 1).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"","code":"xp.boot.par.est() #> boot.type bootgam.objData not available. Did you import the bootSCM data? #> NULL"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.daic.npar.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution of difference in AIC — xp.daic.npar.plot","title":"Distribution of difference in AIC — xp.daic.npar.plot","text":"Distribution difference AIC","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.daic.npar.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution of difference in AIC — xp.daic.npar.plot","text":"","code":"xp.daic.npar.plot(   bootscm.obj = NULL,   main = NULL,   xlb = \"Difference in AIC\",   ylb = \"Density\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.daic.npar.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution of difference in AIC — xp.daic.npar.plot","text":"bootscm.obj bootscm object. main title plot xlb x-label plot ylb y-label plot ... Additional parameters passed panel.xyplot xyplot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.daic.npar.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution of difference in AIC — xp.daic.npar.plot","text":"lattice plot object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"function creates kernel smoothed plot number covariates included final model gam/scm bootgam/bootscm procedure.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"","code":"xp.distr.mod.size(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   bw = 0.5,   xlb = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main Plot title. bw smoothing bandwidth used kernel. xlb x-axis label. ... Additional plotting parameter may passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.npar.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution of difference in OFV — xp.dofv.npar.plot","title":"Distribution of difference in OFV — xp.dofv.npar.plot","text":"Distribution difference OFV","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.npar.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution of difference in OFV — xp.dofv.npar.plot","text":"","code":"xp.dofv.npar.plot(   bootscm.obj = NULL,   main = NULL,   xlb = \"Difference in OFV\",   ylb = \"Density\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.npar.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution of difference in OFV — xp.dofv.npar.plot","text":"bootscm.obj bootscm object. main title plot xlb x-label plot ylb y-label plot ... Additional parameters passed panel.xyplot xyplot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.npar.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution of difference in OFV — xp.dofv.npar.plot","text":"lattice plot object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"OFV difference (optimism) plot. — xp.dofv.plot","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"plot difference OFV final bootscm models reference final scm model.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"","code":"xp.dofv.plot(   bootscm.obj = NULL,   main = NULL,   xlb = \"Difference in OFV\",   ylb = \"Density\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"bootscm.obj bootgam bootscm object. main Plot title. xlb Label x-axis. ylb Label y-axis. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.get.disp.html","id":null,"dir":"Reference","previous_headings":"","what":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","title":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","text":"Default function calculating dispersion xpose.gam.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.get.disp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","text":"","code":"xp.get.disp(gamdata, parnam, covnams, family = \"gaussian\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.get.disp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","text":"gamdata data used GAM parnam ONE (one) model parameter name. covnams Covariate names test parameter. family Assumption parameter distribution. ... Used pass arguments basic functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.get.disp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","text":"list including dispersion","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.cond.stab.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace plots for conditional indices — xp.inc.cond.stab.cov","title":"Trace plots for conditional indices — xp.inc.cond.stab.cov","text":"Trace plots conditional indices","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.cond.stab.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace plots for conditional indices — xp.inc.cond.stab.cov","text":"","code":"xp.inc.cond.stab.cov(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   xlb = \"Bootstrap replicate number\",   ylb = \"Conditional inclusion frequency\",   normalize = TRUE,   split.plots = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.cond.stab.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace plots for conditional indices — xp.inc.cond.stab.cov","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main title plot xlb x-label plot ylb y-label plot normalize one normalize? split.plots plots split? ... Additional parameters passed panel.xyplot xyplot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.cond.stab.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trace plots for conditional indices — xp.inc.cond.stab.cov","text":"lattice plot object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.ind.cond.stab.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","title":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","text":"Trace plots conditional indices rper replicate number","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.ind.cond.stab.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","text":"","code":"xp.inc.ind.cond.stab.cov(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   xlb = \"Bootstrap replicate number\",   ylb = \"Conditional inclusion frequency\",   limits = c(0.2, 0.8),   normalize = TRUE,   split.plots = FALSE,   start = 25,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.ind.cond.stab.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main title plot xlb x-label plot ylb y-label plot limits Limits inclusion index. normalize one normalize? split.plots plots split? start start. ... Arguments passed functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.ind.cond.stab.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","text":"lattice plot object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":null,"dir":"Reference","previous_headings":"","what":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"Plot inclusion frequency common 2-covariate combinations.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"","code":"xp.inc.prob.comb.2(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   col = \"#6495ED\",   xlb = NULL,   ylb = \"Covariate combination\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main Plot title col Color used plot. xlb Label x-axis. ylb Label y-axis. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":null,"dir":"Reference","previous_headings":"","what":"Inclusion frequency plot — xp.inc.prob","title":"Inclusion frequency plot — xp.inc.prob","text":"Plot inclusion frequencies covariates final models obtained bootgam bootscm. Covariates ordered inclusion frequency.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inclusion frequency plot — xp.inc.prob","text":"","code":"xp.inc.prob(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   col = \"#6495ED\",   xlb = NULL,   ylb = \"Covariate\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inclusion frequency plot — xp.inc.prob","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main Plot title col Color used plot. xlb Label x-axis. ylb Label y-axis. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inclusion frequency plot — xp.inc.prob","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inclusion frequency plot — xp.inc.prob","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"Inclusion stability plot plot inclusion frequency covariates vs bootgam/bootscm   iteration number. plot can used evaluate whether sufficient   iterations performed.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"","code":"xp.inc.stab.cov(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   normalize = TRUE,   split.plots = FALSE,   xlb = \"Bootstrap replicate number\",   ylb = \"Difference of estimate with final\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL,  means user asked make choice. main Plot title normalize plot normalized? split.plots plots split? xlb label x-axis. ylb label y-axis. ... Additional plotting parameters","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"lattice plot object returned.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"plot showing range inclusion indices individuals covariates. plot can used evaluate whether covariates influenced constituency bootstrapped dataset others.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"","code":"xp.incl.index.cov.comp(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   xlb = \"Individual inclusion index\",   ylb = \"ID\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main title plot. xlb label x-axis. ylb label y-axis. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of inclusion index of covariates. — xp.incl.index.cov","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"Covariate inclusion indices show correlation inclusion covariate final model bootgam bootscm.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"","code":"xp.incl.index.cov(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   xlb = \"Index\",   ylb = \"Covariate\",   add.ci = FALSE,   incl.range = NULL,   return_plot = TRUE,   results.tab = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main Plot title. xlb Label x-axis. ylb Label y-axis. add.ci Add confidence interval plotted data. incl.range Included range return_plot function return plot? results.tab Specify results table. ... Additional plotting information.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"lattice plot object returned.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual inclusion index — xp.incl.index.cov.ind","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"function generate plot individual inclusion indexes specific covariate, can used identify influential individuals inclusion covariate. index individual calculated observed number inclusions individual specific covariate included minus expected number inclusions (based total bootstrap inclusions), divided expected.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"","code":"xp.incl.index.cov.ind(   bootgam.obj = NULL,   boot.type = NULL,   cov.name = NULL,   main = NULL,   ylb = \"ID\",   xlb = \"Individual inclusion index\",   return_plot = TRUE,   results.tab = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. cov.name name covariate create plot. main title plot. ylb label x-axis. xlb label y-axis. return_plot plot object returned? results.tab Supply results table. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"lattice plot object returned.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.scope3.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","title":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","text":"Define scope gam.  Used default input scope argument  xpose.gam","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.scope3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","text":"","code":"xp.scope3(   object,   covnam = xvardef(\"covariates\", object),   nmods = 3,   smoother1 = 0,   arg1 = NULL,   smoother2 = 1,   arg2 = NULL,   smoother3 = \"ns\",   arg3 = \"df=2\",   smoother4 = \"ns\",   arg4 = \"df=3\",   excl1 = NULL,   excl2 = NULL,   excl3 = NULL,   excl4 = NULL,   extra = NULL,   subset = xsubset(object),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.scope3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","text":"object xpose.data object. covnam Covariate names test. nmods Number models examine. smoother1 Smoother model. arg1 Argument model 1. smoother2 Smoother model. arg2 Argument model 2. smoother3 Smoother model. arg3 Argument model 3. smoother4 Smoother model. arg4 Argument model 4. excl1 Covariate exclusion model 1. excl2 Covariate exclusion model 2. excl3 Covariate exclusion model 3. excl4 Covariate exclusion model 4. extra Extra exclusion criteria. subset Subset data. ... Used pass arguments basic functions.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.scope3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","text":"","code":"xp.scope3(simpraz.xpdb) #> $SEX #> ~1 + SEX #>  #>  #> $RACE #> ~1 + RACE #>  #>  #> $SMOK #> ~1 + SMOK #>  #>  #> $HCTZ #> ~1 + HCTZ #>  #>  #> $PROP #> ~1 + PROP #>  #>  #> $CON #> ~1 + CON #>  #>  #> $AGE #> ~1 + AGE + ns(AGE, df = 2) #>  #>  #> $HT #> ~1 + HT + ns(HT, df = 2) #>  #>  #> $WT #> ~1 + WT + ns(WT, df = 2) #>  #>  #> $SECR #> ~1 + SECR + ns(SECR, df = 2) #>  #>"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":null,"dir":"Reference","previous_headings":"","what":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"Xpose Visual Predictive Check (VPC) continuous Limit Quantification (BLQ ALQ) data.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"","code":"xpose.VPC.both(   vpc.info = \"vpc_results.csv\",   vpctab = dir(pattern = \"^vpctab\")[1],   object = NULL,   subset = NULL,   main = \"Default\",   main.sub = NULL,   inclZeroWRES = FALSE,   cont.logy = F,   hline = \"default\",   add.args.cont = list(),   add.args.cat = list(),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"vpc.info Name PSN file use.  File come VPC command PsN. vpctab Name vpctab file produced PsN. object Xpose data object. subset Subset data look . main Title plot. main.sub Used names plot using multiple plots. vector, e.g. c(\"title 1\",\"title 2\"). inclZeroWRES Include WRES=0 rows computations plots? cont.logy continuous plot y-axis log scale? hline Horizontal line marking limits quantification.  defined, must vector values. add.args.cont Additional arguments continuous plot. xpose.VPC. add.args.cat Additional arguments categorical plot. xpose.VPC.categorical. ... Additional arguments plots.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"","code":"if (FALSE) { library(xpose4)  ## move to the directory where results from PsN ## are found cur.dir <- getwd() setwd(paste(cur.dir,\"/vpc_cont_LLOQ/\",sep=\"\"))  xpose.VPC() xpose.VPC.categorical(censored=T)  xpose.VPC.both()  xpose.VPC.both(subset=\"DV>1.75\")  xpose.VPC.both(add.args.cont=list(ylim=c(0,80)))  xpose.VPC.both(add.args.cont = list(ylim = c(0.01, 80)), xlim = c(0,      40), add.args.cat = list(ylim = c(0, 0.4)), cont.logy = T)  xpose.VPC.both(cont.logy=T) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"Xpose visual predictive check categorical data (binary, ordered categorical count data).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"","code":"xpose.VPC.categorical(   vpc.info = \"vpc_results.csv\",   vpctab = dir(pattern = \"^vpctab\")[1],   object = NULL,   subset = NULL,   main = \"Default\",   main.sub = \"Default\",   main.sub.cex = 0.85,   real.col = 4,   real.lty = \"b\",   real.cex = 1,   real.lwd = 1,   median.line = FALSE,   median.col = \"darkgrey\",   median.lty = 1,   ci.lines = FALSE,   ci.col = \"blue\",   ci.lines.col = \"darkblue\",   ci.lines.lty = 3,   xlb = \"Default\",   ylb = \"Proportion of Total\",   force.x.continuous = FALSE,   level.to.plot = NULL,   max.plots.per.page = 1,   rug = TRUE,   rug.col = \"orange\",   censored = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"vpc.info Name PSN file use.  File come VPC command PsN. vpctab Name vpctab file produced PsN. object Xpose data object. subset Subset data look . main Title plot. main.sub Used names plot using multiple plots. vector, e.g. c(\"title 1\",\"title 2\"). main.sub.cex Size main.sub real.col Color real line. real.lty Real line type. real.cex Size real line. real.lwd Width real line. median.line Dray median line? median.col Color median line. median.lty median line type. ci.lines Lines marking confidence interval? ci.col Color CI area. ci.lines.col Color CI lines. ci.lines.lty Type CI lines. xlb X-axis label.  \"default\"\" passed directly xyplot. ylb Y-axis label. Passed directly xyplot. force.x.continuous x variable continuous. level..plot levels variable plot. Smallest level 1, largest number_of_levels.  example, 4 levels, largest level 4, smallest 1. max.plots.per.page number plots per page. rug markings plot showing intervals VPC ? rug.col Color rug. censored censored data?  Censored data can limit quantification. ... Additional information passed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"","code":"if (FALSE) { library(xpose4)  ## move to the directory where results from PsN ## are found cur.dir <- getwd() setwd(paste(cur.dir,\"/binary/vpc_36\",sep=\"\"))  xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4) xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4,by=\"DOSE\")  ## ordered categorical plots setwd(paste(cur.dir,\"/ordered_cat/vpc_45\",sep=\"\")) xpose.VPC.categorical()   ## count setwd(paste(cur.dir,\"/count/vpc65b\",sep=\"\")) xpose.VPC.categorical()  setwd(paste(cur.dir,\"/count/vpc65a\",sep=\"\")) xpose.VPC.categorical()  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":null,"dir":"Reference","previous_headings":"","what":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"Function used create VPC xpose using output  vpc command Pearl Speaks NONMEM (PsN).  function reads  output files created PsN creates plot data.  dependent  variable, independent variable conditioning variable automatically  determined PsN files.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"","code":"xpose.VPC(   vpc.info = \"vpc_results.csv\",   vpctab = dir(pattern = \"^vpctab\")[1],   object = NULL,   ids = FALSE,   type = \"p\",   by = NULL,   PI = NULL,   PI.ci = \"area\",   PI.ci.area.smooth = FALSE,   PI.real = TRUE,   subset = NULL,   main = \"Default\",   main.sub = NULL,   main.sub.cex = 0.85,   inclZeroWRES = FALSE,   force.x.continuous = FALSE,   funy = NULL,   logy = FALSE,   ylb = \"Default\",   verbose = FALSE,   PI.x.median = TRUE,   PI.rug = \"Default\",   PI.identify.outliers = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"vpc.info results file vpc command PsN.  example vpc_results.csv, file separate directory  ./vpc_dir1/vpc_results.csv. vpctab vpctab vpc command PsN.   example vpctab5, file separate directory  ./vpc_dir1/vpctab5.  Can NULL.  default looks  current working directory takes first file starts  vpctab finds.  Note default can result  wrong files read multiple vpctab files  directory. One object vpctab required.   present information vpctab -ride  xpose data object object (.e. values vpctab  replace matching values object\\@Data portion xpose data object). object xpose data object. Created xpose.data.  One object vpctab required.  present  information vpctab -ride xpose data  object object (.e. values vpctab replace  matching values object\\@Data portion xpose data  object). ids logical value indicating whether text ID labels used  plotting symbols (variable used symbols indicated  idlab xpose data variable). Can FALSE TRUE. type Character string describing way points plot  displayed. details, see plot. Use  type=\"n\" want observations plot. string vector strings name(s)  conditioning variables. example = c(\"SEX\",\"WT\").  function automatically determines conditioning variable PsN  input file specified vpc.info, command can control  separate plots created condition (=NULL),  conditioning plot created (=\"WT\" example).   vpc.info file conditioning variable must match  variable.  conditioning variable vpc.info  PI conditioned plot PI  entire data set (conditioning subset). PI Either \"lines\", \"area\" \"\" specifying whether prediction  intervals (lines, shaded area ) added plot.  NULL means prediction interval. PI.ci Plot confidence interval simulated data's percentiles bin (simulated data set compute percentiles bin, , percentiles simulated datasets compute 95% CI percentiles). Values can \"\", \"area\" \"lines\". CIs can used asses PI.real values model misspecification. Note observations per bin CIs approximate  percentiles bin approximate. example, 95th  percentile 4 data points always largest 4 data  points. PI.ci.area.smooth \"area\" PI.ci smoothed match \"lines\" argument? Allowed values TRUE/FALSE. \"area\" set default show bins used PI.ci computation.  smoothing, information lost , general, confidence intervals smaller reality. PI.real Plot percentiles real data various bins.  values can NULL TRUE.  Note bin actual  observations percentiles approximate.  example, 95th  percentile 4 data points always largest 4 data  points. subset string giving subset expression applied data plotting. See xsubset. main string giving plot title NULL none.  \"Default\" creates default title. main.sub Used names plot using multiple plots.  vector c(\"Group 1\",\"Group 2\") main.sub.cex size main.sub titles. inclZeroWRES Logical value indicating whether rows WRES=0  included plot. force.x.continuous Logical value indicating whether x-values converted continuous variables, even defined factors. funy String function apply Y data. example \"abs\" logy Logical value indicating whether y-axis  logarithmic, base 10. ylb Label y-axis verbose warning messages diagnostic information  passed screen? (TRUE FALSE) PI.x.median x-location percentile lines bin  marked median x-values? (TRUE FALSE) PI.rug markings plot showing binning intervals  VPC  (locations independent variable used VPC calculation binning used)? PI.identify.outliers outlying percentiles real data highlighted? (TRUE FALSE) ... arguments passed xpose.panel.default,  xpose.plot.default others. Please see functions descriptions can .","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"plot list plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"additional-arguments","dir":"Reference","previous_headings":"","what":"Additional arguments","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"additional arguments can control look    feel VPC.  See    xpose.panel.default potential options. Additional graphical elements available VPC plots.  PI.mirror = NULL, TRUE .INTEGER.VALUE Plot percentiles    one simulated data set bin. TRUE takes first mirror   vpc_results.csv .INTEGER.VALUE can 1, 2,    ...{} n n number mirror's output    vpc_results.csv file. PI.limits = c(0.025, 0.975) vector two    values describe limits prediction interval    displayed.  limits found vpc_results.csv    file. limits also used percentages PI.real,    PI.mirror PI.ci.  However, confidence interval    PI.ci always one defined vpc_results.csv file. Additional options control look feel PI.    See See grid.polygon plot    details.  PI.arcol color PI area PI..lty upper line type. can \"dotted\" \"dashed\", etc. PI..type upper type used plotting.  Defaults line. PI..col upper line color PI..lwd upper line width PI..lty lower line type. can \"dotted\" \"dashed\", etc. PI..type lower type used plotting. Defaults line. PI..col lower line color PI..lwd lower    line width PI.med.lty median line type. can \"dotted\"    \"dashed\", etc. PI.med.type median type used plotting.    Defaults line. PI.med.col median line color PI.med.lwd median line width Additional options control look feel    PI.ci. See See grid.polygon    plot details.  PI.ci..arcol color upper PI.ci. PI.ci.med.arcol color median PI.ci. PI.ci..arcol color lower PI.ci. PI.ci..lty upper line type. can \"dotted\" \"dashed\", etc. PI.ci..type upper type used plotting.  Defaults    line. PI.ci..col upper line color PI.ci..lwd    upper line width PI.ci..lty lower line type. can    \"dotted\" \"dashed\", etc. PI.ci..type lower type used   plotting.  Defaults line. PI.ci..col lower line    color PI.ci..lwd lower line width PI.ci.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.ci.med.type median type used plotting.  Defaults    line. PI.ci.med.col median line color PI.ci.med.lwd median line width PI.ci.area.smooth    \"area\" PI.ci smoothed match \"lines\" argument? Allowed    values TRUE/FALSE. \"area\" set default show bins   used PI.ci computation.  smoothing, information lost    , general, confidence intervals smaller    reality. Additional options control look feel    PI.real. See See grid.polygon    plot details.  PI.real..lty upper line type. can \"dotted\"    \"dashed\", etc. PI.real..type upper type used plotting.    Defaults line. PI.real..col upper line color PI.real..lwd upper line width PI.real..lty lower    line type. can \"dotted\" \"dashed\", etc. PI.real..type   lower type used plotting.  Defaults line. PI.real..col lower line color PI.real..lwd lower    line width PI.real.med.lty median line type. can \"dotted\"    \"dashed\", etc. PI.real.med.type median type used    plotting.  Defaults line. PI.real.med.col median line    color PI.real.med.lwd median line width Additional options control look feel    PI.mirror. See See plot    details.  PI.mirror..lty upper line type. can \"dotted\"    \"dashed\", etc. PI.mirror..type upper type used plotting.   Defaults line. PI.mirror..col upper line color PI.mirror..lwd upper line width PI.mirror..lty    lower line type. can \"dotted\" \"dashed\", etc. PI.mirror..type lower type used plotting.  Defaults    line. PI.mirror..col lower line color PI.mirror..lwd lower line width PI.mirror.med.lty    median line type. can \"dotted\" \"dashed\", etc. PI.mirror.med.type median type used plotting.  Defaults    line. PI.mirror.med.col median line color PI.mirror.med.lwd median line width","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"","code":"if (FALSE) { library(xpose4)  xpose.VPC()  ## to be more clear about which files should be read in vpc.file <- \"vpc_results.csv\" vpctab <- \"vpctab5\" xpose.VPC(vpc.info=vpc.file,vpctab=vpctab)  ## with lines and a shaded area for the prediction intervals xpose.VPC(vpc.file,vpctab=vpctab,PI=\"both\")  ## with the percentages of the real data xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T)  ## with mirrors (if supplied in 'vpc.file') xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.mirror=5)  ## with CIs xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci=\"area\") xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci=\"area\",PI=NULL)  ## stratification (if 'vpc.file' is stratified) cond.var <- \"WT\" xpose.VPC(vpc.file,vpctab=vpctab) xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var) xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI=\"both\",by=cond.var,type=\"n\")  ## with no data points in the plot xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var,PI.real=T,PI.ci=\"area\",PI=NULL,type=\"n\")  ## with different DV and IDV, just read in new files and plot vpc.file <- \"vpc_results.csv\" vpctab <- \"vpctab5\" cond.var <- \"WT\" xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI=\"both\",by=cond.var) xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI=\"both\")  ## to use an xpose data object instead of vpctab ## ## In this example ## we expect to find the required NONMEM run and table files for run ## 5 in the current working directory runnumber <- 5 xpdb <- xpose.data(runnumber) xpose.VPC(vpc.file,object=xpdb)  ## to read files in a directory different than the current working directory  vpc.file <- \"./vpc_strat_WT_4_mirror_5/vpc_results.csv\" vpctab <- \"./vpc_strat_WT_4_mirror_5/vpctab5\" xpose.VPC(vpc.info=vpc.file,vpctab=vpctab)  ## to rearrange order of factors in VPC plot xpdb@Data$SEX <- factor(xpdb@Data$SEX,levels=c(\"2\",\"1\")) xpose.VPC(by=\"SEX\",object=xpdb)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to ask the user for the name of a file — xpose.ask.for.filename","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"Asks user name file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"","code":"xpose.ask.for.filename(   object,   listfile = paste(\"run\", object@Runno, \".lst\", sep = \"\"),   modfile = paste(\"run\", object@Runno, \".mod\", sep = \"\"),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"object xpose.data object. listfile NONMEM output file modfile NONMEM model file ... Additional arguments passed function","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"name file exists, otherwise nothing returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"Function checks file exists, filename returned function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"Niclas Jonsson, Justin Wilkins, Mats Karlsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":null,"dir":"Reference","previous_headings":"","what":"Title — xpose.bootgam","title":"Title — xpose.bootgam","text":"Title","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Title — xpose.bootgam","text":"","code":"xpose.bootgam(   object,   n = n,   id = object@Prefs@Xvardef$id,   oid = \"OID\",   seed = NULL,   parnam = xvardef(\"parms\", object)[1],   covnams = xvardef(\"covariates\", object),   conv.value = object@Prefs@Bootgam.prefs$conv.value,   check.interval = as.numeric(object@Prefs@Bootgam.prefs$check.interval),   start.check = as.numeric(object@Prefs@Bootgam.prefs$start.check),   algo = object@Prefs@Bootgam.prefs$algo,   start.mod = object@Prefs@Bootgam.prefs$start.mod,   liif = as.numeric(object@Prefs@Bootgam.prefs$liif),   ljif.conv = as.numeric(object@Prefs@Bootgam.prefs$ljif.conv),   excluded.ids = as.numeric(object@Prefs@Bootgam.prefs$excluded.ids),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Title — xpose.bootgam","text":"object xpose.data object. n number bootstrap iterations id column name id oid create new column original ID data seed random seed parnam ONE (one) model parameter name. covnams Covariate names test parameter. conv.value Convergence value check.interval often check convergence start.check start checking algo algorithm use start.mod start model liif liif value ljif.conv convergence value liif excluded.ids ID values exclude. ... Used pass arguments basic functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Title — xpose.bootgam","text":"list results bootstrap GAM.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Title — xpose.bootgam","text":"","code":"if (FALSE) { ## filter out occasion as a covariate as only one value all_covs <- xvardef(\"covariates\",simpraz.xpdb) some_covs <- all_covs[!(all_covs %in% \"OCC\") ]   ## here only running n=5 replicates to see that things work ##   use something like n=100 for resonable results boot_gam_obj <- xpose.bootgam(simpraz.xpdb,5,parnam=\"KA\",covnams=some_covs,seed=1234) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions to create labels for plots — xpose.create.title","title":"Functions to create labels for plots — xpose.create.title","text":"Functions create labels plots","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions to create labels for plots — xpose.create.title","text":"","code":"xpose.create.title(   x,   y,   object,   subset = NULL,   funx = NULL,   funy = NULL,   no.runno = FALSE,   ... )  xpose.create.label(   x,   object,   fun,   logx,   autocorr.x = FALSE,   autocorr.y = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions to create labels for plots — xpose.create.title","text":"x Column name x-variable y Column name y variable object Xpose data object subset Subset used plot funx Function applied x data funy Function applied y data .runno include run number label ... additional arguments passed function. fun Function applied data","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions to create labels for plots — xpose.create.title","text":"Plot titles labels.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Functions to create labels for plots — xpose.create.title","text":"xpose.create.label(): Create label values","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions to create labels for plots — xpose.create.title","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.text.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Xpose title text for plots. — xpose.create.title.text","title":"Create Xpose title text for plots. — xpose.create.title.text","text":"Create Xpose title text plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Xpose title text for plots. — xpose.create.title.text","text":"","code":"xpose.create.title.text(x, y, text, object, subset, text2 = NULL, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Xpose title text for plots. — xpose.create.title.text","text":"x x-axis variable name. y y-axis variable name. text Initial text title. object Xpose data object xpose.data. subset Subset definition. text2 Text end title. ... Additional options passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.text.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create Xpose title text for plots. — xpose.create.title.text","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class xpose.data — xpose.data-class","title":"Class xpose.data — xpose.data-class","text":"xpose.data class fundamental data object Xpose 4. contains data preferences used creation Xpose plots analyses.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data-class.html","id":"objects-from-the-class","dir":"Reference","previous_headings":"","what":"Objects from the Class","title":"Class xpose.data — xpose.data-class","text":"Objects easily created xpose.data function, reads appropriate NONMEM table files populates slots object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Class xpose.data — xpose.data-class","text":"Niclas Jonsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an Xpose data object — xpose.data","title":"Create an Xpose data object — xpose.data","text":"Creates xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an Xpose data object — xpose.data","text":"","code":"xpose.data(   runno,   tab.suffix = \"\",   sim.suffix = \"sim\",   cwres.suffix = \"\",   directory = \".\",   quiet = TRUE,   table.names = c(\"sdtab\", \"mutab\", \"patab\", \"catab\", \"cotab\", \"mytab\", \"extra\", \"xptab\",     \"cwtab\"),   cwres.name = c(\"cwtab\"),   mod.prefix = \"run\",   mod.suffix = \".mod\",   phi.suffix = \".phi\",   phi.file = NULL,   nm7 = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an Xpose data object — xpose.data","text":"runno Run number table files read. tab.suffix Suffix appended table file names  \"real\" data. sim.suffix Suffix appended table file names  simulated data. cwres.suffix Suffix appended table file names  CWRES data. directory files located. quiet logical value indicating diagnostic messages printed running function. table.names Default text Xpose looks searching table files. cwres.name default text xpose looks searching CWRES  table files. mod.prefix Start model file name. mod.suffix End model file name. phi.suffix End .phi file name. phi.file name .phi file. NULL supersedes paste(mod.prefix,runno,phi.suffix,sep=\"\"). nm7 T/F table files NONMEM 7/6, NULL undefined. ... Extra arguments passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an Xpose data object — xpose.data","text":"xpose.data object.  Default values object    created file called 'xpose.ini'.  file can found root   directory 'xpose4' package:  system.file(\"xpose.ini\",package=\"xpose4\"). can modified fit users    wants placed home folder user working directory,    override default settings.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create an Xpose data object — xpose.data","text":"Xpose expects, default, find least one following NONMEM  tables working directory able create Xpose data object  (using run number '5' example): sdtab5: 'standard' parameters, including IWRE, IPRE, TIME, NONMEM default items (DV, PRED, RES WRES) added NOAPPEND  present $TABLE record. $TABLE ID TIME IPRE IWRE NOPRINT ONEHEADER FILE=sdtab5 patab5: empirical Bayes estimates individual model parameter values,  posthoc estimates. model parameters, CL, V2, ETA1, etc. $TABLE ID CL V2 KA K F1 ETA1 ETA2 ETA3 NOPRINT NOAPPEND ONEHEADER  FILE=patab5 catab5: Categorical covariates, e.g. SEX, RACE. $TABLE ID SEX HIV GRP NOPRINT NOAPPEND ONEHEADER FILE=catab5 cotab5: Continuous covariates, e.g. WT, AGE. $TABLE ID WT AGE BSA HT GGT HB NOPRINT NOAPPEND ONEHEADER FILE=cotab5 mutab5, mytab5, extra5, xptab5: Additional variables kind. might useful covariates can accommodated  covariates tables, example, variables added, e.g. CMAX, AUC. default names table files can changed changing default  values function.  files Xpose looks default : paste(table.names, runno, tab.suffix, sep=\"\") default CWRES table file name called: paste(cwres.name,runno,cwres.suffix,tab.suffix,sep=\"\") simulation files present Xpose looks files  named: paste(table.names, runno, sim.suffix, tab.suffix, sep=\"\")  paste(cwres.name,runno,sim.suffix,cwres.suffix,tab.suffix,sep=\"\") basically wrapper function read.nm.tables,  Data SData functions. See information. Also reads .phi file associated run (Individual OFVs,  parameters, variances parameters.)","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create an Xpose data object — xpose.data","text":"Niclas Jonsson, Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an Xpose data object — xpose.data","text":"","code":"# Here we create files from an example NONMEM run   od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-b4e0a141bd7a6d87d4e27f8e112db7d2\" #> [2] \"downlit\"                                #> [3] \"file1775638e0fef\"                        simprazExample(overwrite=TRUE) # write files (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"run1.ext\"    \"run1.lst\"    \"run1.mod\"    \"simpraz.dta\" \"xptab1\"       xpdb <- xpose.data(1) #>  #> Looking for NONMEM table files. #>     Reading ./xptab1  #> Table files read. #>  #> Looking for NONMEM simulation table files. #> No simulated table files read. #>    file.remove(new.files) # remove these files #> [1] TRUE TRUE TRUE TRUE TRUE setwd(od)  # restore working directory   if (FALSE) {  # We expect to find the required NONMEM run and table files for run # 5 in the current working directory, and that the table files have # a suffix of '.dat', e.g. sdtab5.dat xpdb5 <- xpose.data(5, tab.suffix = \".dat\")  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.dev.new.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a new graphical device for an Xpose plot. — xpose.dev.new","title":"Create a new graphical device for an Xpose plot. — xpose.dev.new","text":"function uses code dev.new(). function make dev.new() back compatible older versions R (2.8.0).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.dev.new.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a new graphical device for an Xpose plot. — xpose.dev.new","text":"","code":"xpose.dev.new(...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.dev.new.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a new graphical device for an Xpose plot. — xpose.dev.new","text":"... Additional arguments new graphical device.  see dev.new.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.dev.new.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a new graphical device for an Xpose plot. — xpose.dev.new","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"Function takes Xpose object performs generalized additive model (GAM) stepwise search influential covariates single model parameter.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"","code":"xpose.gam(   object,   parnam = xvardef(\"parms\", object)[1],   covnams = xvardef(\"covariates\", object),   trace = TRUE,   scope = NULL,   disp = object@Prefs@Gam.prefs$disp,   start.mod = object@Prefs@Gam.prefs$start.mod,   family = \"gaussian\",   wts.data = object@Data.firstonly,   wts.col = NULL,   steppit = object@Prefs@Gam.prefs$steppit,   subset = xsubset(object),   onlyfirst = object@Prefs@Gam.prefs$onlyfirst,   medianNorm = object@Prefs@Gam.prefs$medianNorm,   nmods = object@Prefs@Gam.prefs$nmods,   smoother1 = object@Prefs@Gam.prefs$smoother1,   smoother2 = object@Prefs@Gam.prefs$smoother2,   smoother3 = object@Prefs@Gam.prefs$smoother3,   smoother4 = object@Prefs@Gam.prefs$smoother4,   arg1 = object@Prefs@Gam.prefs$arg1,   arg2 = object@Prefs@Gam.prefs$arg2,   arg3 = object@Prefs@Gam.prefs$arg3,   arg4 = object@Prefs@Gam.prefs$arg4,   excl1 = object@Prefs@Gam.prefs$excl1,   excl2 = object@Prefs@Gam.prefs$excl2,   excl3 = object@Prefs@Gam.prefs$excl3,   excl4 = object@Prefs@Gam.prefs$excl4,   extra = object@Prefs@Gam.prefs$extra,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"object xpose.data object. parnam ONE (one) model parameter name. covnams Covariate names test parameter. trace TRUE want GAM output screen. scope Scope GAM search. disp dispersion used GAM object. start.mod Starting model. family Assumption parameter distribution. wts.data Weights least squares fitting parameter vs. covariate. Often one can use variances individual parameter values weights. data frame must column name ID subset variable well variable defined wts.col. wts.col column wts.data use. steppit TRUE stepwise search, false search. subset Subset data. onlyfirst TRUE first row individual's data used. medianNorm Normalize median parameter covariates. nmods Number models examine. smoother1 Smoother model. smoother2 Smoother model. smoother3 Smoother model. smoother4 Smoother model. arg1 Argument model 1. arg2 Argument model 2. arg3 Argument model 3. arg4 Argument model 4. excl1 Covariate exclusion model 1. excl2 Covariate exclusion model 2. excl3 Covariate exclusion model 3. excl4 Covariate exclusion model 4. extra Extra exclusion criteria. ... Used pass arguments basic functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"Returned step.Gam object. object  step-wise-selected model returned, two additional  components. \"anova\" component  corresponding steps taken search, well  \"keep\" component \"keep=\" argument supplied call.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"","code":"## Run a GAM using the example xpose database  gam_ka <- xpose.gam(simpraz.xpdb, parnam=\"KA\") #> Start:  KA ~ 1; AIC= 166.381  #> Step:1 KA ~ RACE ; AIC= 165.4162  #> Step:2 KA ~ RACE + SECR ; AIC= 164.8582  #> Step:3 KA ~ RACE + AGE + SECR ; AIC= 164.8219   ## Summarize GAM xp.summary(gam_ka) #>  #> SUMMARY #> Call: gam(formula = KA ~ RACE + AGE + SECR, data = gamdata, trace = FALSE) #> Deviance Residuals: #>      Min       1Q   Median       3Q      Max  #> -1.63145 -0.65128 -0.05558  0.41369  2.68692  #>  #> (Dispersion Parameter for gaussian family taken to be 0.6916) #>  #>     Null Deviance: 47.3804 on 63 degrees of freedom #> Residual Deviance: 40.8068 on 59 degrees of freedom #> AIC: 164.8219  #>  #> Number of Local Scoring Iterations: 2  #>  #> Anova for Parametric Effects #>           Df Sum Sq Mean Sq F value  Pr(>F)   #> RACE       2  3.537 1.76829  2.5567 0.08613 . #> AGE        1  1.629 1.62924  2.3556 0.13018   #> SECR       1  1.408 1.40786  2.0355 0.15893   #> Residuals 59 40.807 0.69164                   #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>  #>  #> PATH TO FINAL MODEL #> Stepwise Model Path  #> Analysis of Deviance Table #>  #> Initial Model: #> KA ~ 1 #>  #> Final Model: #> KA ~ RACE + AGE + SECR #>  #> Scale: 0.7520703 #>  #>   From      To Df  Deviance Resid. Df Resid. Dev      AIC #> 1                           63   47.38043 166.3810 #> 2         RACE -2 -3.536572        61   43.84386 165.4162 #> 3         SECR -1 -1.717840        60   42.12602 164.8582 #> 4          AGE -1 -1.319258        59   40.80676 164.8219 #>  #> COEFFICIENTS #> (Intercept)       RACE2       RACE3         AGE        SECR  #> -0.08272054  0.53724306 -0.32761296 -0.01411650  0.74934090  #>  #> PRERUN RESULTS #> Dispersion:  #>  #> DATA #> Subset expression:  #> Only first value of covariate considered #> for each individual: TRUE  #> Covariates normalized to median: TRUE   ## GAM residuals of base model vs. covariates xp.plot(gam_ka)   ## An Akaike plot of the results xp.akaike.plot(gam_ka)   ## Studentized residuals xp.ind.stud.res(gam_ka)   ## Individual influence on GAM fit xp.ind.inf.fit(gam_ka) #>  #> For ID 23: #>    Cook distance is Inf #>    Leverage is Inf #>    => the point is not included in the plot   ## Individual influence on GAM terms xp.ind.inf.terms(gam_ka)   ## Individual parameters to GAM fit xp.cook(gam_ka) #>     (Intercept)     RACE2     RACE3      AGE       SECR #> 2           NaN 0.4069038 0.4069038 6.384659 0.15108146 #> 12          NaN 0.6632264 0.6632264 6.166696 0.13341637 #> 23          NaN 0.5152555 0.5152555 5.976245 0.14359840 #> 34          NaN 0.4083608 0.4083608 5.526933 0.15078901 #> 44          NaN 0.4491550 0.4491550 4.878261 0.14571869 #> 54          NaN 0.4336340 0.4336340 6.669965 0.14805262 #> 64          NaN 0.3461857 0.3461857 4.527164 0.15895947 #> 72          NaN 0.3522048 0.3522048 3.950764 0.15794212 #> 82          NaN 0.4026346 0.4026346 5.765467 0.15160252 #> 91          NaN 0.4588266 0.4588266 4.012911 0.14621651 #> 97          NaN 0.4048793 0.4048793 5.617073 0.15128375 #> 107         NaN 0.3911857 0.3911857 6.913509 0.15265331 #> 118         NaN 0.4255037 0.4255037 4.985381 0.14871870 #> 129         NaN 0.2775285 0.2775285 5.384752 0.16549514 #> 140         NaN 0.2162661 0.2162661 5.215029 0.17375389 #> 151         NaN 0.8291616 0.8291616 6.405338 0.11860555 #> 162         NaN 0.4094348 0.4094348 5.503623 0.15077884 #> 173         NaN 0.4687459 0.4687459 5.750293 0.14515016 #> 184         NaN 0.2013301 0.2013301 5.245030 0.17645826 #> 194         NaN 0.1656837 0.1656837 5.095194 0.18085408 #> 204         NaN 0.4118900 0.4118900 6.223995 0.15061385 #> 212         NaN 0.4044946 0.4044946 5.637613 0.15133188 #> 223         NaN 0.2613920 0.2613920 5.230492 0.16681641 #> 234         NaN 0.4215614 0.4215614 5.674149 0.14967349 #> 245         NaN 0.2141738 0.2141738 5.160388 0.17440596 #> 256         NaN 0.3187987 0.3187987 5.435324 0.16015507 #> 267         NaN 0.3931962 0.3931962 5.619256 0.15244641 #> 278         NaN 0.1791398 0.1791398 5.118928 0.17802496 #> 288         NaN 0.2786389 0.2786389 5.355595 0.16394934 #> 299         NaN 0.3020364 0.3020364 5.331388 0.15913854 #> 310         NaN 0.5316421 0.5316421 5.886831 0.14016374 #> 321         NaN 0.4808547 0.4808547 5.806053 0.14449849 #> 332         NaN 0.3755979 0.3755979 5.551405 0.15355223 #> 341         NaN 0.2468930 0.2468930 5.263710 0.16836440 #> 350         NaN 0.4810597 0.4810597 5.803645 0.14479950 #> 361         NaN 0.1363992 0.1363992 5.119674 0.18758251 #> 367         NaN 0.4390024 0.4390024 4.949578 0.14706063 #> 373         NaN 0.4171210 0.4171210 5.667151 0.15017331 #> 382         NaN 0.6554549 0.6554549 6.115453 0.13743260 #> 393         NaN 0.3212037 0.3212037 5.450513 0.15824381 #> 404         NaN 0.4468825 0.4468825 5.703860 0.14773504 #> 411         NaN 0.2944807 0.2944807 5.373341 0.16129232 #> 422         NaN 0.3582801 0.3582801 5.537411 0.15588547 #> 433         NaN 0.4071513 0.4071513 6.098040 0.15089973 #> 438         NaN 0.4004948 0.4004948 5.913169 0.15183038 #> 449         NaN 0.5671897 0.5671897 5.977558 0.13739557 #> 455         NaN 1.4642283 1.4642283 7.298495 0.08745328 #> 468         NaN 0.3706551 0.3706551 5.533304 0.15447763 #> 479         NaN 0.4053951 0.4053951 5.565423 0.15118627 #> 490         NaN 0.4250737 0.4250737 6.548404 0.14867238 #> 500         NaN 0.3402770 0.3402770 5.489249 0.15785681 #> 511         NaN 0.5110472 0.5110472 5.847233 0.14135623 #> 522         NaN 0.5370821 0.5370821 7.805795 0.13603361 #> 533         NaN 0.4734972 0.4734972 5.849134 0.14556442 #> 543         NaN 0.3813159 0.3813159 5.346115 0.15430963 #> 550         NaN 0.1893831 0.1893831 5.069682 0.17774347 #> 561         NaN 0.6178044 0.6178044 6.027150 0.13309655 #> 569         NaN 0.3328130 0.3328130 5.436414 0.15842242 #> 578         NaN 0.8144037 0.8144037 6.370737 0.12069337 #> 589         NaN 0.7148646 0.7148646 6.242226 0.12660672 #> 600         NaN 0.5095002 0.5095002 5.829433 0.14194991 #> 610         NaN 0.3837543 0.3837543 5.596823 0.15345795 #> 620         NaN 0.2205120 0.2205120 5.213856 0.17129241 #> 631         NaN 0.3398401 0.3398401 5.505575 0.15815307"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":null,"dir":"Reference","previous_headings":"","what":"Displays the Xpose license and citation information — xpose.license.citation","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"function displays copy Xpose's end user license agreement (EULA).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"","code":"xpose.license.citation()"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"EULA.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"","code":"xpose.license.citation() #>  #> This program is free software: you can redistribute it and/or modify #>   it under the terms of the GNU Lesser General Public License as published by #>   the Free Software Foundation, either version 3 of the License, or #>   (at your option) any later version. #>    #>   This program is distributed in the hope that it will be useful, #>   but WITHOUT ANY WARRANTY; without even the implied warranty of #>   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the #>   GNU Lesser General Public License for more details. #>    #>   A copy of the GNU Lesser General Public License can be found in the R #>   installation directory (/opt/R/4.3.2/lib/R/share)under licenses.   #>   If not, see . #>  #> To cite xpose4 in publications, use: #>  #>   Jonsson, E.N. & Karlsson, M.O. (1999) Xpose--an S-PLUS based #>   population pharmacokinetic/pharmacodynamic model building aid for #>   NONMEM. Computer Methods and Programs in Biomedicine. 58(1):51-64. #>  #>   Keizer RJ, Karlsson MO, Hooker AC (2013). “Modeling and Simulation #>   Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose.” _CPT: #>   Pharmacometrics & Systems Pharmacology_, *2*(6). #>   doi:10.1038/psp.2013.24 . #>  #> To see these entries in BibTeX format, use 'print(, #> bibtex=TRUE)', 'toBibtex(.)', or set #> 'options(citation.bibtex.max=999)'."},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class for creating multiple plots in xpose — xpose.multiple.plot-class","title":"Class for creating multiple plots in xpose — xpose.multiple.plot-class","text":"Class creating multiple plots xpose","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class for creating multiple plots in xpose — xpose.multiple.plot-class","text":"plotList list lattice plots plotTitle plot title prompt prompts used new.first.window Create new first window? max.plots.per.page many plots per page? title title mirror mirror plots create bql.layout use bql.layout","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"Function takes list lattice plot objects prints multiple plot layout title.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"","code":"xpose.multiple.plot.default(   plotList,   plotTitle = NULL,   prompt = FALSE,   new.first.window = FALSE,   max.plots.per.page = 4,   title = list(title.x = unit(0.5, \"npc\"), title.y = unit(0.5, \"npc\"), title.gp =     gpar(cex = 1.2, fontface = \"bold\"), title.just = c(\"center\", \"center\")),   mirror = FALSE,   bql.layout = FALSE,   page.numbers = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"plotList list lattice plot objects plot object can called plotList[[]] plotTitle title used multiple plot layout prompt one page needed want prompt command line next page printed new.first.window first page plot already opened window new window created max.plots.per.page Maximum number plots per page multiple layout title Look title using grid. mirror list contains mirror plots bql.layout use layout optimized BQL measurements? page.numbers add page numbers multiple page plots? ... arguments passed code function","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"returns nothing","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"Additional arguments: title.x title placed title grid region title.y title placed title grid region title.just title justified title.gp par parameters title (see grid)","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create and object with class ","title":"Create and object with class ","text":"Create object class \"xpose.multiple.plot\".","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create and object with class ","text":"","code":"xpose.multiple.plot(   plotList,   plotTitle = NULL,   nm7 = TRUE,   prompt = FALSE,   new.first.window = FALSE,   max.plots.per.page = 4,   title = list(title.x = unit(0.5, \"npc\"), title.y = unit(0.5, \"npc\"), title.gp =     gpar(cex = 1.2, fontface = \"bold\"), title.just = c(\"center\", \"center\")),   mirror = FALSE,   bql.layout = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create and object with class ","text":"plotList list lattice plots. plotTitle Main title plots. nm7 TRUE using NONMEM 7 prompt printing prompt new page plot? new.first.window TRUE FALSE. max.plots.per.page number.  Max value 9. title Title properties. mirror mirror plots plot list? bql.layout use layout optimized plots BQL (limit quantification) measurements? ... Additional options passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create and object with class ","text":"object class \"xpose.multiple.plot\".","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create and object with class ","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","title":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","text":"box--whisker panel function Xpose 4. intended used outside xpose.plot.bw function.  arguments take default values xpose.data object can overridden supplying arguments xpose.plot.bw.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","text":"","code":"xpose.panel.bw(   x,   y,   object,   subscripts,   groups = NULL,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   xvarnam = NULL,   yvarnam = NULL,   type = object@Prefs@Graph.prefs$type,   col = object@Prefs@Graph.prefs$col,   pch = object@Prefs@Graph.prefs$pch,   cex = object@Prefs@Graph.prefs$cex,   lty = object@Prefs@Graph.prefs$lty,   fill = object@Prefs@Graph.prefs$col,   ids = NULL,   idsmode = object@Prefs@Graph.prefs$idsmode,   idsext = object@Prefs@Graph.prefs$idsext,   idscex = object@Prefs@Graph.prefs$idscex,   idsdir = object@Prefs@Graph.prefs$idsdir,   bwhoriz = object@Prefs@Graph.prefs$bwhoriz,   bwratio = object@Prefs@Graph.prefs$bwratio,   bwvarwid = object@Prefs@Graph.prefs$bwvarwid,   bwdotpch = object@Prefs@Graph.prefs$bwdotpch,   bwdotcol = object@Prefs@Graph.prefs$bwdotcol,   bwdotcex = object@Prefs@Graph.prefs$bwdotcex,   bwreccol = object@Prefs@Graph.prefs$bwreccol,   bwrecfill = object@Prefs@Graph.prefs$bwrecfill,   bwreclty = object@Prefs@Graph.prefs$bwreclty,   bwreclwd = object@Prefs@Graph.prefs$bwreclwd,   bwumbcol = object@Prefs@Graph.prefs$bwumbcol,   bwumblty = object@Prefs@Graph.prefs$bwumblty,   bwumblwd = object@Prefs@Graph.prefs$bwumblwd,   bwoutcol = object@Prefs@Graph.prefs$bwoutcol,   bwoutcex = object@Prefs@Graph.prefs$bwoutcex,   bwoutpch = object@Prefs@Graph.prefs$bwoutpch,   grid = object@Prefs@Graph.prefs$grid,   logy = FALSE,   logx = FALSE,   force.x.continuous = TRUE,   binvar = NULL,   bins = 10,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","text":"x Name(s) x-variable. y Name(s) y-variable. object xpose.data object. subscripts standard Trellis subscripts argument (see xyplot). groups Name variable used superpose plots. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. onlyfirst Logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. xvarnam Character string name x-variable. yvarnam Character string name y-variable. type Character value indicating type display use: \"l\"=lines, \"p\"=points, \"b\"=points lines. col Colour lines plot symbols. pch Plot character use. cex Size plot characters. lty Line type. fill Fill colour. ids Character value name variable label data points . idsmode Determines way text labels added plots. NULL means extreme points labelled. Non-NULL means data points labelled. (See link{xpose.plot.default}) idsext See link{xpose.plot.bw} idscex Size text labels. idsdir value \"\" (default) means high low extreme points labelled \"\" \"\" labels high low extreme points respectively. See xpose.plot.bw bwhoriz logical value indicating whether box whiskers horizontal . default FALSE. bwratio Ratio box height inter-box space. default 1.5. argument panel.bwplot. bwvarwid Logical. TRUE, widths boxplots proportional number points used creating . default FALSE. argument panel.bwplot. bwdotpch Graphical parameter controlling dot plotting character 'bwdotpch=\"|\"' treated specially, replacing dot line. default 16. argument panel.bwplot. bwdotcol Graphical parameter controlling dot colour - integer string. See 'col'. default black. argument panel.bwplot. bwdotcex amount plotting text symbols scaled relative default. 'NULL' 'NA' equivalent '1.0'. argument panel.bwplot. bwreccol colour use box rectangle - integer string.  default blue. See trellis.par.get \"box.rectangle\". bwrecfill colour use filling box rectangle - integer string. default transparent (none). See trellis.par.get \"box.rectangle\". bwreclty line type box rectangle - integer string. default solid. See trellis.par.get \"box.rectangle\". bwreclwd width lines box rectangle - integer. default 1. See trellis.par.get \"box.rectangle\". bwumbcol colour use umbrellas - integer string. default blue. See trellis.par.get \"box.umbrella\". bwumblty line type umbrellas - integer string. default solid.See trellis.par.get \"box.umbrella\". bwumblwd width lines umbrellas - integer. default 1. See trellis.par.get \"box.umbrella\". bwoutcol colour use outliers - integer string. default blue. See trellis.par.get \"box.symbol\". bwoutcex amount outlier points scaled relative default. 'NULL' 'NA' equivalent '1.0'. default 0.8. See trellis.par.get \"box.symbol\". bwoutpch plotting character, symbol, use outlier points.  Specified integer. See R help 'points'. default open circle. See trellis.par.get \"box.symbol\". grid logical value indicating whether visual reference grid added graph. (use arguments line type, color etc). logy Logical value indicating whether y-axis logarithmic. logx Logical value indicating whether x-axis logarithmic. force.x.continuous Logical value indicating whether x-values taken continuous, even categorical. binvar Variable used binning. bins number bins used. default 10. ... arguments may needed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Default panel function for Xpose 4 — xpose.panel.default","title":"Default panel function for Xpose 4 — xpose.panel.default","text":"panel function Xpose 4. intended ised outside xpose.plot.default function. arguments take default values xpose.data object can overridden supplying argument xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default panel function for Xpose 4 — xpose.panel.default","text":"","code":"xpose.panel.default(   x,   y,   object,   subscripts,   groups = object@Prefs@Xvardef$id,   grp.col = NULL,   iplot = NULL,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   xvarnam = NULL,   yvarnam = NULL,   PI = NULL,   PI.subset = NULL,   PI.bin.table = NULL,   PI.real = NULL,   PI.mirror = NULL,   PI.ci = NULL,   PPI = NULL,   PI.mean = FALSE,   PI.delta.mean = FALSE,   PI.x.median = TRUE,   PI.rug = \"Default\",   PI.rug.col = \"orange\",   PI.rug.lwd = 3,   PI.identify.outliers = TRUE,   PI.outliers.col = \"red\",   PI.outliers.pch = 8,   PI.outliers.cex = 1,   PI.limits = c(0.025, 0.975),   PI.arcol = \"lightgreen\",   PI.up.lty = 2,   PI.up.type = \"l\",   PI.up.col = \"black\",   PI.up.lwd = 2,   PI.down.lty = 2,   PI.down.type = \"l\",   PI.down.col = \"black\",   PI.down.lwd = 2,   PI.med.lty = 1,   PI.med.type = \"l\",   PI.med.col = \"black\",   PI.med.lwd = 2,   PI.mean.lty = 3,   PI.mean.type = \"l\",   PI.mean.col = \"black\",   PI.mean.lwd = 2,   PI.delta.mean.lty = 3,   PI.delta.mean.type = \"l\",   PI.delta.mean.col = \"black\",   PI.delta.mean.lwd = 2,   PI.real.up.lty = 2,   PI.real.up.type = \"l\",   PI.real.up.col = \"red\",   PI.real.up.lwd = 2,   PI.real.down.lty = 2,   PI.real.down.type = \"l\",   PI.real.down.col = \"red\",   PI.real.down.lwd = 2,   PI.real.med.lty = 1,   PI.real.med.type = \"l\",   PI.real.med.col = \"red\",   PI.real.med.lwd = 2,   PI.real.mean.lty = 3,   PI.real.mean.type = \"l\",   PI.real.mean.col = \"red\",   PI.real.mean.lwd = 2,   PI.real.delta.mean.lty = 3,   PI.real.delta.mean.type = \"l\",   PI.real.delta.mean.col = \"red\",   PI.real.delta.mean.lwd = 2,   PI.mirror.up.lty = 2,   PI.mirror.up.type = \"l\",   PI.mirror.up.col = \"darkgreen\",   PI.mirror.up.lwd = 1,   PI.mirror.down.lty = 2,   PI.mirror.down.type = \"l\",   PI.mirror.down.col = \"darkgreen\",   PI.mirror.down.lwd = 1,   PI.mirror.med.lty = 1,   PI.mirror.med.type = \"l\",   PI.mirror.med.col = \"darkgreen\",   PI.mirror.med.lwd = 1,   PI.mirror.mean.lty = 3,   PI.mirror.mean.type = \"l\",   PI.mirror.mean.col = \"darkgreen\",   PI.mirror.mean.lwd = 1,   PI.mirror.delta.mean.lty = 3,   PI.mirror.delta.mean.type = \"l\",   PI.mirror.delta.mean.col = \"darkgreen\",   PI.mirror.delta.mean.lwd = 1,   PI.ci.up.arcol = \"blue\",   PI.ci.up.lty = 3,   PI.ci.up.type = \"l\",   PI.ci.up.col = \"darkorange\",   PI.ci.up.lwd = 2,   PI.ci.down.arcol = \"blue\",   PI.ci.down.lty = 3,   PI.ci.down.type = \"l\",   PI.ci.down.col = \"darkorange\",   PI.ci.down.lwd = 2,   PI.ci.med.arcol = \"red\",   PI.ci.med.lty = 4,   PI.ci.med.type = \"l\",   PI.ci.med.col = \"darkorange\",   PI.ci.med.lwd = 2,   PI.ci.mean.arcol = \"purple\",   PI.ci.mean.lty = 4,   PI.ci.mean.type = \"l\",   PI.ci.mean.col = \"darkorange\",   PI.ci.mean.lwd = 2,   PI.ci.delta.mean.arcol = \"purple\",   PI.ci.delta.mean.lty = 4,   PI.ci.delta.mean.type = \"l\",   PI.ci.delta.mean.col = \"darkorange\",   PI.ci.delta.mean.lwd = 2,   PI.ci.area.smooth = FALSE,   type = object@Prefs@Graph.prefs$type,   col = object@Prefs@Graph.prefs$col,   pch = object@Prefs@Graph.prefs$pch,   cex = object@Prefs@Graph.prefs$cex,   lty = object@Prefs@Graph.prefs$lty,   lwd = object@Prefs@Graph.prefs$lwd,   fill = object@Prefs@Graph.prefs$fill,   ids = NULL,   idsmode = object@Prefs@Graph.prefs$idsmode,   idsext = object@Prefs@Graph.prefs$idsext,   idscex = object@Prefs@Graph.prefs$idscex,   idsdir = object@Prefs@Graph.prefs$idsdir,   abline = object@Prefs@Graph.prefs$abline,   abllwd = object@Prefs@Graph.prefs$abllwd,   abllty = object@Prefs@Graph.prefs$abllty,   ablcol = object@Prefs@Graph.prefs$ablcol,   smooth = object@Prefs@Graph.prefs$smooth,   smlwd = object@Prefs@Graph.prefs$smlwd,   smlty = object@Prefs@Graph.prefs$smlty,   smcol = object@Prefs@Graph.prefs$smcol,   smspan = object@Prefs@Graph.prefs$smspan,   smdegr = object@Prefs@Graph.prefs$smdegr,   smooth.for.groups = NULL,   lmline = object@Prefs@Graph.prefs$lmline,   lmlwd = object@Prefs@Graph.prefs$lmlwd,   lmlty = object@Prefs@Graph.prefs$lmlty,   lmcol = object@Prefs@Graph.prefs$lmcol,   suline = object@Prefs@Graph.prefs$suline,   sulwd = object@Prefs@Graph.prefs$sulwd,   sulty = object@Prefs@Graph.prefs$sulty,   sucol = object@Prefs@Graph.prefs$sucol,   suspan = object@Prefs@Graph.prefs$suspan,   sudegr = object@Prefs@Graph.prefs$sudegr,   grid = object@Prefs@Graph.prefs$grid,   logy = FALSE,   logx = FALSE,   force.x.continuous = FALSE,   bwhoriz = object@Prefs@Graph.prefs$bwhoriz,   bwratio = object@Prefs@Graph.prefs$bwratio,   bwvarwid = object@Prefs@Graph.prefs$bwvarwid,   bwdotpch = object@Prefs@Graph.prefs$bwdotpch,   bwdotcol = object@Prefs@Graph.prefs$bwdotcol,   bwdotcex = object@Prefs@Graph.prefs$bwdotcex,   bwreccol = object@Prefs@Graph.prefs$bwreccol,   bwrecfill = object@Prefs@Graph.prefs$bwrecfill,   bwreclty = object@Prefs@Graph.prefs$bwreclty,   bwreclwd = object@Prefs@Graph.prefs$bwreclwd,   bwumbcol = object@Prefs@Graph.prefs$bwumbcol,   bwumblty = object@Prefs@Graph.prefs$bwumblty,   bwumblwd = object@Prefs@Graph.prefs$bwumblwd,   bwoutcol = object@Prefs@Graph.prefs$bwoutcol,   bwoutcex = object@Prefs@Graph.prefs$bwoutcex,   bwoutpch = object@Prefs@Graph.prefs$bwoutpch,   autocorr = FALSE,   vline = NULL,   vllwd = 3,   vllty = 2,   vlcol = \"grey\",   hline = NULL,   hllwd = 3,   hllty = 1,   hlcol = \"grey\",   pch.ip.sp = pch,   cex.ip.sp = cex,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default panel function for Xpose 4 — xpose.panel.default","text":"x Name(s) x-variable. y Name(s) y-variable. object xpose.data object. subscripts standard Trellis subscripts argument (see xyplot) groups Name variable used superpose plots. grp.col Logical value indicating whether use colour highlighting groups specified. NULL means highlighting, TRUE identify group members colour. iplot individual plots matrix? Internal use . inclZeroWRES Logical value indicating whether rows WRES=0 included plot. onlyfirst Logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. xvarnam Character string name x-variable. yvarnam Character string name y-variable. PI Either \"lines\", \"area\" \"\" specifying whether prediction intervals (lines, shaded area ) computed data SData added display. NULL means prediction interval. PI.subset subset used PI. PI.bin.table table used create VPC plots.  specific format created read.npc.vpc.results PI.real Plot percentiles real data various bins. values can NULL TRUE.  Note bin actual observations percentiles approximate.  example, 95th percentile 4 data points always largest 4 data points. PI.mirror Plot percentiles one simulated data set bin. values allowed NULL, TRUE .INTEGER.VALUE. TRUE takes first mirror PI.bin.table .INTEGER.VALUE can 1, 2, ...{} n n number mirror's output PI.bin.table.  Used mainly xpose.VPC. PI.ci Plot prediction interval simulated data's percentiles bin. Values can \"\", \"area\" \"lines\" can thought prediction interval PI.real confidence interval PI.  However, note increasing number simulations CI go towards zero interval also dependent size data set. PPI plot prediction interval. specific format must followed.  See setup.PPI. PI.mean mean plotted VPCs? TRUE FALSE. PI.delta.mean delta mean plotted VPCs? TRUE FALSE. PI.x.median x-location percentile lines bin  marked median x-values? (TRUE FALSE) PI.rug markings plot showing binning intervals  VPC  (locations independent variable used VPC calculation binning used)? PI.rug.col Color PI.rug. PI.rug.lwd Linw width PI.rug. PI.identify.outliers outlying percentiles real data highlighted? (TRUE FALSE) PI.outliers.col Color PI.identify.outliers points PI.outliers.pch pch PI.identify.outliers points PI.outliers.cex cex PI.identify.outliers points PI.limits vector two values describe limits prediction interval displayed.  example c(0.025, 0.975).  limits found PI.bin.table table. limits also used percentages PI.real, PI.mirror PI.ci.  However, confidence interval PI.ci always one defined PI.bin.table. PI.arcol color PI area PI..lty upper line type. can \"dotted\" \"dashed\", etc. PI..type upper type used plotting.  Defaults line. PI..col upper line color PI..lwd upper line width PI..lty lower line type. can \"dotted\" \"dashed\", etc. PI..type lower type used plotting.  Defaults line. PI..col lower line color PI..lwd lower line width PI.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.med.type median type used plotting.  Defaults line. PI.med.col median line color PI.med.lwd median line width PI.mean.lty mean line type. can \"dotted\" \"dashed\", etc. PI.mean.type mean type used plotting.  Defaults line. PI.mean.col mean line color PI.mean.lwd mean line width PI.delta.mean.lty delta.mean line type. can \"dotted\" \"dashed\", etc. PI.delta.mean.type delta.mean type used plotting.  Defaults line. PI.delta.mean.col delta.mean line color PI.delta.mean.lwd delta.mean line width PI.real..lty upper line type. can \"dotted\" \"dashed\", etc. PI.real..type upper type used plotting.  Defaults line. PI.real..col upper line color PI.real..lwd upper line width PI.real..lty lower line type. can \"dotted\" \"dashed\", etc. PI.real..type lower type used plotting.  Defaults line. PI.real..col lower line color PI.real..lwd lower line width PI.real.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.real.med.type median type used plotting.  Defaults line. PI.real.med.col median line color PI.real.med.lwd median line width PI.real.mean.lty mean line type. can \"dotted\" \"dashed\", etc. PI.real.mean.type mean type used plotting.  Defaults line. PI.real.mean.col mean line color PI.real.mean.lwd mean line width PI.real.delta.mean.lty delta.mean line type. can \"dotted\" \"dashed\", etc. PI.real.delta.mean.type delta.mean type used plotting. Defaults line. PI.real.delta.mean.col delta.mean line color PI.real.delta.mean.lwd delta.mean line width PI.mirror..lty upper line type. can \"dotted\" \"dashed\", etc. PI.mirror..type upper type used plotting.  Defaults line. PI.mirror..col upper line color PI.mirror..lwd upper line width PI.mirror..lty lower line type. can \"dotted\" \"dashed\", etc. PI.mirror..type lower type used plotting.  Defaults line. PI.mirror..col lower line color PI.mirror..lwd lower line width PI.mirror.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.mirror.med.type median type used plotting.  Defaults line. PI.mirror.med.col median line color PI.mirror.med.lwd median line width PI.mirror.mean.lty mean line type. can \"dotted\" \"dashed\", etc. PI.mirror.mean.type mean type used plotting.  Defaults line. PI.mirror.mean.col mean line color PI.mirror.mean.lwd mean line width PI.mirror.delta.mean.lty delta.mean line type. can \"dotted\" \"dashed\", etc. PI.mirror.delta.mean.type delta.mean type used plotting. Defaults line. PI.mirror.delta.mean.col delta.mean line color PI.mirror.delta.mean.lwd delta.mean line width PI.ci..arcol color upper PI.ci. PI.ci..lty upper line type. can \"dotted\" \"dashed\", etc. PI.ci..type upper type used plotting.  Defaults line. PI.ci..col upper line color PI.ci..lwd upper line width PI.ci..arcol color lower PI.ci. PI.ci..lty lower line type. can \"dotted\" \"dashed\", etc. PI.ci..type lower type used plotting.  Defaults line. PI.ci..col lower line color PI.ci..lwd lower line width PI.ci.med.arcol color median PI.ci. PI.ci.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.ci.med.type median type used plotting.  Defaults line. PI.ci.med.col median line color PI.ci.med.lwd median line width PI.ci.mean.arcol color mean PI.ci. PI.ci.mean.lty mean line type. can \"dotted\" \"dashed\", etc. PI.ci.mean.type mean type used plotting.  Defaults line. PI.ci.mean.col mean line color PI.ci.mean.lwd mean line width PI.ci.delta.mean.arcol color delta.mean PI.ci. PI.ci.delta.mean.lty delta.mean line type. can \"dotted\" \"dashed\", etc. PI.ci.delta.mean.type delta.mean type used plotting. Defaults line. PI.ci.delta.mean.col delta.mean line color PI.ci.delta.mean.lwd delta.mean line width PI.ci.area.smooth \"area\" PI.ci smoothed match \"lines\" argument? Allowed values TRUE/FALSE. \"area\" set default show bins used PI.ci computation.  smoothing, information lost , general, confidence intervals smaller reality. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. col color lines points. Specified integer text string. full list obtained R command colours(). default blue (col=4). pch plotting character, symbol, use. Specified integer. See R help points. default open circle. cex amount plotting text symbols scaled relative default. 'NULL' 'NA' equivalent '1.0'. lty line type. Line types can either specified integer (0=blank, 1=solid, 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash) one character strings '\"blank\"', '\"solid\"', '\"dashed\"', '\"dotted\"', '\"dotdash\"', '\"longdash\"', '\"twodash\"', '\"blank\"' uses 'invisible lines' (.e., draw ). lwd width lines. Specified integer. default 1. fill fill areas plot ids Logical value specifying whether label data points. idsmode Determines way text labels added plots. NULL means extreme points labelled. Non-NULL means data points labelled. (See link{xpose.plot.default}) idsext specifies extent extremes used labelling points. default 0.05 (extreme 5% points labelled). idscex amount labels scaled relative default. 'NULL' 'NA' equivalent '1.0'. idsdir string indicating directions extremes include labelling. Possible values \"\", \"\" \"\". abline Vector arguments panel.abline function. abline drawn NULL. abllwd Line width abline. abllty Line type abline. ablcol Line colour abline. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. smlwd Line width x-y smooth. smlty Line type x-y smooth. smcol Line color x-y smooth. smspan smoothness parameter x-y smooth. default 0.667. argument panel.loess. smdegr degree polynomials used x-y smooth, 2. default 1. argument panel.loess. smooth..groups smooth group drawn? lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) lmlwd Line width lmline. lmlty Line type lmline. lmcol Line colour lmline. suline NULL value indicates superposed line added graph. non-NULL vector (length y) data points used smoothed superposed line. sulwd Line width superposed smooth. sulty Line type superposed smooth. sucol Line color superposed smooth. suspan smoothness parameter. default 0.667. argument panel.loess. sudegr degree polynomials used, 2. default 1. argument panel.loess. grid logical value indicating whether visual reference grid added graph. (use arguments line type, color etc). logy Logical value indicating whether y-axis logarithmic. logx Logical value indicating whether y-axis logarithmic. force.x.continuous Logical value indicating whether x-values taken continuous, even categorical. bwhoriz logical value indicating whether box whiskers horizontal . default FALSE. bwratio Ratio box height inter-box space. default 1.5. argument panel.bwplot. bwvarwid Logical. TRUE, widths boxplots proportional number points used creating . default FALSE. argument panel.bwplot. bwdotpch Graphical parameter controlling dot plotting character boxplots. 'bwdotpch=\"|\"' treated specially, replacing dot line. default 16. argument panel.bwplot. bwdotcol Graphical parameter controlling dot colour boxplots - integer string. See 'col'. default black. argument panel.bwplot. bwdotcex amount plotting text symbols scaled relative default boxplots. 'NULL' 'NA' equivalent '1.0'. argument panel.bwplot. bwreccol colour use box rectangle boxplots - integer string.  default blue. See trellis.par.get \"box.rectangle\". bwrecfill colour use filling box rectangle boxplots - integer string. default transparent (none). See trellis.par.get \"box.rectangle\". bwreclty line type box rectangle boxplots - integer string.  default solid. See trellis.par.get \"box.rectangle\". bwreclwd width lines box rectangle boxplots - integer. default 1. See trellis.par.get \"box.rectangle\". bwumbcol colour use umbrellas boxplots - integer string.  default blue. See trellis.par.get \"box.umbrella\". bwumblty line type umbrellas boxplots - integer string. default solid.See trellis.par.get \"box.umbrella\". bwumblwd width lines umbrellas boxplots - integer. default 1. See trellis.par.get \"box.umbrella\". bwoutcol colour use outliers boxplots - integer string.  default blue. See trellis.par.get \"box.symbol\". bwoutcex amount outlier points scaled relative default boxplots. 'NULL' 'NA' equivalent '1.0'. default 0.8. See trellis.par.get \"box.symbol\". bwoutpch plotting character, symbol, use outlier points boxplots.  Specified integer. See R help 'points'. default open circle. See trellis.par.get \"box.symbol\". autocorr autocorrelation plot?  Values can TRUE/FALSE. vline Add vertical line plot values specified. vllwd Width (lwd) vertical line vllty Line type (lty) vertical line vlcol Color (col) vertical line hline Add horizontal line plot values specified. hllwd Width (lwd) horizontal line hllty Line type (lty) horizontal line hlcol Color (col) horizontal line pch.ip.sp panel just one observation specifies type points DV, IPRED PRED respectively. cex.ip.sp panel just one observation specifies size points DV, IPRED PRED respectively. ... arguments may needed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.default.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default panel function for Xpose 4 — xpose.panel.default","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","title":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","text":"histogram panel function Xpose 4. intended ised outside xpose.plot.histogram function. arguments take default values xpose.data object can overridden supplying argument xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","text":"","code":"xpose.panel.histogram(   x,   object,   breaks = NULL,   dens = TRUE,   hidlty = object@Prefs@Graph.prefs$hidlty,   hidcol = object@Prefs@Graph.prefs$hidcol,   hidlwd = object@Prefs@Graph.prefs$hidlwd,   hiborder = object@Prefs@Graph.prefs$hiborder,   hilty = object@Prefs@Graph.prefs$hilty,   hicol = object@Prefs@Graph.prefs$hicol,   hilwd = object@Prefs@Graph.prefs$hilwd,   math.dens = NULL,   vline = NULL,   vllwd = 3,   vllty = 1,   vlcol = \"grey\",   hline = NULL,   hllwd = 3,   hllty = 1,   hlcol = \"grey\",   bins.per.panel.equal = TRUE,   showMean = FALSE,   meanllwd = 3,   meanllty = 1,   meanlcol = \"orange\",   showMedian = FALSE,   medianllwd = 3,   medianllty = 1,   medianlcol = \"black\",   showPCTS = FALSE,   PCTS = c(0.025, 0.975),   PCTSllwd = 2,   PCTSllty = hidlty,   PCTSlcol = \"black\",   vdline = NULL,   vdllwd = 3,   vdllty = 1,   vdlcol = \"red\",   ...,   groups )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","text":"x Name(s) x-variable. object xpose.data object. breaks breakpoints histogram. dens Density plot top histogram? hidlty Density line type. hidcol Color density line. hidlwd Width density line. hiborder Colour bar borders. hilty Line type bar borders. hicol Fill colour bars. hilwd Width bar borders. math.dens density line drawn.  Values NULL TRUE. vline NULL vector locations vertical lines drawn.  example, vline=c(50,60) draw two vertical lines. function panel.abline used. vllwd Line width vertical lines defined vline. Can vector single value, example vllwd=2 vllwd=c(2,3). vllty Line type vertical lines defined vline. Can vector single value, example vllty=1 vllty=c(1,2). vlcol Line color vertical lines defined vline. Can vector single value, example vlcol=\"red\" vllty=c(\"red\",\"blue\"). hline NULL vector locations horizontal lines drawn.  example, hline=c(50,60) draw two horizontal lines. function panel.abline used. hllwd Line width horizontal lines defined hline. Can vector single value, example hllwd=2 hllwd=c(2,3). hllty Line type horizontal lines defined hline. Can vector single value, example hllty=1 hllty=c(1,2). hlcol Line color horizontal lines defined hline. Can vector single value, example hlcol=\"red\" hllty=c(\"red\",\"blue\"). bins.per.panel.equal Allow different bins different panels continuous data? TRUE FALSE. showMean mean data histogram shown? meanllwd Line width mean line. meanllty line type mean meanlcol Color mean line showMedian median data histogram shown vertical line? medianllwd line width median line. medianllty line type median line. medianlcol color median line. showPCTS percentiles data histogram shown? PCTS vector percentiles show.  Can length. PCTSllwd line width percentiles.  Can vector length PCTS. PCTSllty Line type percentiles.  Can vector length PCTS. PCTSlcol Color percentiles.  Can vector length PCTS. vdline vertical line different histogram. Must vector. vdllwd line widths vdllty line types vdlcol line colors ... arguments may needed function. groups used pass conditioning variable function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.histogram.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","text":"Andrew Hooker, Mats Karlsson, Justin Wilkins & E. Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Default QQ panel function for Xpose 4 — xpose.panel.qq","title":"Default QQ panel function for Xpose 4 — xpose.panel.qq","text":"QQ panel function Xpose 4. intended used outside xpose.plot.qq function.  arguments take default values xpose.data object can overridden supplying argument xpose.plot.qq.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default QQ panel function for Xpose 4 — xpose.panel.qq","text":"","code":"xpose.panel.qq(   x,   object,   pch = object@Prefs@Graph.prefs$pch,   col = object@Prefs@Graph.prefs$col,   cex = object@Prefs@Graph.prefs$cex,   abllty = object@Prefs@Graph.prefs$abllty,   abllwd = object@Prefs@Graph.prefs$abllwd,   ablcol = object@Prefs@Graph.prefs$ablcol,   grid = object@Prefs@Graph.prefs$grid,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default QQ panel function for Xpose 4 — xpose.panel.qq","text":"x Name(s) x-variable. object xpose.data object. pch Plot character use. col Colour lines plot symbols. cex Amount scale plotting character . abllty Line type. abllwd Line width. ablcol Line colour. grid logical value indicating whether visual reference grid added graph. (use arguments line type, color etc). ... arguments may needed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default QQ panel function for Xpose 4 — xpose.panel.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.splom.html","id":null,"dir":"Reference","previous_headings":"","what":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","title":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","text":"scatterplot matrix panel function Xpose 4. intended ised outside xpose.plot.splom function. arguments take default values xpose.data object can overridden supplying argument xpose.plot.splom.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.splom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","text":"","code":"xpose.panel.splom(   x,   y,   object,   subscripts,   onlyfirst = TRUE,   inclZeroWRES = FALSE,   type = \"p\",   col = object@Prefs@Graph.prefs$col,   pch = object@Prefs@Graph.prefs$pch,   cex = object@Prefs@Graph.prefs$cex,   lty = object@Prefs@Graph.prefs$lty,   lwd = object@Prefs@Graph.prefs$lwd,   smooth = TRUE,   smlwd = object@Prefs@Graph.prefs$smlwd,   smlty = object@Prefs@Graph.prefs$smlty,   smcol = object@Prefs@Graph.prefs$smcol,   smspan = object@Prefs@Graph.prefs$smspan,   smdegr = object@Prefs@Graph.prefs$smdegr,   lmline = NULL,   lmlwd = object@Prefs@Graph.prefs$lmlwd,   lmlty = object@Prefs@Graph.prefs$lmlty,   lmcol = object@Prefs@Graph.prefs$lmcol,   grid = object@Prefs@Graph.prefs$grid,   groups = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.splom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","text":"x Name(s) x-variable. y Name(s) y-variable. object xpose.data object. subscripts standard Trellis subscripts argument (see xyplot) onlyfirst Logical value indicating whether first row per individual included plot. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. col color lines points. Specified integer text string. full list obtained R command colours(). default blue (col=4). pch plotting character, symbol, use. Specified integer. See R help points. default open circle. cex amount plotting text symbols scaled relative default. 'NULL' 'NA' equivalent '1.0'. lty line type. Line types can either specified integer (0=blank, 1=solid, 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash) one character strings '\"blank\"', '\"solid\"', '\"dashed\"', '\"dotted\"', '\"dotdash\"', '\"longdash\"', '\"twodash\"', '\"blank\"' uses 'invisible lines' (.e., draw ). lwd width lines. Specified integer. default 1. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. smlwd Line width x-y smooth. smlty Line type x-y smooth. smcol Line color x-y smooth. smspan smoothness parameter x-y smooth. default 0.667. argument panel.loess. smdegr degree polynomials used x-y smooth, 2. default 1. argument panel.loess. lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) lmlwd Line width lmline. lmlty Line type lmline. lmcol Line colour lmline. grid logical value indicating whether visual reference grid added graph. (use arguments line type, color etc). groups Name variable used superpose plots. ... arguments may needed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.splom.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"wrapper function lattice bwplot function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"","code":"xpose.plot.bw(   x,   y,   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   panel = xpose.panel.bw,   groups = NULL,   ids = FALSE,   logy = FALSE,   logx = FALSE,   aspect = object@Prefs@Graph.prefs$aspect,   funy = NULL,   funx = NULL,   PI = FALSE,   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   ordby = object@Prefs@Graph.prefs$ordby,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   subset = xsubset(object),   main = xpose.create.title(x, y, object, subset, funx, funy, ...),   xlb = xpose.create.label(x, object, funx, logx, ...),   ylb = xpose.create.label(y, object, funy, logy, ...),   scales = list(),   suline = object@Prefs@Graph.prefs$suline,   binvar = NULL,   bins = 10,   mirror = FALSE,   max.plots.per.page = 4,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"x Name(s) x-variable. y Name(s) y-variable. object xpose.data object. inclZeroWRES logical value indicating whether rows WRES=0 plotted. onlyfirst logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. panel name panel function use. cases left xpose.panel.bw. groups string name grouping variable (used groups argument panel.xyplot. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab Xpose data variable). logy Logical value indicating whether y-axis logarithmic. logx Logical value indicating whether x-axis logarithmic. aspect aspect ratio display (see bwplot). funy String name function apply y-variable plotting, e.g. \"abs\". funx String name function apply x-variable plotting, e.g. \"abs\". PI Either \"lines\", \"area\" \"\" specifying whether prediction intervals (lines, shaded area ) computed data SData added display. NULL means prediction interval. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. ordby string name variable used reorder factor conditioning variables (). variable used call reorder function. byordfun name function used reordering factor conditioning variable (see argument ordby). shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum) strip name function used strip argument bwplot. subset string giving subset expression applied data plotting. See xsubset. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. scales list used scales argument bwplot. suline string giving variable used construct smooth superpose display. NULL none. argument used want add superpose line variable present y list variables. binvar Variable used binning. bins number bins used. default 10. mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. max.plots.per.page maximum number plots per page can created mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument bwplot used. ... arguments passed xpose.panel.bw.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Box & whisker plot of WRES vs PRED xpose.plot.bw(\"WRES\", \"PRED\", xpdb5, binvar=\"PRED\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":null,"dir":"Reference","previous_headings":"","what":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"function wrapper lattice xyplot function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"","code":"xpose.plot.default(   x,   y,   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   panel = xpose.panel.default,   groups = object@Prefs@Xvardef$id,   ids = object@Prefs@Graph.prefs$ids,   logy = FALSE,   logx = FALSE,   yscale.components = \"default\",   xscale.components = \"default\",   aspect = object@Prefs@Graph.prefs$aspect,   funx = NULL,   funy = NULL,   iplot = NULL,   PI = NULL,   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   ordby = object@Prefs@Graph.prefs$ordby,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   by.interval = NULL,   strip = function(...) {      strip.default(..., strip.names = c(TRUE, TRUE))  },   use.xpose.factor.strip.names = TRUE,   subset = xsubset(object),   autocorr = FALSE,   main = xpose.create.title(x, y, object, subset, funx, funy, ...),   xlb = xpose.create.label(x, object, funx, logx, autocorr.x = autocorr, ...),   ylb = xpose.create.label(y, object, funy, logy, autocorr.y = autocorr, ...),   scales = list(),   suline = object@Prefs@Graph.prefs$suline,   bwhoriz = object@Prefs@Graph.prefs$bwhoriz,   dilution = FALSE,   dilfrac = object@Prefs@Graph.prefs$dilfrac,   diltype = object@Prefs@Graph.prefs$diltype,   dilci = object@Prefs@Graph.prefs$dilci,   seed = NULL,   mirror = FALSE,   max.plots.per.page = 4,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"x string vector strings name(s) x-variable(s). y string vector strings name(s) y-variable(s). object \"xpose.data\" object. inclZeroWRES logical value indicating whether rows WRES=0 plotted. onlyfirst logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. panel name panel function use. groups string name grouping variable (used groups argument panel.xyplot. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). logy Logical value indicating whether y-axis logarithmic. logx Logical value indicating whether x-axis logarithmic. yscale.components Used change way axis look logy used. Can user defined function link{xpose.yscale.components.log10}.  axes log transformed yscale.components.default used. xscale.components Used change way axis look logx used. Can user defined function link{xpose.xscale.components.log10}.  axes log transformed xscale.components.default used. aspect aspect ratio display (see xyplot). funx String name function apply x-variable plotting, e.g. \"abs\". funy String name function apply y-variable plotting, e.g. \"abs\". iplot individual plots matrix? Internal use . PI Either \"lines\", \"area\" \"\" specifying whether prediction intervals (lines, shaded area ) computed data SData added display. NULL means prediction interval. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. ordby string name variable used reorder factor conditioning variables (). variable used call reorder.factor function. byordfun name function used reordering factor conditioning variable (see argument ordby) shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum) .interval intervals use conditioning continuous variable . strip name function used strip argument xyplot. easy way change strip appearance use strip.custom.  example, want change text strips can use strip=strip.custom(factor.levels=c(\"Hi\",\"\")) variable factor strip=strip.custom(var.name=c(\"New Name\")) variable continuous. use.xpose.factor.strip.names Use factor names strips conditioning plots.. subset string giving subset expression applied data plotting. See xsubset. autocorr autocorrelation plot?  Values can TRUE/FALSE. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. scales list used scales argument xyplot. suline string giving variable used construct smooth superpose display. NULL none. argument used want add superpose line variable present y list variables. bwhoriz logical value indicating box whiskers bars plotted horizontally . Used x-variable(s) categorical. dilution Logical value indicating whether data dilution used. dilfrac Dilution fraction indicating expected fraction individuals display plots. exact meaning depends type dilution (see ). diltype Indicating type dilution apply. NULL means random dilution without stratification. nonNULL value means stratified dilution. dilci number 0 1 giving range eligible dilution stratified dilution (see ). seed Seed number used random dilution. NULL means seed. mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. max.plots.per.page maximum number plots per page can created mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument xyplot used. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"Returns xyplot graph object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"y must numeric (continuous) x can either numeric factor. x numeric regular xy-plot drawn. x factor, hand, box whiskers plot constructed. x y can either single valued strings vector strings. x y can vectors call function. ids TRUE, text labels added plotting symbols. labels taken idlab xpose data variable. way text labels plotted governed idsmode argument (passed panel function). idsmode=NULL (default) means extreme data points labelled non-NULL value adds labels data points (default Xpose 3). xpose.panel.default identifies extreme data points fitting loess smooth (y~x) looking residuals fit. Points associated highest/lowest residuals labelled. \"High\" \"low\" judged panel function parameter idsext, gives fraction total number data points judged extreme \"\" \"\" direction. default value idsext 0.05 (see xpose.prefs-class). also possibility label high low extreme points. done idsdir argument xpose.panel.default. value \"\" (default) means high low extreme points labelled \"\" \"\" labels high low extreme points respectively. Data dilution useful situations excessive amount data. xpose.plot.default can dilute data two different ways. first completely random dilution individuals eligible exclusion plot. case argument dilfrac determines fraction individuals excluded plot. second type dilution uses stratification make sure none extreme individuals omitted plot. Extreme individuals identified similar manner extreme data points identified text labelling. smooth fitted data extreme residuals fit used inform extremeness. judged extreme determined argument dilci, defaults 0.95 (Note meaning opposite idsext). dilci give confidence level interval around fitted curve outside points deemed extreme. Extreme individuals least one point \"extremeness\" interval. Individuals extreme points eligible dilution dilfrac give number omitted graph. means dilfrac usually grater stratified dilution completely random dilution. smooths added diluted plot based undiluted data. graphical parameters may passed xpose.panel.default.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## A spaghetti plot of DV vs TIME xpose.plot.default(\"TIME\", \"DV\", xpdb5)   ## A conditioning plot xpose.plot.default(\"TIME\", \"DV\", xpdb5, by = \"SEX\")   ## Multiple x-variables xpose.plot.default(c(\"WT\", \"SEX\"), \"CL\", xpdb5)  ## Multiple y-variables xpose.plot.default(\"WT\", c(\"CL\", \"V\"), xpdb5) xpose.plot.default(\"WT\", c(\"CL\", \"V\"), xpdb5, by=c(\"SEX\", \"HCTZ\"))  ## determining the interval for the conditioning variable wt.ints <- matrix(c(50,60,60,70,70,80,80,90,90,100,100,150),nrow=6,ncol=2,byrow=T) xpose.plot.default(\"TIME\",\"DV\",xpdb5,by=\"WT\", by.interval=wt.ints) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"function wrapper lattice xyplot function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"","code":"xpose.plot.histogram(   x,   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   type = \"density\",   aspect = object@Prefs@Graph.prefs$aspect,   scales = list(),   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   ordby = object@Prefs@Graph.prefs$ordby,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   subset = xsubset(object),   main = xpose.create.title.hist(x, object, subset, ...),   xlb = NULL,   ylb = \"Density\",   hicol = object@Prefs@Graph.prefs$hicol,   hilty = object@Prefs@Graph.prefs$hilty,   hilwd = object@Prefs@Graph.prefs$hilwd,   hidcol = object@Prefs@Graph.prefs$hidcol,   hidlty = object@Prefs@Graph.prefs$hidlty,   hidlwd = object@Prefs@Graph.prefs$hidlwd,   hiborder = object@Prefs@Graph.prefs$hiborder,   mirror = FALSE,   max.plots.per.page = 4,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"x string vector strings name(s) x-variable(s). object \"xpose.data\" object. inclZeroWRES logical value indicating whether rows WRES=0 plotted. onlyfirst logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. type type histogram make.  See histogram. aspect aspect ratio display (see histogram). scales list used scales argument histogram. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. ordby string name variable used reorder factor conditioning variables (). variable used call reorder.factor function. byordfun name function used reordering factor conditioning variable (see argument ordby) shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum) strip name function used strip argument xyplot. subset string giving subset expression applied data plotting. See xsubset. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. hicol fill colour histogram - integer string.  default blue (see histogram). hilty border line type histogram - integer.  default 1 (see histogram). hilwd border line width histogram - integer.  default 1 (see histogram). hidcol fill colour density line - integer string. default black (see histogram). hidlty border line type density line - integer.  default 1 (see histogram). hidlwd border line width density line - integer.  default 1 (see histogram). hiborder border colour histogram - integer string. default black (see histogram). mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. max.plots.per.page maximum number plots per page can created mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument xyplot used. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"Returns histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"x can either numeric factor, can either single valued strings vectors strings.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  xpose.plot.histogram(\"AGE\", xpdb5, onlyfirst = TRUE) xpose.plot.histogram(c(\"SEX\", \"AGE\"), xpdb5, onlyfirst = TRUE)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"The generic Xpose functions for QQ plots — xpose.plot.qq","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"wrapper function lattice qqmath function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"","code":"xpose.plot.qq(   x,   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   aspect = object@Prefs@Graph.prefs$aspect,   scales = list(),   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   ordby = object@Prefs@Graph.prefs$ordby,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   subset = xsubset(object),   main = xpose.create.title.hist(x, object, subset, ...),   xlb = \"Quantiles of Normal\",   ylb = paste(\"Quantiles of \", xlabel(x, object), sep = \"\"),   pch = object@Prefs@Graph.prefs$pch,   col = object@Prefs@Graph.prefs$col,   cex = object@Prefs@Graph.prefs$cex,   abllty = object@Prefs@Graph.prefs$abllty,   abllwd = object@Prefs@Graph.prefs$abllwd,   ablcol = object@Prefs@Graph.prefs$ablcol,   mirror = FALSE,   max.plots.per.page = 4,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"x string vector strings name(s) x-variable(s). object \"xpose.data\" object. inclZeroWRES logical value indicating whether rows WRES=0 plotted. onlyfirst logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. aspect aspect ratio display (see qqmath). scales list used scales argument qqmath. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. ordby string name variable used reorder factor conditioning variables (). variable used call reorder function. byordfun name function used reordering factor conditioning variable (see argument ordby). shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum). strip name function used strip argument xyplot. subset string giving subset expression applied data plotting. See xsubset. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. pch Plotting symbol. col Color plotting symbol. cex Amount scale plotting character . abllty Line type qqline. abllwd Line width qqline. ablcol Color qqline. mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. max.plots.per.page maximum number plots per page can created mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument qqmath used. ... arguments passed xpose.plot.qq.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## A QQ plot of WRES xpose.plot.qq(\"WRES\", xpdb5)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":null,"dir":"Reference","previous_headings":"","what":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"function wrapper lattice splom function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"","code":"xpose.plot.splom(   plist,   object,   varnames = NULL,   main = \"Scatterplot Matrix\",   xlb = NULL,   ylb = NULL,   scales = list(),   onlyfirst = TRUE,   inclZeroWRES = FALSE,   subset = xsubset(object),   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   include.cat.vars = FALSE,   ordby = NULL,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   groups = NULL,   ids = object@Prefs@Graph.prefs$ids,   smooth = TRUE,   lmline = NULL,   panel = xpose.panel.splom,   aspect = object@Prefs@Graph.prefs$aspect,   samp = NULL,   max.plots.per.page = 4,   mirror = FALSE,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"plist vector strings containing variable names scatterplot matrix. object \"xpose.data\" object. varnames vector strings containing labels variables scatterplot matrix. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. scales list used scales argument xyplot. onlyfirst logical value indicating whether first row per individual included plot. inclZeroWRES logical value indicating whether rows WRES=0 plotted. subset string giving subset expression applied data plotting. See xsubset. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. include.cat.vars Logical value. ordby string name variable used reorder factor conditioning variables (). variable used call reorder.factor function. byordfun name function used reordering factor conditioning variable (see argument ordby) shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum) strip name function used strip argument xyplot. groups string name grouping variable (used groups argument panel.xyplot. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) panel name panel function use. aspect aspect ratio display (see xyplot). samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. max.plots.per.page maximum number plots per page can created mirror plots. mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument qqmath used. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"Returns scatterplot matrix graph object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"ids TRUE, text labels added plotting symbols. labels taken idlab xpose data variable. way text labels plotted governed idsmode argument (passed panel function). idsmode=NULL (default) means extreme data points labelled non-NULL value adds labels data points (default Xpose 3). xpose.panel.default identifies extreme data points fitting loess smooth (y~x) looking residuals fit. Points associated highest/lowest residuals labelled. \"High\" \"low\" judged panel function parameter idsext, gives fraction total number data points judged extreme \"\" \"\" direction. default value idsext 0.05 (see link{xpose.prefs-class}). also possibility label high low extreme points. done idsdir argument xpose.panel.default. value \"\" (default) means high low extreme points labelled \"\" \"\" labels high low extreme points respectively. graphical parameters may passed xpose.panel.splom. example, want adjust size varnames axis tick labels can use parameters varname.cex=0.5 axis.text.cex=0.5.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## CL, WT, HT, SEX with a regression line xpose.plot.splom(c(\"CL\", \"WT\", \"HT\", \"SEX\"), xpdb5, lmline = TRUE)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.prefs-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"object \"xpose.prefs\" class holds information variable graphical preferences particular \"xpose.data\" object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.prefs-class.html","id":"objects-from-the-class","dir":"Reference","previous_headings":"","what":"Objects from the Class","title":"Class ","text":"Objects can created calls form new(\"xpose.prefs\",...) usually necessary since \"xpose.prefs\" object created time \"xpose.data\" object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.prefs-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Class ","text":"Niclas Jonsson & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize an xpose database — xpose.print","title":"Summarize an xpose database — xpose.print","text":"Summarize xpose database","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize an xpose database — xpose.print","text":"","code":"xpose.print(object, long = TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize an xpose database — xpose.print","text":"object xpose data object long long format .","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize an xpose database — xpose.print","text":"\"\"","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize an xpose database — xpose.print","text":"","code":"xpose.print(simpraz.xpdb) #> The database contains the following observed items: #> ID TIME IPRED IWRES CWRES CL V KA ETA1 ETA2 ETA3 AGE HT WT  #> SECR SEX RACE SMOK HCTZ PROP CON OCC DV PRED RES WRES #>  #> The following variables are defined: #>  #> ID variable: ID  #> Label variable: ID  #> Independent variable: TIME  #> Occasion variable: OCC  #> Dependent variable: DV  #> Population prediction variable: PRED  #> Individual prediction variable: IPRED  #> Weighted population residual variable: WRES  #> Weighted individual residual variable: IWRES  #> Population residual variable: RES  #> Parameters: ETA3 ETA2 ETA1 KA V CL #> Covariates: SEX RACE SMOK HCTZ PROP CON OCC AGE HT WT SECR #>   ( Continuous: AGE HT WT SECR ) #>   ( Categorical: SEX RACE SMOK HCTZ PROP CON OCC ) #> Variability parameters: ETA1 ETA2 ETA3 #> Missing value label: -99"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.string.print.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a pretty string. — xpose.string.print","title":"Print a pretty string. — xpose.string.print","text":"Print string certain number characters per row.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.string.print.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a pretty string. — xpose.string.print","text":"","code":"xpose.string.print(value, fill = 60, file = \"\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.string.print.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a pretty string. — xpose.string.print","text":"value text print. fill wide text per row. file print. \"\" means screen.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.string.print.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a pretty string. — xpose.string.print","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"functions used create standard tic marks axis labels axes log scale.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"","code":"xpose.logTicks(lim, loc = c(1, 5))  xpose.yscale.components.log10(lim, ...)  xpose.xscale.components.log10(lim, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"lim Limits loc Locations ... Additional arguments passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"functions create log scales look like (default R scales). functions used input xscale.components argument lattice plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"xpose.logTicks(): Make log tic marks xpose.xscale.components.log10(): Make log scale x-axis","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"","code":"if (FALSE) { xpdb5 <- xpose.data(5) xpose.plot.default(\"PRED\",\"DV\",xpdb,logy=T,logx=T) xpose.plot.default(\"PRED\",\"DV\",xpdb,logy=T,logx=T,                    yscale.components = xpose.yscale.components.log10,                    xscale.components = xpose.xscale.components.log10)  ## both give the same result }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The Xpose Package — xpose4-package","title":"The Xpose Package — xpose4-package","text":"Xpose R-based model building aid population analysis using NONMEM.  facilitates data set checkout, exploration visualization, model  diagnostics, candidate covariate identification model comparison.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Xpose Package — xpose4-package","text":"Xpose takes output NONMEM output /PsN output generates graphs analyses. assumed NONMEM run can uniquely identified run number (see section generate appropriate input Xpose). Xpose implemented using lattice graphics library. Xpose package can divided six subsections (functions associated different subsections linked \"See Also\" section): Data Functions Functions managing          input data manipulating Xpose database. Generic Functions Generic wrapper functions around lattice          functions. functions can invoked user require quite          detailed instructions generate desired output. Specific Functions functions single purpose functions        generate specific output given Xpose database input.        behavior can, extent, influenced user. Classic Functions Xpose text based menu interface make       simple user invoke Xpose specific functions. interface       called Xpose Classic. Given limitations text based interface imposes,       Xpose Classic flexible may useful quick assessment       model learning use Xpose. PsN Functions functions interface Xpose        PsN, .e. post-process NONMEM output rather PsN output. GAM Functions Functions take Xpose object performs generalized additive model         (GAM) stepwise search influential covariates single model parameter.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"how-to-make-nonmem-generate-input-to-xpose","dir":"Reference","previous_headings":"","what":"How to make NONMEM generate input to Xpose","title":"The Xpose Package — xpose4-package","text":"Xpose recognizes NONMEM    runs, files associated particular run, though run number.    number used name NONMEM model files, output   files table files.  fundamental input Xpose one   NONMEM table files.  table files named followed   run number, example xptab1 run number 1.  Xpose looks files   according following pattern, * run number: sdtab* Standard table file, containing ID, IDV, DV, PRED, IPRED,    WRES, IWRES, RES, IRES, etc. patab* Parameter table, containing model parameters - THETAs, ETAs    EPSes catab* Categorical covariates, e.g. SEX, RACE cotab* Continuous covariates, e.g. WT, AGE extra*, mutab*, mytab*, xptab*, cwtab* variables might   need available Xpose run*.mod Model specification file run*.lst NONMEM output Strictly, one table file needed xpose (example sdtab*    xptab*).  However, using patab*, cotab*, catab* influence way   Xpose interprets data recommended get full benefit    Xpose. can use code NONMEM similar following generate tables    need.  NONMEM automatically appends DV, PRED, WRES RES unless    NOAPPEND specified.  forget leave least one blank line   end NONMEM model specification file. $TABLE ID TIME IPRED IWRES EVID MDV NOPRINT ONEHEADER FILE=sdtab1    $TABLE ID CL V2 KA K SLP KENZ NOPRINT ONEHEADER FILE=patab1    $TABLE ID WT HT AGE BMI PKG NOPRINT ONEHEADER FILE=cotab1    $TABLE ID SEX SMOK ALC NOPRINT ONEHEADER FILE=catab1","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Xpose Package — xpose4-package","text":"PsN","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The Xpose Package — xpose4-package","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Xpose Package — xpose4-package","text":"","code":"if (FALSE) { # run the classic interface library(xpose4) xpose4()    # command line interface   library(xpose4) xpdb <- xpose.data(5) basic.gof(xpdb) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4.html","id":null,"dir":"Reference","previous_headings":"","what":"Classic menu system for Xpose 4 — xpose4","title":"Classic menu system for Xpose 4 — xpose4","text":"Classic menu system Xpose 4","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Classic menu system for Xpose 4 — xpose4","text":"","code":"xpose4()"},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Classic menu system for Xpose 4 — xpose4","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Classic menu system for Xpose 4 — xpose4","text":"","code":"if (FALSE) { xpose4() }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or set the value of the Subset slot. — xsubset","title":"Extract or set the value of the Subset slot. — xsubset","text":"Extract set value Subset slot \"xpose.data\" object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or set the value of the Subset slot. — xsubset","text":"","code":"xsubset(object)  xsubset(object) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or set the value of the Subset slot. — xsubset","text":"object \"xpose.data\" object. value string subset expression.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or set the value of the Subset slot. — xsubset","text":"string representing subset expression.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract or set the value of the Subset slot. — xsubset","text":"subset string syntax subset argument , e.g. panel.xyplot. Note, however, \"xpose.data\" subset used argument panel.xyplot. intended subset argument Data SData functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract or set the value of the Subset slot. — xsubset","text":"xsubset(object) <- value: assign value string representing subset expression","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or set the value of the Subset slot. — xsubset","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or set the value of the Subset slot. — xsubset","text":"","code":"xpdb <- simpraz.xpdb xsubset(xpdb) <- \"DV > 0\" xsubset(xpdb) #> [1] \"DV > 0\""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract and set Xpose variable definitions. — xvardef","title":"Extract and set Xpose variable definitions. — xvardef","text":"function extracts set Xpose variable definitions \"xpose.data\" objects.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract and set Xpose variable definitions. — xvardef","text":"","code":"xvardef(x, object)  xvardef(object) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract and set Xpose variable definitions. — xvardef","text":"x name xpose variable (see ). object xpose.data object. value two element vector first element name variable second column name Data slot object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract and set Xpose variable definitions. — xvardef","text":"Returns string name data variable defined Xpose data variable.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract and set Xpose variable definitions. — xvardef","text":"Xpose variable definitions used map particular variable types column names data.frame Data slot \"xpose.data\" object. single-valued Xpose variable definitions : id, idlab, idv, occ, dv, pred, ipred, iwres, res. (potentially) vector-valued Xpose variable definitions : parms, covariates, ranpar, tvparms (parameters, covariates, random effects parameters=etas, typical value parameters). default values can found createXposeClasses function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract and set Xpose variable definitions. — xvardef","text":"xvardef(object) <- value: reset column label dv points Data slot xpose database object","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract and set Xpose variable definitions. — xvardef","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract and set Xpose variable definitions. — xvardef","text":"","code":"xpdb <- simpraz.xpdb  ## get the column name in the Data slot of object xpdb ## corresponding to the label dv xvardef(\"dv\", xpdb) #> [1] \"DV\"  ## reset the which column the label dv points to in the Data slot of ## object xpdb xvardef(xpdb) <- c(\"dv\", \"DVA\")"},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-471","dir":"Changelog","previous_headings":"","what":"xpose4 4.7.1","title":"xpose4 4.7.1","text":"CRAN release: 2020-12-18 Fix bug filtering “-99” rows table files filter plotting variables. Fix bug items catab cotab files added list covariates xpose database (#16). Fix bug csv files improperly read database situations (#16). Updated code using depreciated dplyr tibble functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-470","dir":"Changelog","previous_headings":"","what":"xpose4 4.7.0","title":"xpose4 4.7.0","text":"CRAN release: 2020-02-27 allow changes relative length censored lines kaplan.plot(). Handle directory without trailing slash xpose.data() (#14, @rikardn). fix bug classic menu system allowing change variable definitions. Various small spelling bug fixes (#13, @vrognas).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-461","dir":"Changelog","previous_headings":"","what":"xpose4 4.6.1","title":"xpose4 4.6.1","text":"CRAN release: 2018-03-08 Updates comply changes readr gam packages.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-460","dir":"Changelog","previous_headings":"","what":"xpose4 4.6.0","title":"xpose4 4.6.0","text":"CRAN release: 2017-06-17 Update xpose.VPC() outliers can identified plotted. Update xpose.VPC() lines VPC plotted median observed values X axis bins default. Update xpose.VPC() allow rug bottom plot showing bins located. Update namespace lattice loaded loading xpose. documentation now written roxygen Updates boot GAM boot SCM plots documentation. Various small bug fixes.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-453","dir":"Changelog","previous_headings":"","what":"xpose4 4.5.3","title":"xpose4 4.5.3","text":"CRAN release: 2014-11-24 Update ind.plots() allow subsets per-y-variable basis. Useful show IPRED PRED finer grid DV. See option “y.vals.subset”. Update axes limits computed xpose.plot.default. Fix using expression() ylb argument xpose.VPC. Various small bug fixes.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-452","dir":"Changelog","previous_headings":"","what":"xpose4 4.5.2","title":"xpose4 4.5.2","text":"Internal release Updates read.bootscm.par.est()","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-451","dir":"Changelog","previous_headings":"","what":"xpose4 4.5.1","title":"xpose4 4.5.1","text":"Internal release Updated xpose.gam work latest version gam package Updated kaplan.plot allow ylim specification “cov” argument used. Updated compute.cwres associated functions work NONMEM 7. Fixed warnings created xpose.VPC.categorical creating personalized x y axis labels.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-450","dir":"Changelog","previous_headings":"","what":"xpose4 4.5.0","title":"xpose4 4.5.0","text":"CRAN release: 2014-05-20 External release just one package instead five. Added ind.plots one data point individual, PRED IPRED show plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-443","dir":"Changelog","previous_headings":"","what":"xpose4 4.4.3","title":"xpose4 4.4.3","text":"Internal release Added functionality plotting delta mean output vpc tool PsN. Option xpose.VPC() can turned using PI.delta.mean=T. See ?xpose.panel.default information.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-442","dir":"Changelog","previous_headings":"","what":"xpose4 4.4.2","title":"xpose4 4.4.2","text":"Internal release Removed default messages print screen running xpose.VPC(). can change back previous behavior option verbose=TRUE. Combined five packages xpose one package. Updated Histogram functionality. New plots randtest.hist() boot.hist() creating histograms results PsN’s ‘randtest’ ‘bootstrap’ tools. Updated xpose.VPC() function handle plotting mean values simulations.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-441","dir":"Changelog","previous_headings":"","what":"xpose4 4.4.1","title":"xpose4 4.4.1","text":"CRAN release: 2013-08-13 Updates kaplan.plot.R (thanks Leonid Gibiansky reporting problems) kaplan.plot.R: Removed debugging command mistakenly left function kaplan.plot.R: “ylab” argument now passed plot cov option used. kaplan.plot.R: Using cov option repeated censoring observations break chain mean value calculation wrong (used surviving IDs last censored ID). Fixed now. Changed “aspect” argument plots default “fill”. Previously “1”.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-440","dir":"Changelog","previous_headings":"","what":"xpose4 4.4.0","title":"xpose4 4.4.0","text":"CRAN release: 2012-10-17 Added bootstrap GAM diagnostics boostrap PsN function boot_scm.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-436","dir":"Changelog","previous_headings":"","what":"xpose4 4.3.6","title":"xpose4 4.3.6","text":"fixed plot classic menu system “Weighted residuals vs covariates”.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-435","dir":"Changelog","previous_headings":"","what":"xpose4 4.3.5","title":"xpose4 4.3.5","text":"CRAN release: 2012-04-19 Updated help files workable examples, example dataset. Look data(simpraz.xpdb), simprazExample() example(xpose.data) dataset examples example(basic.gof) example(cwres.vs.idv) plot examples. xpose4specific functions now examples can run example(). Updated kaplan.plot() kaplan-Meier mean covariate (KMMC) plot can created. Also added options adjusting plot properties. New gofSetup() command create customized series GOF plots. fixed RSE values reported runsum() parameter fixed. Fixed argument xpose.VPC.categorical(max.plots.per.page=1), one plot per page possible. Fixed xpose.VPC() psn option vpc “confidence_interval=X” works. Fixed compute.cwres() function wasn’t computing anything (returning error).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-432","dir":"Changelog","previous_headings":"","what":"xpose4 4.3.2","title":"xpose4 4.3.2","text":"CRAN release: 2010-11-30 Fixed bug xpose.VPC asking logx=T (didn’t work previously). Fixed dOFV.vs.id ties individual dOFV drops.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-430","dir":"Changelog","previous_headings":"","what":"xpose4 4.3.0","title":"xpose4 4.3.0","text":"CRAN release: 2010-10-23 Updated read.nm.tables comma separated NONMEM 7 files can read Xpose. Changing behavior xpose.multiple.plot.default. Now multiple plots returned objects just like single plots (automatic printing function created plot list). accomplished defining new class - xpose.multiple.plots - corresponding print show methods class. Updated xpose.VPC, xpose.VPC.categorical xpose.VPC.handle new format PsN vpc_results.csv files. xpose.VPC.categorical now new option: censored (T F) create BLOQ VPC plots TRUE. xpose.VPC.tries combine continuous categorical BLOQ plots. page numbers can turned multiple page plots using page.numbers option (T F).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-410","dir":"Changelog","previous_headings":"","what":"xpose4 4.1.0","title":"xpose4 4.1.0","text":"Updated ind.plots(), function much flexible now. Added graphical options xpose.VPC.categorical() Fixed logy=T option xpose.VPC(Pi.ci=T,logy=T). Fixed logy=T logx=T option (bug resulting error). VPC changed require y-axis continuous default. Fixed classic version parm.vs.parm() plot. Fixed runsum(). Previous version line line model file. Added new function change.xvardef(), replaces much previous change functions. Thanks Sebastien Bihorel input helped create function. Added ability apply functions x-axis plots. function options now called funx funy. Added support reading NONMEM 7 table output files. Added functions odd type (categorical, TTE, count) plots including VPCs. Updated handling PsN vpc output file Updated interpretation categories xpose.VPC.categorical()","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-404","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.4","title":"xpose4 4.0.4","text":"cwres.vs.pred.bw() fixed. Previously cwres.vs.pred.bw() gave result cwres.vs.idv.bw(). Fixed xpose.VPC() bug causing plots created situations. Added functionality xpose.VPC() users can define titles subplot stratification used VPC. see ?xpose.VPC info. Updated method opening graphical devices windows consistent new methods used R version 2.8.0. Added functionality allow user plot vertical horizontal lines histograms. See ?xpose.panel.histogram information. Fixed small bug xpose.panel.splom().","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-403","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.3","title":"xpose4 4.0.3","text":"compute.cwres() debugging flag left file resulting R going debugging mode function called. fixed.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-402","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.2","title":"xpose4 4.0.2","text":"Added ability smooth PI.ci “area” plots match “line” plots. See ‘PI.ci.area.smooth’ xpose.panel.default() Added ‘logx’ ‘logy’ functionality PI plots. Changed par.summary cov.summary routines removed functions almost thing (adding functionality current functions). fixed GAM plot problems xp.plot() added support GAM command line. Fixed problem ind.plots() ID variable called ID. Changed functions xpose4specific began “abs.” begin “absval.” consistent rules generic function definitions R. Changed name add.abs() add.absval(). Changed name par.summary() parm.summary(). Changed name param.vs.cov() parm.vs.cov(). Changed name param.vs.param() parm.vs.parm().","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-401","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.1","title":"xpose4 4.0.1","text":"Added functionality visual predictive checks Added functionality numerical predictive checks","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40037","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.7","title":"xpose4 4.0.0.3.7","text":"Added generic functions xpose.draw.table, xpose.draw.cell, xpose.get.c xpose.get.r drawing tables using graphics device (JW) Added specific function param.table display parameter estimates using graphics device (e.g. PDF file) (JW) Added additional specific functions : Added additional specific functions: IWRES distribution (histogram) (iwres.dist.hist) Added additional specific functions: IWRES distribution (QQ) (iwres.dist.qq) Added additional specific functions: ETA distribution (histogram) (ranpar.dist.hist) Added additional specific functions: ETA scatter-plot matrices (ranpar.splom) Added additional specific functions: ETAs vs covariates (ranpar.vs.cov) Added additional specific functions: Parameter tables graphics device (param.table) Updated compute.cwres function work without xpose 4 Just ‘source’ file (compute.cwres.R) work (AH) fixed problems run summary function (AH) added new general class printing multiple plot objects page (AH) Fixed bug plotting results GAM (AH)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40035","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.5","title":"xpose4 4.0.0.3.5","text":"Bugs ‘groups’ argument fixed xpose.plot.default, dv.vs.pred.ipred, dv.preds.vs.idv (multiple values x y properly handled) (JW) File devices (e.g. pdf, postscript, etc) now work correctly functions (JW) Bug multiple-page covariate plots fixed (first page display) (JW) Bug reading table files sometimes leave file debris, interfere reading subsequent data - fixed (JW) Bug covariate checking sometimes cause plot functions fail (e.g. abs.wres.vs.pred..cov) - fixed (JW) Bug classic menu system prevented display plots - fixed (JW) Bug classic menu system prevented display plots - fixed (JW) Bug CWRES calculation fixed (AH) Bug parameter histogram display fixed (JW) Missing values (defaults -99) now handled correctly (JW) QQ plots longer display categorical variables (JW)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40033","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.3","title":"xpose4 4.0.0.3.3","text":"Bug ‘subset’ argument individual plots corrected (JW)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40032","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.2","title":"xpose4 4.0.0.3.2","text":"Online documentation cleaned (JW) Numerous small bugs fixed (JW) *nix support added (JW) Multipage plots now create stacks display windows, rather stacks plots single window (JW) Scatter-plot matrices added (JW) QQ plots parameters covariates added (JW) Generic functions renamed consistency (JW)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40031","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.1","title":"xpose4 4.0.0.3.1","text":"Bugs CWRES application documentation fixed (AH) Bugs histogram functions fixed - lack defined covariates longer causes crash - customization options now work","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-4003","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3","title":"xpose4 4.0.0.3","text":"GAM added (AH) CWRES plots functions added (AH) gam package now required Known bugs corrected","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-4002","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.2","title":"xpose4 4.0.0.2","text":"SUBSET functionality fixed procedures Preferences, summaries data checkout implemented Box whisker plots now preferences tell ‘label’ function renamed ‘xlabel’ compatibility Hmisc package now required Many small additions tweaks R package functionality fixed","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40021","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.2.1","title":"xpose4 4.0.0.2.1","text":"Ind.plots.R updated (AH)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-4001","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.1","title":"xpose4 4.0.0.1","text":"Xpose 4 completely rewritten version Xpose 3.1, everything changed.","code":""}]
+[{"path":"http://uupharmacometrics.github.io/xpose4/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Andrew C. Hooker. Author, maintainer, copyright holder. Mats O. Karlsson. Author, copyright holder. Justin J. Wilkins. Author. E. Niclas Jonsson. Author, translator, copyright holder. Ron Keizer. Contributor.            functionality bootstrap GAM SCM","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Jonsson, E.N. & Karlsson, M.O. (1999) Xpose--S-PLUS based population   pharmacokinetic/pharmacodynamic model building aid NONMEM. Computer Methods  Programs Biomedicine. 58(1):51-64. Keizer RJ, Karlsson MO, Hooker AC (2013). “Modeling Simulation Workbench NONMEM: Tutorial Pirana, PsN, Xpose.” CPT: Pharmacometrics & Systems Pharmacology, 2(6). doi:10.1038/psp.2013.24.","code":"@Article{,   title = {Xpose--an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM},   journal = {Computer Methods and Programs in Biomedicine},   volume = {58},   number = {1},   pages = {51-64},   year = {1999},   author = {E. N. Jonsson and M. O. Karlsson},   doi = {10.1016/s0169-2607(98)00067-4}, } @Article{,   title = {Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose},   author = {Ron J Keizer and Mats O Karlsson and Andrew C Hooker},   journal = {CPT: Pharmacometrics & Systems Pharmacology},   year = {2013},   volume = {2},   number = {6},   doi = {10.1038/psp.2013.24}, }"},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"xpose-4-","dir":"","previous_headings":"","what":"Diagnostics for Nonlinear Mixed-Effect Models","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Andrew C. Hooker, Mats O. Karlsson E. Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Xpose 4 collection functions used model building aid nonlinear mixed-effects (population) analysis using NONMEM. facilitates data set checkout, exploration visualization, model diagnostics, candidate covariate identification model comparison.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"install xpose need R (>= version 2.2.0). install Xpose R use one following methods: latest stable release – CRAN. Write R command line: Latest development version – Github. Note command installs “master” (development) branch; want release branch Github add ref=\"release\" install_github() call.","code":"install.packages(\"xpose4\") # install.packages(\"devtools\") devtools::install_github(\"UUPharmacometrics/xpose4\")"},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"running-xpose-4","dir":"","previous_headings":"","what":"Running Xpose 4","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Start R load xpose: use classic menu system, type R command prompt: function independently available command line, Xpose library loaded. First create set files NONMEM run can import files Xpose Display goodness--fit plots:  Clean files created show examples: help available online documentation, can found typing (example) ?xpose4 R command line.","code":"library(xpose4) #> Loading required package: lattice xpose4() cur.files <- dir() # current files in temp directory  simprazExample() # write files from an example NONMEM run new.files <- dir()[!(dir() %in% cur.files)]  # the new files created by simprazExample xpdb <- xpose.data(1) basic.gof(xpdb) unlink(new.files)"},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"the-xpose-4-bestiary","dir":"","previous_headings":"","what":"The Xpose 4 Bestiary","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"detailed description Xpose example plots explanaitions functions package available Bestiarium: https://xpose.sourceforge.net/bestiarium_v1.0.pdf","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"dont-panic","dir":"","previous_headings":"","what":"Don’t Panic","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Andrew Hooker (andrew.hooker farmaci.uu.se) able get answer run trouble. website https://uupharmacometrics.github.io/xpose4/ also help.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"release-schedule","dir":"","previous_headings":"","what":"Release Schedule","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Bugfix releases released regularly, fixing problems found.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"Xpose 4 free software: can redistribute /modify terms GNU Lesser General Public License published Free Software Foundation, either version 3 License, (option) later version. program distributed hope useful, WITHOUT WARRANTY; without even implied warranty MERCHANTABILITY FITNESS PARTICULAR PURPOSE. See GNU Lesser General Public License details https://www.gnu.org/licenses/.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/index.html","id":"known-bugs","dir":"","previous_headings":"","what":"Known Bugs","title":"Diagnostics for Nonlinear Mixed-Effect Models","text":"None present, certainly . Report https://github.com/UUPharmacometrics/xpose4/issues.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"GAM functions for Xpose 4 — GAM_summary_and_plot","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"functions summarizing plotting results  generalized additive model within Xpose","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"","code":"xp.akaike.plot(   gamobj = NULL,   title = \"Default\",   xlb = \"Akaike value\",   ylb = \"Models\",   ... )  xp.cook(gam.object)  xp.ind.inf.fit(   gamobj = NULL,   plot.ids = TRUE,   idscex = 0.7,   ptscex = 0.7,   title = \"Default\",   recur = FALSE,   xlb = NULL,   ylb = NULL,   ... )  xp.ind.inf.terms(   gamobj = NULL,   xlb = NULL,   ylb = NULL,   plot.ids = TRUE,   idscex = 0.7,   ptscex = 0.7,   prompt = TRUE,   ... )  xp.ind.stud.res(   gamobj = NULL,   title = \"Default\",   recur = FALSE,   xlb = NULL,   ylb = NULL )  xp.plot(   gamobj = NULL,   plot.ids = TRUE,   idscex = 0.7,   ptscex = 0.7,   prompt = TRUE,   ... )  xp.summary(gamobj = NULL)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"gamobj GAM object use plot. null user asked choose list GAM objects memory. title text string indicating plot title. NULL, left blank. xlb text string indicating x-axis legend. NULL, left blank. ylb text string indicating y-axis legend. NULL, left blank. ... arguments passed GAM functions. gam.object GAM object (see gam. plot.ids Logical, specifies whether ID numbers displayed. idscex ID label size. ptscex Point size. recur dispersion used GAM object. prompt Specifies whether user prompted press RETURN plot pages. Default TRUE. object xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"Plots summaries.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"xp.akaike.plot(): Akaike plot results. xp.cook(): Individual parameters GAM fit. xp.ind.inf.fit(): Individual influence GAM fit. xp.ind.inf.terms(): Individual influence GAM terms. xp.ind.stud.res(): Studentized residuals. xp.plot(): GAM residuals base model vs. covariates. xp.summary(): Summarize GAM.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/GAM_summary_and_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"GAM functions for Xpose 4 — GAM_summary_and_plot","text":"Niclas Jonsson & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"creates stack box whisker plot absolute population conditional weighted residuals (|CWRES|) vs covariates, specific function Xpose 4.  wrapper encapsulating arguments codexpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"","code":"absval.cwres.vs.cov.bw(object, xlb = \"|CWRES|\", main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"object xpose.data object. xlb string giving label x-axis. NULL none. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.bw.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"Returns stack box--whisker plots |CWRES| vs covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling box--whisker plots available. See xpose.plot.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.cov.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute conditional weighted residuals vs covariates for Xpose 4 — absval.cwres.vs.cov.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  absval.cwres.vs.cov.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"plot absolute population conditional weighted residuals (|CWRES|) vs population predictions (PRED) conditioned covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"","code":"absval.cwres.vs.pred.by.cov(   object,   covs = \"Default\",   ylb = \"|CWRES|\",   type = \"p\",   smooth = TRUE,   idsdir = \"up\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"object xpose.data object. covs vector covariates use plot. \"Default\"  covariates defined object@Prefs@Xvardef$Covariates used. ylb string giving label y-axis. NULL none. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed. default TRUE. idsdir Direction displaying point labels. default \"\", since displaying absolute values. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"Returns stack xyplots |CWRES| vs PRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. main argument supported owing multiple plots generated function. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute value of the conditional weighted residuals vs. population\npredictions, conditioned on covariates, for Xpose 4 — absval.cwres.vs.pred.by.cov","text":"","code":"absval.cwres.vs.pred.by.cov(simpraz.xpdb, covs=c(\"HCTZ\",\"WT\"), max.plots.per.page=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"plot absolute population conditional weighted residuals (|CWRES|) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"","code":"absval.cwres.vs.pred(object, idsdir = \"up\", type = \"p\", smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"object xpose.data object. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"Returns xyplot |CWRES| vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.cwres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute population conditional weighted residuals vs population predictions\nfor Xpose 4 — absval.cwres.vs.pred","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.cwres.vs.pred(xpdb)   ## A conditioning plot absval.cwres.vs.pred(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.cwres.vs.pred(xpdb, main=\"My conditioning plot\", ylb=\"|CWRES|\", xlb=\"PRED\")   ## Custom colours and symbols, no IDs absval.cwres.vs.pred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"matrix plot absolute population weighted residuals (|CWRES|) vs population predictions (PRED) absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED), specific function Xpose 4. wrapper encapsulating arguments absval.cwres.vs.pred absval.iwres.vs.ipred functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"","code":"absval.iwres.cwres.vs.ipred.pred(object, main = \"Default\", ...)  absval.iwres.wres.vs.ipred.pred(object, main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"object xpose.data object. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"Returns compound plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"plots created absval.wres.vs.pred absval.iwres.vs.ipred functions presented side side comparison. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"absval.iwres.wres.vs.ipred.pred(): absolute population weighted residuals (|WRES|) vs population predictions (PRED) absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED)","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.cwres.vs.ipred.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute population weighted residuals vs population predictions, and\nabsolute individual weighted residuals vs individual predictions, for Xpose\n4 — absval.iwres.cwres.vs.ipred.pred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot absval.iwres.wres.vs.ipred.pred(xpdb)  absval.iwres.cwres.vs.ipred.pred(xpdb)   ## Custom colours and symbols absval.iwres.cwres.vs.ipred.pred(xpdb, cex=0.6, pch=8, col=1)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.cov.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","title":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","text":"box whisker plots absolute value  individual weighted residuals vs. covariates","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.cov.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","text":"","code":"absval.iwres.vs.cov.bw(object, xlb = \"|iWRES|\", main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.cov.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","text":"object \"xpose.data\" object. xlb string giving label x-axis. NULL none. main string giving plot title NULL none. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.cov.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"box and whisker plots of the absolute value of the \nindividual weighted residuals vs. covariates — absval.iwres.vs.cov.bw","text":"xpose.multiple.plot object","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","title":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","text":"absolute value  individual weighted residuals vs. independent variable","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","text":"","code":"absval.iwres.vs.idv(   object,   ylb = \"|iWRES|\",   smooth = TRUE,   idsdir = \"up\",   type = \"p\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","text":"object \"xpose.data\" object. ylb string giving label y-axis. NULL none. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. idsdir string indicating directions extremes include labelling. Possible values \"\", \"\" \"\". type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"absolute value of the \nindividual weighted residuals vs. the independent variable — absval.iwres.vs.idv","text":"lattice object","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"plot absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED) conditioned covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"","code":"absval.iwres.vs.ipred.by.cov(   object,   ylb = \"|IWRES|\",   idsdir = \"up\",   type = \"p\",   smooth = TRUE,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"object xpose.data object. ylb string giving label y-axis. NULL none. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"Returns stack xyplots |IWRES| vs IPRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute individual weighted residuals vs individual predictions,\nconditioned on covariates, for Xpose 4 — absval.iwres.vs.ipred.by.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.iwres.vs.ipred.by.cov(xpdb)  ## Custom axis labels absval.iwres.vs.ipred.by.cov(xpdb, ylb=\"|IWRES|\", xlb=\"IPRED\")  ## Custom colours and symbols, no IDs absval.iwres.vs.ipred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=FALSE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"plot absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"","code":"absval.iwres.vs.ipred(   object,   ylb = \"|iWRES|\",   type = \"p\",   ids = FALSE,   idsdir = \"up\",   smooth = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"object xpose.data object. ylb string giving label y-axis. NULL none. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. ids id values displayed? idsdir Direction displaying point labels. default \"\", since displaying absolute values. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"Returns xyplot |IWRES| vs IPRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.ipred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute individual weighted residuals vs individual predictions for Xpose 4 — absval.iwres.vs.ipred","text":"","code":"## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.iwres.vs.ipred(xpdb)   ## A conditioning plot absval.iwres.vs.ipred(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.iwres.vs.ipred(xpdb, main=\"My conditioning plot\", ylb=\"|IWRES|\", xlb=\"IPRED\")   ## Custom colours and symbols, no IDs absval.iwres.vs.ipred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"plot absolute individual weighted residuals (|IWRES|) vs individual predictions (PRED) independent variable (IDV), specific functions Xpose 4. functions wrappers encapsulating arguments xpose.plot.default function.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"","code":"absval.iwres.vs.pred(   object,   ylb = \"|IWRES|\",   smooth = TRUE,   idsdir = \"up\",   type = \"p\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"object xpose.data object. ylb string giving label y-axis. NULL none. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"Returns xyplot |IWRES| vs PRED |IWRES| vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.iwres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute individual weighted residuals vs population predictions or\nindependent variable for Xpose 4 — absval.iwres.vs.pred","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.iwres.vs.pred(xpdb)   ## A conditioning plot absval.iwres.vs.pred(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.iwres.vs.pred(xpdb, main=\"My conditioning plot\", ylb=\"|IWRES|\", xlb=\"PRED\")   ## Custom colours and symbols, no IDs absval.iwres.vs.pred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"creates stack box whisker plot absolute population weighted residuals (|WRES| |iWRES|) vs covariates.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"","code":"absval.wres.vs.cov.bw(object, xlb = \"|WRES|\", main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"object xpose.data object. xlb string giving label x-axis. NULL none. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.bw.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"Returns stack box--whisker plots |WRES| vs covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling box--whisker plots available. See xpose.plot.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.cov.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute weighted residuals vs covariates for Xpose 4 — absval.wres.vs.cov.bw","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.wres.vs.cov.bw(xpdb)  ## A custom plot absval.wres.vs.cov.bw(xpdb, bwdotcol=\"white\",    bwdotpch=15,   bwreccol=\"red\",   bwrecfill=\"red\",   bwumbcol=\"red\",   bwoutpch=5,   bwoutcol=\"black\")  ## A vanilla plot using IWRES absval.iwres.vs.cov.bw(xpdb) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"plot absolute value CWRES (default, residuals option) vs independent variable, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"","code":"absval.wres.vs.idv(   object,   idv = \"idv\",   wres = \"Default\",   ylb = \"Default\",   smooth = TRUE,   idsdir = \"up\",   type = \"p\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"object xpose.data object. idv independent variable. wres weighted residual use. \"Default\" CWRES. ylb Y-axis label. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"Returns xyplot |CWRES| vs idv (often TIME, defined xvardef).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute value of (C)WRES vs. independent variable plot in Xpose4. — absval.wres.vs.idv","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.wres.vs.idv(xpdb)   ## A conditioning plot absval.wres.vs.idv(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.wres.vs.idv(xpdb, main=\"Hello World\", ylb=\"|CWRES|\", xlb=\"IDV\")   ## Custom colours and symbols absval.wres.vs.idv(xpdb, cex=0.6, pch=3, col=1)   ## using the NPDEs instead of CWRES absval.wres.vs.idv(xpdb,wres=\"NPDE\") #>  #>  -----------Variable(s) not defined!------------- #>  NPDE is/are not defined in the current database #>  and must be defined for this command to work! #>  ------------------------------------------------ #> NULL  ## subsets absval.wres.vs.idv(xpdb,subset=\"TIME<10\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"plot absolute population weighted residuals (|WRES|) vs population predictions (PRED) conditioned covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"","code":"absval.wres.vs.pred.by.cov(   object,   ylb = \"|WRES|\",   type = \"p\",   smooth = TRUE,   ids = FALSE,   idsdir = \"up\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"object xpose.data object. ylb string giving label y-axis. NULL none. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ids Logical. id labels points shown? idsdir Direction displaying point labels. default \"\", since displaying absolute values. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"Returns stack xyplots |WRES| vs PRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute population weighted residuals vs population predictions,\nconditioned on covariates, for Xpose 4 — absval.wres.vs.pred.by.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.wres.vs.pred.by.cov(xpdb)  ## Custom axis labels absval.wres.vs.pred.by.cov(xpdb, ylb=\"|CWRES|\", xlb=\"PRED\")  ## Custom colours and symbols, IDs absval.wres.vs.pred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"plot absolute population weighted residuals (|WRES|) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"","code":"absval.wres.vs.pred(   object,   ylb = \"|WRES|\",   idsdir = \"up\",   type = \"p\",   smooth = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"object xpose.data object. ylb string giving label y-axis. NULL none. idsdir Direction displaying point labels. default \"\", since displaying absolute values. type Type plot. default points (\"p\"), lines (\"l\") (\"b\") also available. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"Returns xyplot |WRES| vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval.wres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Absolute population weighted residuals vs population predictions for Xpose 4 — absval.wres.vs.pred","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot absval.wres.vs.pred(xpdb)   ## A conditioning plot absval.wres.vs.pred(xpdb, by=\"HCTZ\")   ## Custom heading and axis labels absval.wres.vs.pred(xpdb, main=\"My conditioning plot\",    ylb=\"|WRES|\", xlb=\"PRED\")   ## Custom colours and symbols absval.wres.vs.pred(xpdb, cex=0.6, pch=19, col=1,    smcol=\"blue\", smlty=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"functions plot absolute differences PRED, IPRED, WRES, CWRES IWRES covariates two specified model fits.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"","code":"absval.dcwres.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"CWRES|\")),   main = \"Default\",   ... )  absval.dipred.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"IPRED|\")),   main = \"Default\",   ... )  absval.diwres.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"IWRES|\")),   main = \"Default\",   ... )  absval.dpred.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"PRED|\")),   main = \"Default\",   ... )  absval.dwres.vs.cov.model.comp(   object,   object.ref = NULL,   type = NULL,   ylb = expression(paste(\"|\", Delta, \"WRES|\")),   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"object xpose.data object. object.ref xpose.data object. supplied, user prompted. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. ylb string giving label y-axis. NULL none. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"Returns stack plots comprising comparisons PRED, IPRED, WRES (CWRES) IWRES two specified runs.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"Conditional weighted residuals (CWRES) may require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"absval.dcwres.vs.cov.model.comp(): absolute differences individual predictions covariates two specified model fits. absval.dipred.vs.cov.model.comp(): absolute differences individual predictions covariates two specified model fits. absval.diwres.vs.cov.model.comp(): absolute differences individual weighted  residuals covariates two specified model fits. absval.dpred.vs.cov.model.comp(): absolute differences population predictions covariates two specified model fits. absval.dwres.vs.cov.model.comp(): absolute differences  population weighted residuals covariates two specified model fits.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/absval_delta_vs_cov_model_comp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model comparison plots, of absolute differences in goodness-of-fit\npredictors against covariates, for Xpose 4 — absval_delta_vs_cov_model_comp","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for runs ## 5 and 6 in the current working directory xpdb5 <- xpose.data(5) xpdb6 <- xpose.data(6)  ## A basic dWRES plot, without prompts absval.dwres.vs.cov.model.comp(xpdb5, xpdb6)  ## Custom colours and symbols, no user IDs absval.dpred.vs.cov.model.comp(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":null,"dir":"Reference","previous_headings":"","what":"Print tables or text in a grid object — add.grid.table","title":"Print tables or text in a grid object — add.grid.table","text":"functions take array values labels array text add one many grid viewports orderly fashion.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print tables or text in a grid object — add.grid.table","text":"","code":"add.grid.table(   txt,   col.nams = NULL,   ystart,   xstart = unit(0, \"npc\"),   start.pt = 1,   vp,   vp.num = 1,   minrow = 5,   cell.padding = 0.5,   mult.col.padding = 1,   col.optimize = TRUE,   equal.widths = FALSE,   space.before.table = 1,   center.table = FALSE,   use.rect = FALSE,   fill.type = NULL,   fill.col = \"grey\",   cell.lines.lty = 0,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print tables or text in a grid object — add.grid.table","text":"txt text table values add grid object. col.nams column names table values ystart y location start printing grid viewport xstart x location start printing grid viewport start.pt start point (row) table array start printing vp viewport(s) add table text vp.num viewport number vp start printing minrow minimum rows printing columns use table cell.padding padding cells table mult.col.padding padding multiple columns table col.optimize column optimize (TRUE) row optimize (FALSE) equal.widths columns equal widths space..table space table center.table center table viewport? use.rect make rectangles background color around table entries TRUE FALSE fill.type rectangles filled.  Allowed values \"\", \"top\", \"side\", \"\" NULL. fill.col color filled rectangles cell.lines.lty line-type lines cells, using values lty. ... arguments passed various functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print tables or text in a grid object — add.grid.table","text":"List returned following components ystart new starting point new text stop.pt null everything gets printed vp.num viewport needed next text printed xpose.table grob object can plotted.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.grid.table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print tables or text in a grid object — add.grid.table","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional model comparison plots, for Xpose 4 — add.model.comp","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"creates stack four plots, comparing absolute values PRED, absolute values IPRED, delta CWRES (WRES) delta IWRES estimates two specified model fits.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"","code":"add.model.comp(   object,   object.ref = NULL,   onlyfirst = FALSE,   inclZeroWRES = FALSE,   subset = xsubset(object),   main = \"Default\",   force.wres = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"object xpose.data object. object.ref xpose.data object. supplied, user prompted. onlyfirst Logical value indicating whether first row per individual included plot. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default TRUE. subset string giving subset expression applied data plotting. See xsubset. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. force.wres use WRES plots instead CWRES (logical TRUE FALSE) ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"Returns stack plots comprising comparisons absolute values PRED, absolute values IPRED, absolute differences CWRES (WRES) absolute differences IWRES two specified runs.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"Four model comparison plots displayed sequence. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add.model.comp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additional model comparison plots, for Xpose 4 — add.model.comp","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for runs ## 5 and 6 in the current working directory xpdb5 <- xpose.data(5) xpdb6 <- xpose.data(6)  ## A vanilla plot, without prompts add.model.comp(xpdb5, xpdb6, prompt = FALSE)  ## Custom colours and symbols, no user IDs add.model.comp(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":null,"dir":"Reference","previous_headings":"","what":"Column-transformation functions for Xpose 4 — add_transformed_columns","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"functions transform existing Xpose 4 data columns, adding new columns.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"","code":"add.absval(object, listall = TRUE, classic = FALSE)  add.dichot(object, listall = TRUE, classic = FALSE)  add.exp(object, listall = TRUE, classic = FALSE)  add.log(object, listall = TRUE, classic = FALSE)  add.tad(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"object xpose.data object. listall logical operator specifying whether items database listed. classic logical operator specifying whether function assume classic menu system.  internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"xpose.data object (classic == FALSE) null (classic == TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"functions may used create new data columns within Xpose data object transforming existing ones.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"add.absval(): Create column containing absolute values data  another column. add.dichot(): Create categorical data column based continuous data column add.exp(): Create exponentiated version existing variable add.log(): Create log transformation existing variable add.tad(): Create time--dose (TAD) data item based upon dose time variables dataset.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"Niclas Jonsson, Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/add_transformed_columns.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Column-transformation functions for Xpose 4 — add_transformed_columns","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Create a column containing the absolute values of data in another  ## column add.absval(xpdb5)  ## Create a categorical data column based on a continuous data column,  ## and do not list variables  add.dichot(xpdb5, listall = FALSE)  ## Create a column containing the exponentiated values of data in  ## another column add.exp(xpdb5)  ## Create a column containing log-transformations of data in another  ## column add.log(xpdb5)  ## Create a time-after-dose column add.tad(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic internal functions for Xpose 4 — addid","title":"Generic internal functions for Xpose 4 — addid","text":"internal functions relating Xpose generic functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic internal functions for Xpose 4 — addid","text":"","code":"addid(   x,   y,   ids = ids,   idsmode = NULL,   idsext = 0.05,   idscex = 0.7,   idsdir = \"both\",   gridmode = TRUE )  computePI(   x,   y,   object,   limits = object@Prefs@Graph.prefs$PIlimits,   logy = FALSE,   logx = FALSE,   onlyfirst = FALSE,   inclZeroWRES = FALSE,   PI.subset = NULL,   subscripts )  create.rand(data, object, frac, seed = NULL)  create.strat.rand(data, object, x, y, frac, dilci, seed = NULL)  eq.xpose(x, number = 6, overlap = 0.5)  get.refrunno(database = \".ref.db\")  xpose.stack(data, object, select, rep, subset = NULL, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic internal functions for Xpose 4 — addid","text":"Internal helper functions generic Xpose functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generic internal functions for Xpose 4 — addid","text":"internal Xpose functions, adding ID numbers, computing prediction intervals, randomization, stacking, binning. intended direct use.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generic internal functions for Xpose 4 — addid","text":"Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"compound plot consisting plots weighted population residuals (WRES) vs population predictions (PRED), absolute individual weighted residuals (|IWRES|) vs independent variable (IDV), WRES vs IDV, weighted population residuals vs log(IDV), specific function Xpose 4. wrapper encapsulating arguments wres.vs.pred, iwres.vs.idv wres.vs.idv functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"","code":"addit.gof(   object,   type = \"p\",   title.size = 0.02,   title.just = c(\"center\", \"top\"),   main = \"Default\",   force.wres = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"object xpose.data object. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. title.size Amount, range 0-1, much space title take plot) title.just title justified main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. force.wres Plot WRES even residuals available. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"Returns compound plot comprising plots weighted population residuals (WRES) vs population predictions (PRED), absolute individual weighted residuals (|IWRES|) vs independent variable (IDV), WRES vs IDV, weighted population residuals vs log(IDV).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"Four additional goodness--fit plots presented side side comparison. wide array extra options controlling xyplots available. See xpose.plot.default xpose.multiple.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/addit.gof.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additional goodness-of-fit plots, for Xpose 4 — addit.gof","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot addit.gof(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":null,"dir":"Reference","previous_headings":"","what":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"autocorrelation plot conditional weighted residuals, specific function Xpose 4. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"","code":"autocorr.cwres(   object,   type = \"p\",   smooth = TRUE,   ids = F,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"object xpose.data object. type 1-character string giving type plot desired. following values possible, details, see plot: '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. smooth Logical value indicating whether smooth superimposed. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"Returns autocorrelation plot conditional weighted population residuals (CWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"wide array extra options controlling xyplots available. See xpose.plot.default details. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.cwres.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autocorrelation of conditional weighted residuals for Xpose 4 — autocorr.cwres","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot autocorr.cwres(xpdb)   ## A conditioning plot autocorr.cwres(xpdb, dilution=TRUE)   ## Custom heading and axis labels autocorr.cwres(xpdb, main=\"My conditioning plot\", ylb=\"|CWRES|\", xlb=\"PRED\")   ## Custom colours and symbols, IDs autocorr.cwres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.iwres.html","id":null,"dir":"Reference","previous_headings":"","what":"autocorrelation of the individual weighted residuals — autocorr.iwres","title":"autocorrelation of the individual weighted residuals — autocorr.iwres","text":"autocorrelation individual weighted residuals","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.iwres.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"autocorrelation of the individual weighted residuals — autocorr.iwres","text":"","code":"autocorr.iwres(   object,   type = \"p\",   smooth = TRUE,   ids = F,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.iwres.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"autocorrelation of the individual weighted residuals — autocorr.iwres","text":"object \"xpose.data\" object. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). main string giving plot title NULL none. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.iwres.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"autocorrelation of the individual weighted residuals — autocorr.iwres","text":"Lattice object","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":null,"dir":"Reference","previous_headings":"","what":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"autocorrelation plot weighted residuals.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"","code":"autocorr.wres(   object,   type = \"p\",   smooth = TRUE,   ids = F,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"object xpose.data object. type 1-character string giving type plot desired. following values possible, details, see plot: '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. smooth Logical value indicating whether smooth superimposed. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"Returns autocorrelation plot weighted population residuals (WRES) individual weighted residuals (IWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/autocorr.wres.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autocorrelation of weighted residuals for Xpose 4 — autocorr.wres","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) }  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot autocorr.wres(xpdb)   ## A conditioning plot autocorr.wres(xpdb, dilution=TRUE)   ## Custom heading and axis labels autocorr.wres(xpdb, main=\"My conditioning plot\", ylb=\"|CWRES|\", xlb=\"PRED\")   ## Custom colours and symbols, IDs autocorr.wres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)   ## A vanilla plot with IWRES autocorr.iwres(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"compound plot consisting plots observations (DV) vs population predictions (PRED), observations (DV) vs individual predictions (IPRED), absolute individual weighted residuals (|IWRES|) vs IPRED, weighted population residuals (CWRES) vs independent variable (IDV), specific function Xpose 4. WRES also supported. wrapper encapsulating arguments dv.vs.pred, dv.vs.ipred, absval.iwres.vs.ipred wres.vs.idv functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"","code":"basic.gof(object, force.wres = FALSE, main = \"Default\", use.log = FALSE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"object xpose.data object. force.wres plots use WRES?  Values can TRUE/FALSE.  Otherwise CWRES used present. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. use.log use log transformations plots? ... arguments passed xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"Returns compound plot comprising plots observations (DV) vs population predictions (PRED), DV vs individual predictions (IPRED), absolute individual weighted residuals (|IWRES|) vs IPRED, weighted populations residuals (WRES) vs independent variable (IDV).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"Four basic goodness--fit plots presented side side comparison. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details. basic.gof.cwres just wrapper basic.gof use.cwres=TRUE.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.gof.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic goodness-of-fit plots, for Xpose 4 — basic.gof","text":"","code":"basic.gof(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic model comparison plots, for Xpose 4 — basic.model.comp","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"creates stack four plots, comparing PRED, IPRED, WRES (CWRES), IWRES estimates two specified model fits.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"","code":"basic.model.comp(   object,   object.ref = NULL,   onlyfirst = FALSE,   inclZeroWRES = FALSE,   subset = xsubset(object),   main = \"Default\",   force.wres = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"object xpose.data object. object.ref xpose.data object. supplied, user prompted. onlyfirst Logical value indicating whether first row per individual included plot. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default TRUE. subset string giving subset expression applied data plotting. See xsubset. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. force.wres Force function use WRES? ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"Returns stack plots comprising comparisons PRED, IPRED, WRES (CWRES) IWRES two specified runs.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"Four basic model comparison plots displayed sequence. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/basic.model.comp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic model comparison plots, for Xpose 4 — basic.model.comp","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for runs ## 5 and 6 in the current working directory xpdb5 <- xpose.data(5) xpdb6 <- xpose.data(6)  ## A vanilla plot, without prompts basic.model.comp(xpdb5, xpdb6, prompt = FALSE)  ## Custom colours and symbols, no user IDs basic.model.comp.cwres(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"Reads results bootstrap tool PsN creates histograms.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"","code":"boot.hist(   results.file = \"raw_results_run1.csv\",   incl.ids.file = \"included_individuals1.csv\",   min.failed = FALSE,   cov.failed = FALSE,   cov.warnings = FALSE,   boundary = FALSE,   showOriginal = TRUE,   showMean = FALSE,   showMedian = FALSE,   showPCTS = FALSE,   PCTS = c(0.025, 0.975),   excl.id = c(),   layout = NULL,   sort.plots = TRUE,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"results.file location results file bootstrap tool PsN incl.ids.file location included ids file bootstrap tool PsN min.failed NONMEM runs failed minimization skipped? TRUE FALSE cov.failed NONMEM runs failed covariance step skipped? TRUE FALSE cov.warnings NONMEM runs covariance step warnings skipped? TRUE FALSE boundary NONMEM runs boundary warnings skipped? TRUE FALSE showOriginal show value original NONMEM run histograms? TRUE FALSE showMean show mean histogram data? TRUE FALSE showMedian show median histogram data? TRUE FALSE showPCTS show percentiles histogram data? TRUE FALSE PCTS percentiles show.  Can vector length.  example, c(0.05,0.2,0.5,0.7) excl.id Vector id numbers exclude. layout Layout plots.  vector number rows columns plot. c(3,3) example. sort.plots plots sorted based type parameter. Sorting parameters, standard errors, shrinkage eigenvalues. main title plot. ... Additional arguments can passed xpose.plot.histogram, xpose.panel.histogram, histogram lattice-package functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"lattice object","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"PsN","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/boot.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to create histograms of results from the bootstrap tool in\nPsN — boot.hist","text":"","code":"if (FALSE) { boot.hist(results.file=\"./boot1/raw_results_run1.csv\",            incl.ids.file=\"./boot1/included_individuals1.csv\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary information for a bootgam or bootscm — bootgam.print","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"functions prints summary information bootgam performed Xpose, bootscm performed PsN.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"","code":"bootgam.print(bootgam.obj = NULL)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"bootgam.obj bootgam bootscm object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"value returned","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootgam.print.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print summary information for a bootgam or bootscm — bootgam.print","text":"","code":"if (FALSE) { bootgam.print(current.bootgam)  # Print summary for the current Xpose bootgam object bootgam.print(current.bootscm)  # Print summary for the current Xpose bootscm object }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootscm.import.html","id":null,"dir":"Reference","previous_headings":"","what":"Import bootscm data into R/Xpose — bootscm.import","title":"Import bootscm data into R/Xpose — bootscm.import","text":"function imports data generated PsN boot_scm function Xpose / R environment.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootscm.import.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import bootscm data into R/Xpose — bootscm.import","text":"","code":"bootscm.import(   scm.folder = NULL,   silent = FALSE,   n.bs = NULL,   cov.recoding = NULL,   group.by.cov = NULL,   skip.par.est.import = FALSE,   dofv.forward = 3.84,   dofv.backward = 6.64,   runno = NULL,   return.obj = FALSE )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootscm.import.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import bootscm data into R/Xpose — bootscm.import","text":"scm.folder folder PsN-generated bootscm data . silent output progress report. Default FALSE. n.bs number bootstraps performed. Defaults 100. cov.recoding categorical covariates recoded dichotomous covariates within bootscm configuration file, list can specified containing data frames recoding. See example details. group..cov Group inclusion frequencies covariate, instead calculating per parameter-covariates relationship. Default NULL, means user asked make choice. skip.par.est.import Skip import parameter estimates (final model scm's, well parameter estimates first step scm). data required make plot show inclusion bias correlation parameter estimates. Importing data takes bit time (may take minute ), intend make plots anyhow step can skipped. Default FALSE. dofv.forward dOFV value used forward step scm. dofv.backward dOFV value used backward step scm. runno run-number base model bootSCM. return.obj bootscm object returned function?","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/bootscm.import.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import bootscm data into R/Xpose — bootscm.import","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":null,"dir":"Reference","previous_headings":"","what":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"Categorical observations vs. independent variable using stacked bars.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"","code":"cat.dv.vs.idv.sb(   object,   dv = xvardef(\"dv\", object),   idv = xvardef(\"idv\", object),   by = NULL,   groups = dv,   force.by.factor = FALSE,   recur = F,   xlb = idv,   ylb = \"Proportion\",   subset = NULL,   vary.width = T,   level.to.plot = NULL,   refactor.levels = TRUE,   main = xpose.create.title.text(idv, dv, \"Proportions of\", object, subset = subset, ...),   stack = TRUE,   horizontal = FALSE,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   scales = list(),   inclZeroWRES = TRUE,   onlyfirst = FALSE,   samp = NULL,   aspect = object@Prefs@Graph.prefs$aspect,   auto.key = \"Default\",   mirror = FALSE,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"object Xpose data object. dv dependent variable (e.g. \"DV\" \"CP\".) idv independent variable (e.g. \"TIME\".) Conditioning variable groups group values conditional plot. force..factor force data treated factors? recur used. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. subset Subset data. vary.width vary width bars match amount information? level..plot levels DV plot. refactor.levels refactor levels? main title plot. stack stack bars? horizontal bars horizontal? strip Defining strips appear conditioning plots. scales Scales argument xyplot. inclZeroWRES Include rows WRES=0? onlyfirst include first data point individual? samp Sample use mirror plot (number). aspect Aspect argument xyplot. auto.key Make legend. mirror Mirror can FALSE, TRUE, 1 3. mirror.aspect Aspect mirror. pass.plot.list plot list passed back user? x.cex Size x axis label. y.cex Size Y axis label. main.cex Size Title. mirror.internal Internal stuff. ... arguments passed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.dv.vs.idv.sb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Categorical observations vs. independent variable using stacked bars. — cat.dv.vs.idv.sb","text":"","code":"if (FALSE) { ## read in table files runno <- 45 xpdb <- xpose.data(runno)  ## make some stacked bar plots cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F) cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F,by=\"DOSE\") cat.dv.vs.idv.sb(xpdb,idv=\"DOSE\") cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F,by=\"TIME\") cat.dv.vs.idv.sb(xpdb,idv=\"TIME\") cat.dv.vs.idv.sb(xpdb,idv=\"CAVH\") cat.dv.vs.idv.sb(xpdb,idv=\"TIME\",by=\"DOSE\",scales=list(x=list(rot=45)))  ## make some mirror plots cat.dv.vs.idv.sb(xpdb,idv=\"DOSE\",mirror=1) cat.dv.vs.idv.sb(xpdb,idv=\"CAVH\",mirror=1,auto.key=F) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":null,"dir":"Reference","previous_headings":"","what":"Categorical (visual) predictive check. — cat.pc","title":"Categorical (visual) predictive check. — cat.pc","text":"Categorical (visual) predictive check plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Categorical (visual) predictive check. — cat.pc","text":"","code":"cat.pc(   object,   dv = xvardef(\"dv\", object),   idv = xvardef(\"idv\", object),   level.to.plot = NULL,   subset = NULL,   histo = T,   median.line = F,   PI.lines = F,   xlb = if (histo) {      paste(\"Proportion of \", dv)  } else {      paste(idv)  },   ylb = if (histo) {      paste(\"Percent of Total\")  } else {          paste(\"Proportion of Total\")  },   main = xpose.create.title.text(NULL, dv, \"Predictive check of\", object, subset =     subset, ...),   strip = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Categorical (visual) predictive check. — cat.pc","text":"object Xpose data object. dv dependent variable (e.g. \"DV\" \"CP\".) idv independent variable (e.g. \"TIME\".) level..plot levels plot. subset Subset data. histo FALSE VPC created, given idv defined. median.line Make median line? PI.lines Make prediction interval lines? xlb Label x axis. ylb label y axis. main Main title. strip Defining strips appear conditioning plots. ... Extra arguments passed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Categorical (visual) predictive check. — cat.pc","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cat.pc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Categorical (visual) predictive check. — cat.pc","text":"","code":"if (FALSE) { ## read in table files runno <- 45 xpdb <- xpose.data(runno)  ## create proportion (visual) predictive check cat.pc(xpdb,idv=NULL) cat.pc(xpdb,idv=\"DOSE\") cat.pc(xpdb,idv=\"DOSE\",histo=F) cat.pc(xpdb,idv=\"TIME\",histo=T,level.to.plot=1) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic table functions for Xpose 4 — categorical.table","title":"Generic table functions for Xpose 4 — categorical.table","text":"internal table functions relating Xpose summary functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic table functions for Xpose 4 — categorical.table","text":"","code":"categorical.table(   object,   vars,   onlyfirst = TRUE,   subset = xsubset(object),   inclZeroWRES = FALSE,   miss = object@Prefs@Miss )  continuous.table(   object,   vars,   onlyfirst = TRUE,   subset = xsubset(object),   inclZeroWRES = FALSE,   miss = object@Prefs@Miss )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic table functions for Xpose 4 — categorical.table","text":"Internal helper functions generic Xpose summary functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generic table functions for Xpose 4 — categorical.table","text":"internal Xpose functions outputting summary tables. intended direct use.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/categorical.table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generic table functions for Xpose 4 — categorical.table","text":"Niclas Jonsson, Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":null,"dir":"Reference","previous_headings":"","what":"Change parameter scope. — change.parm","title":"Change parameter scope. — change.parm","text":"Function change parameter scope.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change parameter scope. — change.parm","text":"","code":"change.parm(object, listall = TRUE, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change parameter scope. — change.parm","text":"object xpose data object. listall whether list current parameters. classic true used classic menu system (internal use).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change parameter scope. — change.parm","text":"classic return nothing.  Otherwise return new data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.parm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change parameter scope. — change.parm","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":null,"dir":"Reference","previous_headings":"","what":"Changes the name of an Xpose data item — change.var.name","title":"Changes the name of an Xpose data item — change.var.name","text":"function allows names data items Xpose database changed.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Changes the name of an Xpose data item — change.var.name","text":"","code":"change.var.name(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Changes the name of an Xpose data item — change.var.name","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Changes the name of an Xpose data item — change.var.name","text":"xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Changes the name of an Xpose data item — change.var.name","text":"function facilitates changing data item names object@Data slot.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Changes the name of an Xpose data item — change.var.name","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.var.name.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Changes the name of an Xpose data item — change.var.name","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  xpdb5 <- change.var.name(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":null,"dir":"Reference","previous_headings":"","what":"Changes the label of an Xpose data item — change.xlabel","title":"Changes the label of an Xpose data item — change.xlabel","text":"function allows labels data items Xpose database changed.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Changes the label of an Xpose data item — change.xlabel","text":"","code":"change.xlabel(object, listall = TRUE, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Changes the label of an Xpose data item — change.xlabel","text":"object xpose.data object. listall logical operator specifying whether items database listed. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Changes the label of an Xpose data item — change.xlabel","text":"xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Changes the label of an Xpose data item — change.xlabel","text":"function facilitates changing data item labels object@Prefs@Labels slot.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Changes the label of an Xpose data item — change.xlabel","text":"Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xlabel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Changes the label of an Xpose data item — change.xlabel","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  xpdb5 <- change.xlabel(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":null,"dir":"Reference","previous_headings":"","what":"Change Xpose variable definitions. — change.xvardef","title":"Change Xpose variable definitions. — change.xvardef","text":"functions allow changing Xpose variable definitions like \"idv\" \"dv\". variable definitions used refer columns observed data generic way, generic plotting functions can created.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change Xpose variable definitions. — change.xvardef","text":"","code":"change.xvardef(   object,   var = \".ask\",   def = \".ask\",   listall = TRUE,   classic = FALSE,   check.var = FALSE,   ... )  change.xvardef(   object,   var,   listall = FALSE,   classic = FALSE,   check.var = FALSE,   ... ) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change Xpose variable definitions. — change.xvardef","text":"object xpose.data object. var Xpose variable like change add current object. one-element character vector (e.g. \"idv\").  \".ask\" user prompted input value. def vector column names NONMEM table files (names(object@Data)) associated variable (e.g. c(\"TIME\")).  Multiple values allowed. \".ask\" user prompted input values. listall function list database values? classic function used classic interface.  internal option. check.var variables checked current variables object? ... Items passed functions within function. value vector values","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change Xpose variable definitions. — change.xvardef","text":"called command line function returns xpose database.  called classic interface function updates current xpose database (.cur.db).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Change Xpose variable definitions. — change.xvardef","text":"change.xvardef(   object,   var,   listall = FALSE,   classic = FALSE,   check.var = FALSE,   ... ) <- value: Change covariate scope xpose database object","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"additional-arguments","dir":"Reference","previous_headings":"","what":"Additional arguments","title":"Change Xpose variable definitions. — change.xvardef","text":"default xpose variables : id Individual identifier column dataset idlab values used plotting ID values data points plots occ occasion variable dv dv variable pred pred variable ipred ipred variable wres wres variable cwres cwres variable res res variable parms parameters database covariates covariates database ranpar random parameters database","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change Xpose variable definitions. — change.xvardef","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change.xvardef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change Xpose variable definitions. — change.xvardef","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  # Change the \"id\" variable to point to \"PRED\" in the xpose object xpdb <- change.xvardef(xpdb,var=\"id\",def=\"PRED\")  # Check the value of the \"id\" variable xvardef(\"id\",xpdb) #> [1] \"PRED\"  # Change the \"idv\" variable change.xvardef(xpdb,var=\"idv\") <- \"TIME\"  # Change the covariate scope change.xvardef(xpdb,var=\"covariates\") <- c(\"SEX\",\"AGE\",\"WT\")  if (FALSE) { # Use the interactive capabilities of the function xpdb <- change.xvardef(xpdb) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"functions allow customization Xpose's graphics settings.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"","code":"change.ab.graph.par(object, classic = FALSE)  change.bw.graph.par(object, classic = FALSE)  change.cond.graph.par(object, classic = FALSE)  change.dil.graph.par(object, classic = FALSE)  change.label.par(object, classic = FALSE)  change.lm.graph.par(object, classic = FALSE)  change.misc.graph.par(object, classic = FALSE)  change.pi.graph.par(object, classic = FALSE)  change.smooth.graph.par(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"xpose.data object (classic == FALSE) null (classic == TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"Settings can saved loaded using export.graph.par import.graph.par, respectively.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"change.ab.graph.par(): change settings line identity. change.bw.graph.par(): sets preferences box--whisker plots change.cond.graph.par(): sets preferences conditioning change.dil.graph.par(): responsible dilution preferences change.label.par(): responsible labelling preferences change.lm.graph.par(): responsible linear regression lines. change.misc.graph.par(): sets basic graphics parameters, including plot type, point type size, colour, line type, line width. change.pi.graph.par(): responsible prediction interval plotting preferences change.smooth.graph.par(): sets preferences loess smooths.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_graphical_parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions changing variable definitions in Xpose 4 — change_graphical_parameters","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Change default miscellaneous graphic preferences xpdb5 <- change.misc.graph.par(xpdb5)  ## Change default linear regression line preferences, creating a new  ## object xpdb5.a <- change.lm.graph.par(xpdb5)  ## Change conditioning preferences xpdb5 <- change.cond.graph.par(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"functions allow viewing changing settings relating subsets, categorical threshold values, documentation numbers indicating missing data values.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"","code":"change.cat.cont(   object,   listall = TRUE,   classic = FALSE,   to.cat.vec = NULL,   to.cont.vec = NULL,   change.type.vec = NULL,   ... )  change.cat.cont(   object,   listall = TRUE,   classic = FALSE,   to.cat.vec = NULL,   to.cont.vec = NULL,   ... ) <- value  change.cat.levels(object, classic = FALSE, cat.limit = NULL, ...)  change.cat.levels(object, classic = FALSE, ...) <- value  change.dv.cat.levels(object, classic = FALSE, dv.cat.limit = NULL, ...)  change.dv.cat.levels(object, classic = FALSE, ...) <- value  change.miss(object, classic = FALSE)  change.subset(object, classic = FALSE)  get.doc(object, classic = FALSE)  set.doc(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"object xpose.data object. listall logical operator specifying whether items database listed. classic logical operator specifying whether function assume classic menu system. internal option need never called command line. .cat.vec vector strings specifying names categorical variables transformed continuous. .cont.vec vector strings specifying names continuous variables transformed categorical. change.type.vec vector strings specifying names variables transformed /continuous/categorical. ... arguments passed functions. value value replaced xpose data object object.  value used “replacement function” version functions.  form function.name(object) <- value.  value NULL functions prompt user value.  change.cat.levels, value categorical limit cat.limit.  change.dv.cat.levels, value DV categorical limit dv.cat.limit.  change.cat.cont, value change.type.vec.  See examples . cat.limit limit treat list values categorical.  cat.limit less unique values list treated categorical. dv.cat.limit limit treat DV categorical.  dv.cat.limit less unique dv values dv treated categorical.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"xpose.data object, except get.doc, returns value object@Doc.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"change.cat.cont(): allows interchange categorical continuous data formats within Xpose database. turn affects plots drawn. change.cat.cont(   object,   listall = TRUE,   classic = FALSE,   .cat.vec = NULL,   .cont.vec = NULL,   ... ) <- value: allows interchange categorical continuous data formats within Xpose database. turn affects plots drawn. change.cat.levels(): change settings number unique data values required variable order define continuous ordinary variables. change.cat.levels(object, classic = FALSE, ...) <- value: change settings number unique data values required variable order define continuous ordinary variables. change.dv.cat.levels(): change settings number unique data values required variable order define continuous dependent variable. change.dv.cat.levels(object, classic = FALSE, ...) <- value: change settings number unique data values required variable order define continuous dependent variable. change.miss(): change value use 'missing'. change.subset(): used setting data item's subset field.  specify subset data process, use variable names regular R selection operators. combine subset two variables, selection expressions two variables combined using R's unary logical operators. variable names specified NONMEM table files (e.g. PRED, TIME, SEX). selection operators : == (equal) != (equal) || () > (greater ) < (less ) example, specify TIME less 24 processed, type expression: TIME < 24. unary logical operators : & () | () example, specify TIME less 24 males (SEX equal 1), type expression: TIME < 24 & SEX == 1 subset selection scheme works variables, including ID numbers. subset selection entirely stable. example, check user enters valid expression, user specifies existing variable names. erroneous expression become evident plot attempted expression takes effect. get.doc(): get documentation field Xpose data object. set.doc(): set documentation field Xpose data object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"Andrew Hooker, Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/change_misc_parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions changing miscellaneous parameter settings in Xpose 4 — change_misc_parameters","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Change default subset xpdb5 <- change.subset(xpdb5)  ## Set documentation field xpdb5 <- set.doc(xpdb5) ## View it view.doc(xpdb5)  ## change the categorical limit for the dv variable change.dv.cat.levels(xpdb5) <- 10  ## change the categorical limit for non DV variables change.cat.levels(xpdb5) <- 2 ## or xpdb5 <- change.cat.levels(xpdb5,cat.levels=2)  ## chnage variables from categorical to continuous xpdb5 <- change.cat.cont(xpdb5,to.cat.vec=c(\"AGE\"),to.cont.vec=c(\"SEX\")) xpdb5 <- change.cat.cont(xpdb5,change.type.vec=c(\"AGE\",\"SEX\")) change.cat.cont(xpdb5) <- c(\"AGE\",\"SEX\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Data functions for Xpose 4 — check.vars","title":"Data functions for Xpose 4 — check.vars","text":"functions perform various tasks managing Xpose data objects.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data functions for Xpose 4 — check.vars","text":"","code":"check.vars(vars, object, silent = FALSE)  is.readable.file(filename)  test.xpose.data(object)  xpose.bin(data, y, bins = 10)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data functions for Xpose 4 — check.vars","text":"vars List variables checked. object xpose.data object. silent logical operator specifying whether output displayed. filename filename checked readability.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Data functions for Xpose 4 — check.vars","text":"TRUE, FALSE NULL.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data functions for Xpose 4 — check.vars","text":"internal Xpose functions, intended direct use.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/check.vars.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Data functions for Xpose 4 — check.vars","text":"Niclas Jonsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the Conditional Weighted Residuals — compute.cwres","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"function computes conditional weighted residuals (CWRES) NONMEM run.  CWRES extension weighted residuals (WRES), calculated based first-order conditional estimation (FOCE) method linearizing pharmacometric model (WRES calculated based first-order (FO) method). function requires NONMEM table file extra output file must explicitly asked running NONMEM, see details .","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"","code":"compute.cwres(   run.number,   tab.prefix = \"cwtab\",   sim.suffix = \"\",   est.tab.suffix = \".est\",   deriv.tab.suffix = \".deriv\",   old.file.convention = FALSE,   id = \"ALL\",   printToOutfile = TRUE,   onlyNonZero = TRUE,   ... )  xpose.calculate.cwres(   object,   cwres.table.prefix = \"cwtab\",   tab.suffix = \"\",   sim.suffix = \"sim\",   est.tab.suffix = \".est\",   deriv.tab.suffix = \".deriv\",   old.file.convention = FALSE,   id = \"ALL\",   printToOutfile = TRUE,   onlyNonZero = FALSE,   classic = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"run.number run number NONMEM CWRES calculated. tab.prefix prefix two NONMEM file containing needed values computation CWRES, described details section. sim.suffix suffix ,\".\", NONMEM file containing needed values computation CWRES, described details section. example, table files might named cwtab1sim.est cwtab1sim.deriv, case sim.suffix=\"sim\". est.tab.suffix suffix, \".\", NONMEM file containing estimated parameter values needed CWRES calculation. deriv.tab.suffix suffix, \".\", NONMEM file containing derivatives model respect random parameters needed CWRES calculation. old.file.convention backwards compatibility.  Use using previous file convention CWRES (table files named cwtab1, cwtab1.50, cwtab1.51, ... , cwtab.58 example). id Can either \"\" number matching ID label datasetname. Value fixed \"\" xpose.calculate.cwres. printToOutfile Logical (TRUE/FALSE) indicating whether CWRES values calculated appended copy datasetname. works id=\"\".  chosen resulting output file datasetname.cwres.  Value fixed TRUE xpose.calculate.cwres. onlyNonZero Logical (TRUE/FALSE) indicating return value (CWRES values) compute.cwres include zero values associated non-measurement lines NONMEM data file. ... arguments passed basic functions code. object xpose.data object. cwres.table.prefix prefix NONMEM table file containing derivative model respect etas epsilons, described details section. tab.suffix suffix NONMEM table file containing derivative model respect etas epsilons, described details section. classic Indicates function used classic menu system.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"xpose.calculate.cwres Returns Xpose data object contains CWRES. simulated data present, CWRES also calculated data.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"function  reads following two files: paste(tab.prefix,run.number,sim.suffix,est.tab.suffix,sep=\"\") paste(tab.prefix,run.number,sim.suffix,deriv.tab.suffix,sep=\"\") might example: (depending input values function) returns CWRES vector form well creating new table file named: paste(tab.prefix,run.number,sim.suffix,sep=\"\") might example:","code":"cwtab1.est cwtab1.deriv cwtab1"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"xpose.calculate.cwres(): function wrapper around function compute.cwres.  computes CWRES model file associated Xpose data object input function.  possible also computes CWRES simulated data associated current Xpose data object.  problems function try using compute.cwres rereading dataset Xpose.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"setting-up-the-nonmem-model-file","dir":"Reference","previous_headings":"","what":"Setting up the NONMEM model file","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"order function calculate CWRES, NONMEM must run requesting certain tables files created.  files created differs depending using $PRED ADVAN well version NONMEM using. procedures known work NONMEM VI may different NONMEM V NONMEM VII.  attempted indicate NONMEM V may different, extensively tested!  NONMEM VII CWRES calculated internally function rarely needed. procedure can done automatically using Perl Speaks NONMEM (PsN) highly recommend using PsN purpose.  installing PsN just type 'execute [modelname] -cwres'.  See https://uupharmacometrics.github.io/PsN/ details. five main insertions needed NONMEM control file: $ABB COMRES=X. Insert line directly $DATA line.  value X number ETA() terms plus number EPS() terms model.  example model three ETA() terms two EPS() terms code look like :  Verbatim code. Using ADVAN. using ADVAN routines model, Verbatim code inserted directly $ERROR section model file.  length code depends number ETA() terms EPS() terms model.  ETA(y) model corresponding term G(y,1) must assign COM() variable.  EPS(y) model, corresponding HH(y,1) term must assign COM() variable. example model using ADVAN routines three ETA() terms two EPS() terms code look like :  Using PRED. using $PRED, verbatim code inserted directly $PRED section model file.  ETA(y) model corresponding term G(y,1) must assign COM() variable. EPS(y) model, corresponding H(y,1) term must assign COM() variable. code look like three ETA() terms two EPS() terms:   INFN routine. Using ADVAN NONMEM VI higher. using ADVAN routines model, $INFN section placed directly $PK section using following code.  example assuming model file named something like 'run1.mod', thus prefix file names 'cwtab' run number attached (.e. 'cwtab1').  changed new run number.  Using ADVAN NONMEM V. using ADVAN routines model, need use INFN subroutine.  call INFN subroutine 'myinfn.' $SUBS line model file include INFN option.  , using ADVAN2 TRANS2 model file $SUBS line look like:  'myinfn.' routine 4 thetas, 3 etas 1 epsilon shown . model different numbers thetas, etas epsilons values NTH, NETA, NEPS, changed respectively.  vales found DATA statement subroutine.  additionally, example assuming model file named something like 'run1.mod', thus prefix output file names ('cwtab') subroutine run number attached (.e. 'cwtab1').  number changed new run number (see line beginning 'OPEN').  Using $PRED NONMEM VI higher. using $PRED, following code placed end $PRED section model file (together verbatim code).  example assuming model file named something like 'run1.mod', thus prefix file names 'cwtab' run number attached (.e. 'cwtab1').  changed new run number.  Using $PRED NONMEM V. using $PRED NONMEM V, need add verbatim code immediately $PRED command.  example assume 4 thetas, 3 etas 1 epsilon.  model different numbers thetas, etas epsilons values NTH, NETA, NEPS, changed respectively.  vales found DATA statement . verbatim code add abbreviated code needed $PRED routine model file.  abbreviated code verbatim code needed.  verbatim code added verbatim code discussed point 2.  example assuming model file named something like 'run1.mod', thus prefix output file names ('cwtab') run number attached (.e. 'cwtab1').  number changed new run number (see line beginning 'OPEN').   cwtab*.deriv table file. special table file needs created print values contained COMRES variables.  addition ID, IPRED, MDV, DV, PRED RES data items needed computation CWRES.  following code added NONMEM model file.  example continue assume using model three ETA() terms two EPS() terms, extra terms added new ETA() EPS() terms model file.  also assume model file named something like 'run1.mod', thus prefix file names 'cwtab' run number attached (.e. 'cwtab1').  changed new run number.  $ESTIMATION. compute CWRES, NONMEM model file must use (least) FO method POSTHOC step.  FO method used POSTHOC step included CWRES values equivalent WRES.  CWRES calculations based FOCE approximation, consequently give idea ability FOCE method fit model data. using another method parameter estimation (e.g. FOCE interaction), CWRES calculated based model linearization procedure.","code":"$DATA temp.csv IGNORE=@  $ABB COMRES=5  $INPUT ID TIME DV MDV AMT EVID  $SUB ADVAN2 TRANS2 \"LAST  \"  COM(1)=G(1,1)  \"  COM(2)=G(2,1)  \"  COM(3)=G(3,1)  \"  COM(4)=HH(1,1)  \"  COM(5)=HH(2,1) \"LAST  \"  COM(1)=G(1,1)  \"  COM(2)=G(2,1)  \"  COM(3)=G(3,1)  \"  COM(4)=H(1,1)  \"  COM(5)=H(2,1) $INFN IF (ICALL.EQ.3) THEN   OPEN(50,FILE='cwtab1.est')   WRITE(50,*) 'ETAS'   DO WHILE(DATA)                                                            IF (NEWIND.LE.1) WRITE (50,*) ETA                                       ENDDO                                                                   WRITE(50,*) 'THETAS'   WRITE(50,*) THETA   WRITE(50,*) 'OMEGAS'   WRITE(50,*) OMEGA(BLOCK)   WRITE(50,*) 'SIGMAS'   WRITE(50,*) SIGMA(BLOCK) ENDIF $SUB ADVAN2 TRANS2 INFN=myinfn.for SUBROUTINE INFN(ICALL,THETA,DATREC,INDXS,NEWIND)      DIMENSION THETA(*),DATREC(*),INDXS(*)      DOUBLE PRECISION THETA      COMMON /ROCM6/ THETAF(40),OMEGAF(30,30),SIGMAF(30,30)      COMMON /ROCM7/ SETH(40),SEOM(30,30),SESIG(30,30)      COMMON /ROCM8/ OBJECT      COMMON /ROCM9/ IERE,IERC      DOUBLE PRECISION THETAF, OMEGAF, SIGMAF      DOUBLE PRECISION OBJECT      REAL SETH,SEOM,SESIG      DOUBLE PRECISION ETA(10)      INTEGER J,I      INTEGER IERE,IERC      INTEGER MODE      INTEGER NTH,NETA,NEPS      DATA NTH,NETA,NEPS/4,3,1/      IF (ICALL.EQ.0) THEN C      open files here, if necessary        OPEN(50,FILE='cwtab1.est')      ENDIF      IF (ICALL.EQ.3) THEN        MODE=0        CALL PASS(MODE)        MODE=1        WRITE(50,*) 'ETAS' 20     CALL PASS(MODE)        IF (MODE.EQ.0) GO TO 30        IF (NEWIND.NE.2) THEN          CALL GETETA(ETA)          WRITE (50,97) (ETA(I),I=1,NETA)        ENDIF        GO TO 20 30     CONTINUE        WRITE (50,*) 'THETAS'        WRITE (50,99) (THETAF(J),J=1,NTH)        WRITE(50,*) 'OMEGAS'        DO 7000 I=1,NETA 7000     WRITE (50,99) (OMEGAF(I,J),J=1,NETA)        WRITE(50,*) 'SIGMAS'        DO 7999 I=1,NEPS 7999     WRITE (50,99) (SIGMAF(I,J),J=1,NEPS)      ENDIF 99   FORMAT (20E15.7) 98   FORMAT (2I8) 97   FORMAT (10E15.7)      RETURN      END IF (ICALL.EQ.3) THEN   OPEN(50,FILE='cwtab1.est')   WRITE(50,*) 'ETAS'   DO WHILE(DATA)                                                            IF (NEWIND.LE.1) WRITE (50,*) ETA                                       ENDDO                                                                   WRITE(50,*) 'THETAS'   WRITE(50,*) THETA   WRITE(50,*) 'OMEGAS'   WRITE(50,*) OMEGA(BLOCK)   WRITE(50,*) 'SIGMAS'   WRITE(50,*) SIGMA(BLOCK) ENDIF $PRED \"FIRST   \"     COMMON /ROCM6/ THETAF(40),OMEGAF(30,30),SIGMAF(30,30)  \"     COMMON /ROCM7/ SETH(40),SEOM(30,30),SESIG(30,30)  \"     COMMON /ROCM8/ OBJECT  \"     DOUBLE PRECISION THETAF, OMEGAF, SIGMAF  \"     DOUBLE PRECISION OBJECT  \"     REAL SETH,SEOM,SESIG  \"     INTEGER J,I  \"     INTEGER MODE  \"     INTEGER NTH,NETA,NEPS  \"     DATA NTH,NETA,NEPS/4,3,1/ \"     IF (ICALL.EQ.0) THEN  \"C    open files here, if necessary  \"       OPEN(50,FILE='cwtab1.est')  \"     ENDIF  \"     IF (ICALL.EQ.3) THEN  \"       MODE=0  \"       CALL PASS(MODE)  \"       MODE=1  \" \t     WRITE(50,*) 'ETAS'  \"20     CALL PASS(MODE)  \"       IF (MODE.EQ.0) GO TO 30  \"       IF (NEWIND.NE.2) THEN  \"         CALL GETETA(ETA)  \"         WRITE (50,97) (ETA(I),I=1,NETA)  \"       ENDIF  \"       GO TO 20  \"30     CONTINUE  \"       WRITE (50,*) 'THETAS'  \"       WRITE (50,99) (THETAF(J),J=1,NTH)  \"       WRITE (50,*) 'OMEGAS'  \"       DO 7000 I=1,NETA  \"7000     WRITE (50,99) (OMEGAF(I,J),J=1,NETA)  \"       WRITE (50,*) 'SIGMAS'  \"       DO 7999 I=1,NEPS  \"7999     WRITE (50,99) (SIGMAF(I,J),J=1,NEPS)  \"     ENDIF  \"99   FORMAT (20E15.7)  \"98   FORMAT (2I8)  \"97   FORMAT (10E15.7) $TABLE ID COM(1)=G11 COM(2)=G21 COM(3)=G31 COM(4)=H11 COM(5)=H21         IPRED MDV NOPRINT ONEHEADER FILE=cwtab1.deriv"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"Hooker AC, Staatz CE, Karlsson MO. Conditional weighted residuals, improved model diagnostic FO/FOCE methods. PAGE 15 (2006) Abstr 1001 [http://www.page-meeting.org/?abstract=1001]. Hooker AC, Staatz CE Karlsson MO, Conditional weighted residuals (CWRES):  model diagnostic FOCE method, Pharm Res, 24(12): p. 2187-97, 2007, [doi:10.1007/s11095-007-9361-x ].","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/compute.cwres.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the Conditional Weighted Residuals — compute.cwres","text":"","code":"if (FALSE) { ## Capture CWRES from cwtab5.est and cwtab5.deriv cwres <- compute.cwres(5) mean(cwres) var(cwres)  ## Capture CWRES from cwtab1.est and cwtab1.deriv, do not print out, allow zeroes cwres <- compute.cwres(\"1\", printToOutFile = FALSE,   onlyNonZero = FALSE)  ## Capture CWRES for ID==1 cwres.1 <- compute.cwres(\"1\", id=1)  ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Compare WRES, CWRES xpdb5 <- xpose.calculate.cwres(xpdb5) cwres.wres.vs.idv(xpdb5)   }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"function takes generic plotting functions Xpose 4 calls multiple times current arguments functions, changing arguments needed mirror plotting.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"","code":"create.mirror(   fun,   arg.list,   mirror,   plotTitle,   fix.y.limits = TRUE,   fix.x.limits = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"fun function name call multiple times arg.list arguments function mirror type mirror plots desired (1 3 mirror plots can created) plotTitle title plots fix.y.limits fix y axes ? fix.x.limits fix x axes ? ... additional arguments passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"list plots, NULL.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"mostly internal function Xpose","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.mirror.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to create mirror plots from the generic Xpose plotting commands — create.mirror","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.xpose.plot.classes.html","id":null,"dir":"Reference","previous_headings":"","what":"Create xpose.multiple.plot class. — create.xpose.plot.classes","title":"Create xpose.multiple.plot class. — create.xpose.plot.classes","text":"Creates class viewing plotting xpose plots multiple plots page multiple pages.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.xpose.plot.classes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create xpose.multiple.plot class. — create.xpose.plot.classes","text":"","code":"create.xpose.plot.classes()"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/create.xpose.plot.classes.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create xpose.multiple.plot class. — create.xpose.plot.classes","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":null,"dir":"Reference","previous_headings":"","what":"This function creates the Xpose data classes (","title":"This function creates the Xpose data classes (","text":"function defines sets Xpose data classes.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This function creates the Xpose data classes (","text":"","code":"createXposeClasses(nm7 = F)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This function creates the Xpose data classes (","text":"nm7 FALSE using NONMEM 7.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"This function creates the Xpose data classes (","text":"default settings defined function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/createXposeClasses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This function creates the Xpose data classes (","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"histogram distribution conditional weighted residuals (CWRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.histogram function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"","code":"cwres.dist.hist(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"object xpose.data object. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"Returns histogram conditional weighted residuals (CWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"Displays histogram conditional weighted residuals (CWRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of conditional weighted residuals (CWRES), for Xpose 4 — cwres.dist.hist","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot cwres.dist.hist(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"QQ plot distribution conditional weighted residuals (CWRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.qq function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"","code":"cwres.dist.qq(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"object xpose.data object. ... arguments passed link{xpose.plot.qq}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"Returns QQ plot conditional weighted residuals (CWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"Displays QQ plot conditional weighted residuals (CWRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.dist.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose\n4 — cwres.dist.qq","text":"","code":"cwres.dist.qq(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"creates stack plots conditional weighted residuals (CWRES) plotted covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default xpose.plot.histogram functions. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"","code":"cwres.vs.cov(   object,   ylb = \"CWRES\",   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"object xpose.data object. ylb string giving label y-axis. NULL none. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default} link{xpose.plot.histogram}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"Returns stack xyplots histograms CWRES versus covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots histograms available. See xpose.plot.default xpose.plot.histogram details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose\n4 — cwres.vs.cov","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  cwres.vs.cov(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"creates box whisker plot conditional weighted residuals (CWRES) vs independent variable (IDV), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"","code":"cwres.vs.idv.bw(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"object xpose.data object. ... arguments passed link{xpose.plot.bw}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"Returns stack box--whisker plots CWRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"creates box whisker plot conditional weighted residuals (CWRES) vs independent variable (IDV), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling bwplots available. See xpose.plot.bw xpose.panel.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Box-and-whisker plot of conditional weighted residuals vs the independent\nvariable for Xpose 4 — cwres.vs.idv.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  cwres.vs.idv.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"plot population conditional weighted residuals (CWRES) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"","code":"cwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"Returns xyplot CWRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"Conditional weighted residuals (CWRES) plotted independent variable, specified object@Prefs@Xvardef$idv. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population conditional weighted residuals (CWRES) plotted against the\nindependent variable (IDV) for Xpose 4 — cwres.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot cwres.vs.idv(xpdb)   ## A conditioning plot cwres.vs.idv(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"creates box whisker plot conditional weighted residuals (CWRES) vs population predictions (PRED), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"","code":"cwres.vs.pred.bw(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"object xpose.data object. ... arguments passed link{xpose.plot.bw}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"Returns box--whisker plot CWRES vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"creates box whisker plot conditional weighted residuals (CWRES) vs population predictions (PRED), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments. Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling bwplots available. See xpose.plot.bw xpose.panel.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Box-and-whisker plot of conditional weighted residuals vs population\npredictions for Xpose 4 — cwres.vs.pred.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  cwres.vs.pred.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"plot population conditional weighted residuals (cwres) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"","code":"cwres.vs.pred(object, abline = c(0, 0), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"Returns xyplot CWRES vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"Conditional weighted residuals (CWRES) require extra steps calculate. See compute.cwres details. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population conditional weighted residuals (CWRES) plotted against population\npredictions (PRED) for Xpose 4 — cwres.vs.pred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  cwres.vs.pred(xpdb)   ## A conditioning plot cwres.vs.pred(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"graphical comparison  WRES CWRES plotted independent variable. Conditional weighted residuals (CWRES) require  extra steps calculate. Either add CWRES NONMEM  table files compute using information proveded  compute.cwres. wide array extra options controlling  xyplots available. See xpose.plot.default  xpose.panel.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"","code":"cwres.wres.vs.idv(   object,   ylb = \"Residuals\",   abline = c(0, 0),   smooth = TRUE,   scales = list(),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"object xpose.data object. ylb string giving label y-axis. NULL none. abline Vector arguments panel.abline function. abline drawn NULL. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. scales scales passed  xpose.plot.default. ... arguments passed xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"compound xyplot.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"Niclas Jonsson & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\nindependent variable (IDV) — cwres.wres.vs.idv","text":"","code":"cwres.wres.vs.idv(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"Graphically compares WRES CWRES plotted population predictions.Conditional weighted residuals (CWRES) require  extra steps calculate. Either add CWRES NONMEM  table files compute using information proveded  compute.cwres. wide array extra options controlling  xyplots available. See xpose.plot.default  xpose.panel.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"","code":"cwres.wres.vs.pred(   object,   ylb = \"Residuals\",   abline = c(0, 0),   smooth = TRUE,   scales = list(),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"object xpose.data object. ylb string giving label y-axis. NULL none. abline Vector arguments panel.abline function. abline drawn NULL. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. scales scales passed  xpose.plot.default ... arguments passed xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"compound xyplot.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"Niclas Jonsson & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/cwres.wres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the\npopulation predictions (PRED) — cwres.wres.vs.pred","text":"","code":"cwres.wres.vs.pred(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"Change individual objective function value vs. covariate value.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"","code":"dOFV.vs.cov(   xpdb1,   xpdb2,   covariates = xvardef(\"covariates\", xpdb1),   ylb = expression(paste(Delta, OFV[i])),   main = \"Default\",   smooth = TRUE,   abline = c(0, 0),   ablcol = \"grey\",   abllwd = 2,   abllty = \"dashed\",   max.plots.per.page = 1,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"xpdb1 Xpose data object first NONMEM run xpdb2 Xpose data object second NONMEM run covariates Covariates plot ylb Label Y axis. main Title plot. smooth smooth? abline abline description. ablcol color abline abllwd line width abline abllty type abline max.plots.per.page Plots per page. ... additional arguments function","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change in individual objective function value vs. covariate value. — dOFV.vs.cov","text":"","code":"if (FALSE) { ## read in table files xpdb8 <- xpose.data(8) xpdb11 <- xpose.data(11)  ## Make some plots dOFV.vs.cov(xpdb8,xpdb11,\"AGE\") dOFV.vs.cov(xpdb8,xpdb11,c(\"AGE\",\"SECR\")) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":null,"dir":"Reference","previous_headings":"","what":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"plot showing least influential individuals determining drop OFV two models.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"","code":"dOFV.vs.id(   xpdb1,   xpdb2,   sig.drop = -3.84,   decrease.label.number = 3,   increase.label.number = 3,   id.lab.cex = 0.6,   id.lab.pos = 2,   type = \"o\",   xlb = \"Number of subjects removed\",   ylb = expression(paste(Delta, \"OFV\")),   main = \"Default\",   sig.line.col = \"red\",   sig.line.lty = \"dotted\",   tot.line.col = \"grey\",   tot.line.lty = \"dashed\",   key = list(columns = 1, lines = list(pch = c(super.sym$pch[1:2], NA, NA), type =     list(\"o\", \"o\", \"l\", \"l\"), col = c(super.sym$col[1:2], sig.line.col, tot.line.col),     lty = c(super.sym$lty[1:2], sig.line.lty, tot.line.lty)), text =     list(c(expression(paste(Delta, OFV[i] < 0)), expression(paste(Delta, OFV[i] > 0)),     expression(paste(\"Significant  \", Delta, OFV)), expression(paste(\"Total  \", Delta,     OFV)))), corner = c(0.95, 0.5), border = T),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"xpdb1 Xpose data object first NONMEM run (\"new\" run) xpdb2 Xpose data object Second NONMEM run (\"reference\" run) sig.drop significant drop OFV? decrease.label.number many points bw labeled ID values IDs drop iOFV? increase.label.number many points bw labeled ID values IDs increase iOFV? id.lab.cex Size ID labels. id.lab.pos ID label position. type Type lines. xlb X-axis label. ylb Y-axis label. main Title plot. sig.line.col Significant OFV drop line color. sig.line.lty Significant OFV drop line type. tot.line.col Total OFV drop line color. tot.line.lty Total OFV drop line type. key Legend plot. ... Additional arguments function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV.vs.id.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change in Objective function value vs. removal of individuals. — dOFV.vs.id","text":"","code":"if (FALSE) { library(xpose4)  ## first make sure that the iofv values are read into xpose cur.dir <- getwd() setwd(paste(cur.dir,\"/LAG_TIME\",sep=\"\")) xpdb1 <- xpose.data(1) setwd(paste(cur.dir,\"/TRANSIT_MODEL\",sep=\"\")) xpdb2 <- xpose.data(1) setwd(cur.dir)  ## then make the plot dOFV.vs.id(xpdb1,xpdb2) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":null,"dir":"Reference","previous_headings":"","what":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"Change individual objective function value 1 vs. individual objective","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"","code":"dOFV1.vs.dOFV2(   xpdb1,   xpdb2,   xpdb3,   ylb = expression(paste(Delta, OFV1[i])),   xlb = expression(paste(Delta, OFV2[i])),   main = \"Default\",   smooth = NULL,   abline = c(0, 1),   ablcol = \"grey\",   abllwd = 2,   abllty = \"dashed\",   lmline = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"xpdb1 Xpose data object first NONMEM run xpdb2 Xpose data object second NONMEM run xpdb3 Xpose data object third NONMEM run ylb Label Y axis. xlb Label X axis. main Title plot. smooth smooth? abline abline description. ablcol color abline abllwd line width abline abllty type abline lmline Linear regression line? ... Additional arguments function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dOFV1.vs.dOFV2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change in individual objective function value 1 vs. individual objective\nfunction value 2. — dOFV1.vs.dOFV2","text":"","code":"if (FALSE) { ## read in table files xpdb8 <- xpose.data(8) xpdb8 <- xpose.data(9) xpdb11 <- xpose.data(11)  ## Make the plot dOFV.vs.cov(xpdb8,xpdb9,xpdb11) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":null,"dir":"Reference","previous_headings":"","what":"Check through the source dataset to detect problems — data.checkout","title":"Check through the source dataset to detect problems — data.checkout","text":"function graphically \"checks \" dataset identify errors inconsistencies.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check through the source dataset to detect problems — data.checkout","text":"","code":"data.checkout(   obj = NULL,   datafile = \".ask.\",   hlin = -99,   dotcol = \"black\",   dotpch = 16,   dotcex = 1,   idlab = \"ID\",   csv = NULL,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check through the source dataset to detect problems — data.checkout","text":"obj NULL xpose.data object. datafile data file, suitable import read.table. hlin integer, specifying line number column headers appear. dotcol Colour dots dotplot. obj xpose data object default use value defined box--whisker plots. dotpch Plotting character dots dotplot. obj xpose data object default use value defined box--whisker plots. dotcex Relative scaling dots dotplot.  obj xpose data object default use value defined box--whisker plots. idlab ID column label dataset.  Input text string. csv data file CSV format (comma separated values)?  value NULL user asked command line.  supplied function value can TRUE/FALSE. main title plot. \"default\" means Xpose creates title. ... arguments passed link[lattice]{dotplot}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check through the source dataset to detect problems — data.checkout","text":"stack dotplots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check through the source dataset to detect problems — data.checkout","text":"function creates series dotplots, one variable dataset, individual ID. Outliers clusters may easily detected manner.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check through the source dataset to detect problems — data.checkout","text":"Niclas Jonsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data.checkout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check through the source dataset to detect problems — data.checkout","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run, table and data files for run ## 5 in the current working directory  xpdb5 <- xpose.data(5)  data.checkout(xpdb5, datafile = \"mydata.dta\") data.checkout(datafile = \"mydata.dta\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or assign data from an xpose.data object. — data_extract_or_assign","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"Extracts assigns data Data SData slots \"xpose.data\" object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"","code":"Data(object, inclZeroWRES = FALSE, onlyfirst = FALSE, subset = NULL)  Data(object, quiet = TRUE, keep.structure = F) <- value  SData(   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   subset = NULL,   samp = NULL )  SData(object) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"object \"xpose.data\" object inclZeroWRES Logical value indicating whether rows WRES==0 included extracted data. onlyfirst Logical value indicating whether first line per individual included extracted data. subset Expression extracted data subset (see xsubset) quiet TRUE FALSE FALSE information printed adding data Xpose object. keep.structure TRUE FALSE ifFALSE values converted continuous categorical according rules set xpose using object@Prefs@Cat.levels, object@Prefs@DV.cat.levels values \"catab\" file. value R data.frame. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"Returns data.frame Data SData slots, excluding rows indicated arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"using Data assign data.frame Data slot \"xpose.data\" object number things happen: column data.frame checked set factor number unique values less value Cat.levels (see xpose.prefs-class). checked predefined xpose data variables exists data.frame. variable definitions exist set NULL. column identified dv xpose variable definition, checked set factor number unique values less equal DV.Cat.levels (see xpose.prefs-class). Finally, column name data.frame checked label (see xpose.prefs-class). non-existent, label set column name. SData used assign data.frame SData slot first checked number rows SData data.frame even multiple number rown Data. Next, column SData data.frame assigned class corresponding column Data data.frame (required columns Data SData). Finally, extra column, \"iter\", added SData, indicates iteration number row belongs . time, Nsim slot \"xpose.data\" object set number iterations (see nsim).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"Data(): Extract data Data(object, quiet = TRUE, keep.structure = F) <- value: assign data SData(): extract simulated data SData(object) <- value: assign simulated data","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/data_extract_or_assign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or assign data from an xpose.data object. — data_extract_or_assign","text":"","code":"xpdb <- simpraz.xpdb  ## Extract data my.dataframe <- Data(xpdb)  ## Assign data Data(xpdb) <- my.dataframe  ## Extract simulated data my.simulated.dataframe <- SData(xpdb)  ## Assign simulated data SData(xpdb) <- my.simulated.dataframe"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints the contents of an Xpose data object — db.names","title":"Prints the contents of an Xpose data object — db.names","text":"functions print summary specified Xpose object R console.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints the contents of an Xpose data object — db.names","text":"","code":"db.names(object)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prints the contents of an Xpose data object — db.names","text":"object xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prints the contents of an Xpose data object — db.names","text":"detailed summary contents specified xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prints the contents of an Xpose data object — db.names","text":"functions return detailed summary contents specified xpose.data object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prints the contents of an Xpose data object — db.names","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/db.names.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prints the contents of an Xpose data object — db.names","text":"","code":"db.names(simpraz.xpdb) #>  #> The current run number is 1. #>  #> The database contains the following observed items: #> ID TIME IPRED IWRES CWRES CL V KA ETA1 ETA2 ETA3 AGE HT WT  #> SECR SEX RACE SMOK HCTZ PROP CON OCC DV PRED RES WRES #>  #> The following variables are defined: #>  #> ID variable: ID  #> Label variable: ID  #> Independent variable: TIME  #> Occasion variable: OCC  #> Dependent variable: DV  #> Population prediction variable: PRED  #> Individual prediction variable: IPRED  #> Weighted population residual variable: WRES  #> Weighted individual residual variable: IWRES  #> Population residual variable: RES  #> Parameters: ETA3 ETA2 ETA1 KA V CL #> Covariates: SEX RACE SMOK HCTZ PROP CON OCC AGE HT WT SECR #>   ( Continuous: AGE HT WT SECR ) #>   ( Categorical: SEX RACE SMOK HCTZ PROP CON OCC ) #> Variability parameters: ETA1 ETA2 ETA3 #> Missing value label: -99  #> NULL"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"plot observations (DV) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"","code":"dv.vs.idv(object, smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"object xpose.data object. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"Returns xyplot DV vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"wide array extra options controlling xyplot available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV) plotted against the independent variable (IDV) for Xpose 4 — dv.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  dv.vs.idv(xpdb)   ## A conditioning plot dv.vs.idv(xpdb, by=\"HCTZ\")   ## Logarithmic Y-axis dv.vs.idv(xpdb, logy=TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"plot dependent variable (DV) vs individual predictions (IPRED) conditioned covariates, specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"","code":"dv.vs.ipred.by.cov(   object,   covs = \"Default\",   abline = c(0, 1),   smooth = TRUE,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"object xpose.data object. covs vector covariates use plot. \"Default\"  covariates defined object@Prefs@Xvardef$Covariates used. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"Returns stack xyplots DV vs IPRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling xyplot available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dependent variable vs individual predictions, conditioned on covariates, for\nXpose 4 — dv.vs.ipred.by.cov","text":"","code":"dv.vs.ipred.by.cov(simpraz.xpdb, covs=c(\"HCTZ\",\"WT\"), max.plots.per.page=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"plot dependent variable (DV) vs individual predictions (IPRED) conditioned independent variable, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"","code":"dv.vs.ipred.by.idv(object, abline = c(0, 1), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"Returns stack xyplots DV vs IPRED, conditioned independent variable.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"wide array extra options controlling xyplot available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.by.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dependent variable vs individual predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.ipred.by.idv","text":"","code":"dv.vs.ipred.by.idv(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"plot observations (DV) vs individual predictions (IPRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"","code":"dv.vs.ipred(object, abline = c(0, 1), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"Returns xyplot DV vs IPRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"wide array extra options controlling xyplot available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.ipred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV) plotted against individual predictions (IPRED) for Xpose 4 — dv.vs.ipred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  dv.vs.ipred(xpdb)   ## A conditioning plot dv.vs.ipred(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"plot dependent variable (DV) vs population predictions (PRED) conditioned covariates, specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"","code":"dv.vs.pred.by.cov(   object,   covs = \"Default\",   abline = c(0, 1),   smooth = TRUE,   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"object xpose.data object. covs vector covariates use plot. \"Default\"  covariates defined object@Prefs@Xvardef$Covariates used. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"Returns stack xyplots DV vs PRED, conditioned covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"covariates Xpose data object, specified object@Prefs@Xvardef$Covariates, evaluated turn, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dependent variable vs population predictions, conditioned on covariates, for\nXpose 4 — dv.vs.pred.by.cov","text":"","code":"dv.vs.pred.by.cov(simpraz.xpdb, covs=c(\"HCTZ\",\"WT\"), max.plots.per.page=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"plot dependent variable (DV) vs population predictions (PRED) conditioned independent variable, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"","code":"dv.vs.pred.by.idv(object, abline = c(0, 1), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"Returns stack xyplots DV vs PRED, conditioned independent variable.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.by.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dependent variable vs population predictions, conditioned on independent\nvariable, for Xpose 4 — dv.vs.pred.by.idv","text":"","code":"dv.vs.pred.by.idv(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"plot observations (DV) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"","code":"dv.vs.pred(object, abline = c(0, 1), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"Returns xyplot DV vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV) plotted against population predictions (PRED) for Xpose 4 — dv.vs.pred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A vanilla plot dv.vs.pred(xpdb)   ## A conditioning plot dv.vs.pred(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"compound plot consisting plots observations (DV) individual predictions (IPRED) population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"","code":"dv.vs.pred.ipred(   object,   xlb = \"Predictions\",   layout = c(2, 1),   abline = c(0, 1),   lmline = TRUE,   smooth = NULL,   scales = list(),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"object xpose.data object. xlb string giving label x-axis. NULL none. layout list giving layout graphs plot, columns rows. abline Vector arguments panel.abline function. abline drawn NULL. lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) smooth NULL TRUE value indicating whether x-y smooth superimposed. scales list used scales argument xyplot. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"Returns compound plot comprising plots observations (DV) individual predictions (IPRED) population predictions (PRED).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"Plots DV vs PRED IPRED presented side side comparison. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/dv.vs.pred.ipred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV) are plotted against individual predictions (IPRED) and\npopulation predictions (PRED), for Xpose 4 — dv.vs.pred.ipred","text":"","code":"dv.vs.pred.ipred(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":null,"dir":"Reference","previous_headings":"","what":"Exports Xpose graphics settings to a file. — export.graph.par","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"function exports graphics settings specified Xpose data object file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"","code":"export.graph.par(object)  xpose.write(object, file = \"xpose.ini\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"object xpose.data object. file file contain exported Xpose settings.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"Null.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"function exports graphics settings (contents object@Prefs@Graph.prefs) given xpose.data object file, typically 'xpose.ini'. wrapper xpose.write. Note file format used import.variable.definitions export.variable.definitions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"xpose.write(): export graphics settings specified Xpose data object file.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.graph.par.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exports Xpose graphics settings to a file. — export.graph.par","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## For a filename prompt export.graph.par(xpdb5)  ## Command-line driven xpose.write(xpdb5, \"c:/XposeSettings/mytheme.ini\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"function exports variable definitions specified Xpose data object file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"","code":"export.variable.definitions(object, file = \"\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"object xpose.data object. file file name string.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"Null.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"function exports variable definitions (contents object@Prefs@Xvardef) given xpose.data object file, typically 'xpose.vardefs.ini'.  Note file format used graphics settings. wrapper R function dput.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/export.variable.definitions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exports Xpose variable definitions to a file from an Xpose data object. — export.variable.definitions","text":"","code":"od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-f00e6fae00d8efe8984ec802f708f91a\" #> [2] \"downlit\"                                 export.variable.definitions(simpraz.xpdb,file=\"xpose.vardefs.ini\") (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"xpose.vardefs.ini\"  file.remove(new.files) # remove this file #> [1] TRUE setwd(od)  # restore working directory"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/find.right.table.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal functions for the VPC — find.right.table","title":"Internal functions for the VPC — find.right.table","text":"Internal functions VPC","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/find.right.table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal functions for the VPC — find.right.table","text":"","code":"find.right.table(   object,   inclZeroWRES,   onlyfirst,   samp,   PI.subset,   subscripts,   PI.bin.table,   panel.number,   ... )  setup.PPI(PIlimits, PI.mirror, tmp.table, ...)  get.polygon.regions(PPI, PI.mirror, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/find.right.table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal functions for the VPC — find.right.table","text":"object Xpose object inclZeroWRES Include row sof data WRES=0 onlyfirst Use first data individual samp sample number PI.subset Prediction interval subset subscripts subscripts PI.bin.table prediction interval binning table panel.number panel number ... Extra options passed arguments PIlimits Prediction interval limits PI.mirror Prediction interval mirror tmp.table temporary table PPI Plot prediction intervals","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/find.right.table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal functions for the VPC — find.right.table","text":"Returned xpose.VPC","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":null,"dir":"Reference","previous_headings":"","what":"Structured goodness of fit diagnostics. — gof","title":"Structured goodness of fit diagnostics. — gof","text":"template function creating structured goodness fit diagnostics using functions Xpose specific library.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Structured goodness of fit diagnostics. — gof","text":"","code":"gof(   runno = NULL,   save = FALSE,   onefile = FALSE,   saveType = \"pdf\",   pageWidth = 7.6,   pageHeight = 4.9,   structural = TRUE,   residual = TRUE,   covariate = FALSE,   iiv = FALSE,   iov = FALSE,   all = FALSE,   myTrace = xpPage )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Structured goodness of fit diagnostics. — gof","text":"runno run number fo Xpose identify appropriate files read. addition runno used construct file name save plots . runno can also NULL cases function used non-Xpose based code. save Logical. TRUE plot(s) saved file. FALSE plot(s) displayed screen. plot(s) saved file named function name followed word 'run', run number, order number followed file name extension appropriate selected saveType. example 'gofrun1-01.pdf' first plot file created script called gof based output run 1 saveType='pdf'. onefile Logical. TRUE plots save single file FALSE plot saved separate file. latter case, file incremented order number (01-99). saveType type graphics file produce save=TRUE. Allowed values 'pdf' (default), 'wmf' (Windows) 'png'. pageWidth width graphics device inches. pageHeight height graphics device inches. structural Logical. TRUE code structural model section (see ) executed FALSE . residual Logical. TRUE code residual model section (see ) executed FALSE . covariate Logical. TRUE code covariate model section (see ) executed FALSE . iiv Logical. TRUE code IIV model section (see ) executed FALSE . iov Logical. TRUE code IOV model section (see ) executed FALSE . Logical. TRUE code sections (see ) executed. myTrace NULL name function. value myTrace can used lattice page= argument annotate plots traceability.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Structured goodness of fit diagnostics. — gof","text":"return anything unless user specify return value.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Structured goodness of fit diagnostics. — gof","text":"gof function provided template facilitate (structured) use functions Xpose specific library. Xpose specific extensively described 'Xpose Bestiary'. function can renamed multiple scripts can used parallel. function set make easy display plots screen well save files. latter case, plots save sub-directory called 'Plots'. arguments structural, residual, covariate, iiv, iov just \"switches\" different parts code (-blocks). blocks can removed default values arguments changed better suit needs user. also possible add tracing information produced plots. done via myTrace argument. non-NULL value function returns panel.text object. default xpPage function put string concatenated device name, function name, working directory date, small, faint grey, font bottom graph page. Note user need add page=myTrace argument Xpose functions effect. function calls support function called gofSetup, responsible setting graphics device determining file names saved graphs.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Structured goodness of fit diagnostics. — gof","text":"E. Niclas Jonsson, Mats Karlsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/gof.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Structured goodness of fit diagnostics. — gof","text":"","code":"if (FALSE) { ## This is an example of how the function may be setup by a user.  library(xpose4) mygof <- gof fix(mygof)  myggof <- function (runno = NULL, save = FALSE, onefile = FALSE, saveType = \"pdf\",           pageWidth = 7.6, pageHeight = 4.9, structural = TRUE, residual = TRUE,           covariate = FALSE, iiv = FALSE, iov = FALSE, all = FALSE, myTrace=xpPage) {           gofSetup(runno, save, onefile, saveType, pageWidth, pageHeight)          xpdb <- xpose.data(runno)           if (structural || all) {           xplot <- dv.vs.pred.ipred(xpdb, page = myPage)           print(xplot)          }          if (residual || all) {           xplot <- absval.wres.vs.pred(xpdb, page = myPage)           print(xplot)          }          if (covariate || all) {          }          if (iiv || all) {          }          if (iov || all) {          }          if (save) dev.off()     invisible()   }  ## The function can then be execute, e.g.: mygof(1)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":null,"dir":"Reference","previous_headings":"","what":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"function imports graphics settings specified Xpose data object file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"","code":"import.graph.par(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"xpose.data object (classic = FALSE) null (classic = TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"function imports graphics settings (contents object@Prefs@Graph.prefs) given xpose.data object file, typically 'xpose.ini'. wrapper xpose.read. returns xpose.data object.  Note file format used import.variable.definitions export.variable.definitions.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.graph.par.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Imports Xpose graphics settings from a file to an Xpose data object. — import.graph.par","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Import graphics preferences you saved earlier using export.graph.par xpdb5 <- import.graph.par(xpdb5)  ## Command-line driven xpdb5 <- xpose.read(xpdb5, \"c:/XposeSettings/mytheme.ini\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"function imports variable definitions specified Xpose data object file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"","code":"import.variable.definitions(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"xpose.data object (classic == FALSE) null (classic == TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"function imports variable definitions (contents object@Prefs@Xvardef) given xpose.data object file, typically 'xpose.vardefs.ini'.  returns xpose.data object. Note file format used graphics settings. wrapper R function dget.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/import.variable.definitions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Imports Xpose variable definitions from a file to an Xpose data object. — import.variable.definitions","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  xpdb5 <- import.variable.definitions(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"compound plot consisting plots observations (DV), individual predictions (IPRED) population predictions (PRED) independent variable every individual dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"","code":"ind.plots(   object,   y.vals = c(xvardef(\"dv\", new.obj), xvardef(\"ipred\", new.obj), xvardef(\"pred\", new.obj)),   x.vals = xvardef(\"idv\", new.obj),   id.vals = xvardef(\"id\", new.obj),   key.text = y.vals,   main = \"Default\",   key = \"Default\",   xlb = xlabel(xvardef(\"idv\", object), object),   ylb = NULL,   layout = c(4, 4),   inclZeroWRES = FALSE,   subset = xsubset(object),   type = \"o\",   grid = FALSE,   col = c(1, 2, 4),   lty = c(0, 1, 3),   lwd = c(1, 1, 1),   pch = c(21, 32, 32),   cex = c(0.7, 0.7, 0.7),   fill = c(\"lightgrey\", 0, 0),   prompt = FALSE,   mirror = NULL,   main.cex = 0.9,   max.plots.per.page = 1,   pch.ip.sp = c(21, 19, 18),   cex.ip.sp = c(0.7, 0.4, 0.4),   y.vals.subset = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"object xpose.data object. y.vals Y values use. x.vals X values use. id.vals ID values use. key.text text legend use. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. key Create legend. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. layout list giving layout graphs plot, columns rows. default 4x4. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default TRUE. subset string giving subset expression applied data plotting. See xsubset. type 1-character string giving type plot desired. default \"o\", -plotted points lines. See xpose.plot.default. grid plots grid plot? col list three elements, giving plotting characters observations, individual predictions, population predictions, order. default black DV, red individual predictions, blue population predictions. lty list three elements, giving line types observations, individual predictions, population predictions, order. lwd list three elements, giving line widths observations, individual predictions, population predictions, order. pch list three elements, giving plotting characters observations, individual predictions, population predictions, order. cex list three elements, giving relative point size observations, individual predictions, population predictions, order. default c(0.7,0.7,0.7). fill Fill circles points? prompt Specifies whether user prompted press RETURN plot pages. Default TRUE. mirror Mirror plots yet implemented function argument must contain value NULL main.cex size title. max.plots.per.page Maximum number plots per page. pch.ip.sp panel just one observation specifies type points DV, IPRED PRED respectively. cex.ip.sp panel just one observation specifies size points DV, IPRED PRED respectively. y.vals.subset Used subset DV, IPRED PRED variables separately.  Either NULL vector three strings, corresponding subset DV, IPRED PRED respectively. See examples . ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"Returns stack plots observations (DV) individual predictions (IPRED) population predictions (PRED). wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"Matrices individual plots presented comparison closer inspection.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observations (DV), individual predictions (IPRED) and population predictions\n(PRED) are plotted against the independent variable for every individual in\nthe dataset, for Xpose 4 — ind.plots","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## Monochrome, suitable for manuscript or report ind.plots(xpdb,    subset=\"ID>40 & ID<57\",    col=c(1,1,1),    lty=c(0,2,3),    strip=function(..., bg)      strip.default(..., bg=\"grey\"))       if (FALSE) {     ## IF we simulate in NONMEM using a dense grid of time points ## and all non-observed DV items are equal to zero. ind.plots(xpdb,inclZeroWRES=TRUE,y.vals.subset=c(\"DV!=0\",\"NULL\",\"NULL\"))   # to plot individual plots of multiple variables ind.plots(xpdb,subset=\"FLAG==1\") ind.plots(xpdb,subset=\"FLAG==2\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"compound plot consisting histograms distribution weighted residuals (weighted residual available NONMEM) every individual dataset.  wrapper encapsulating arguments xpose.plot.histogram function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"","code":"ind.plots.cwres.hist(object, wres = \"cwres\", ...)  ind.plots.wres.hist(   object,   main = \"Default\",   wres = \"wres\",   ylb = NULL,   layout = c(4, 4),   inclZeroWRES = FALSE,   subset = xsubset(object),   scales = list(cex = 0.7, tck = 0.5),   aspect = \"fill\",   force.by.factor = TRUE,   ids = F,   as.table = TRUE,   hicol = object@Prefs@Graph.prefs$hicol,   hilty = object@Prefs@Graph.prefs$hilty,   hilwd = object@Prefs@Graph.prefs$hilwd,   hidcol = object@Prefs@Graph.prefs$hidcol,   hidlty = object@Prefs@Graph.prefs$hidlty,   hidlwd = object@Prefs@Graph.prefs$hidlwd,   hiborder = object@Prefs@Graph.prefs$hiborder,   prompt = FALSE,   mirror = NULL,   main.cex = 0.9,   max.plots.per.page = 1,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"object xpose.data object. wres weighted residual plot? Defaults WRES. ... arguments passed xpose.plot.histogram. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ylb string giving label y-axis. NULL none. layout list giving layout graphs plot, columns rows. default 4x4. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default FALSE. subset string giving subset expression applied data plotting. See xsubset. scales see xpose.plot.histogram aspect see xpose.plot.histogram force..factor see xpose.plot.histogram ids see xpose.plot.histogram .table see xpose.plot.histogram hicol fill colour histogram - integer string.  default blue (see histogram). hilty border line type histogram - integer.  default 1 (see histogram). hilwd border line width histogram - integer.  default 1 (see histogram). hidcol fill colour density line - integer string. default black (see histogram). hidlty border line type density line - integer.  default 1 (see histogram). hidlwd border line width density line - integer.  default 1 (see histogram). hiborder border colour histogram - integer string. default black (see histogram). prompt Specifies whether user prompted press RETURN plot pages. Default FALSE. mirror Mirror plots yet implemented function argument must contain value NULL main.cex size title. max.plots.per.page Maximum number plots per page","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"Returns compound plot comprising histograms weighted residual conditioned individual.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"Matrices histograms weighted residuals included individual displayed. ind.plots.cwres.hist just wrapper ind.plots.wres.hist(object,wres=\"cwres\").","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"ind.plots.cwres.hist(): Histograms conditional  weighted residuals individual","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histograms of weighted residuals for each individual in an Xpose data\nobject, for Xpose 4 — ind.plots.cwres.hist","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A plot of the first 16 individuals ind.plots.cwres.hist(xpdb, subset=\"ID<18\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"compound plot consisting QQ plots distribution weighted residuals (weighted residual produced NONMEM) every individual dataset.  function wrapper encapsulating arguments xpose.plot.qq function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"","code":"ind.plots.cwres.qq(object, wres = \"cwres\", ...)  ind.plots.wres.qq(   object,   main = \"Default\",   wres = \"wres\",   layout = c(4, 4),   inclZeroWRES = FALSE,   subset = xsubset(object),   scales = list(cex = 0.7, tck = 0.5),   aspect = \"fill\",   force.by.factor = TRUE,   ids = F,   as.table = TRUE,   type = \"o\",   pch = object@Prefs@Graph.prefs$pch,   col = object@Prefs@Graph.prefs$col,   cex = object@Prefs@Graph.prefs$cex,   abllty = object@Prefs@Graph.prefs$abllty,   abllwd = object@Prefs@Graph.prefs$abllwd,   ablcol = object@Prefs@Graph.prefs$ablcol,   prompt = FALSE,   main.cex = 0.9,   mirror = NULL,   max.plots.per.page = 1,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"object xpose.data object. wres weighted residual plot? Defaults WRES. ... arguments passed link{xpose.plot.qq}. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. layout list giving layout graphs plot, columns rows. default 4x4. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default FALSE. subset string giving subset expression applied data plotting. See xsubset. scales See xpose.plot.qq. aspect See xpose.plot.qq. force..factor See xpose.plot.qq. ids See xpose.plot.qq. .table See xpose.plot.qq. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. pch plotting character, symbol, use. Specified integer. See R help points. default open circle. col color lines points. Specified integer text string. full list obtained R command colours(). default blue (col=4). cex amount plotting text symbols scaled relative default. 'NULL' 'NA' equivalent '1.0'. abllty Line type line identity. abllwd Line width line identity. ablcol Line colour line identity. prompt Specifies whether user prompted press RETURN plot pages. Default FALSE. main.cex size title. mirror Mirror plots yet implemented function argument must contain value NULL max.plots.per.page Maximum number plots per page","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"Returns compound plot comprising QQ plots weighted residuals conditioned individual.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"Matrices Q-Q plots weighted residuals included individual displayed. wide array extra options controlling Q-Q plots available. See xpose.plot.qq details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"ind.plots.cwres.qq(): Q-Q plots conditional  weighted residuals individual","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ind.plots.wres.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plots of weighted residuals for each individual in an\nXpose data object, for Xpose 4 — ind.plots.cwres.qq","text":"","code":"ind.plots.cwres.qq(simpraz.xpdb,subset=\"ID<18\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"plot Individual predictions (IPRED) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"","code":"ipred.vs.idv(object, smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"object xpose.data object. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"Returns xyplot IPRED vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ipred.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Individual predictions (IPRED) plotted against the independent variable\n(IDV) for Xpose 4 — ipred.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ipred.vs.idv(xpdb)   ## A conditioning plot ipred.vs.idv(xpdb, by=\"HCTZ\")   ## Logarithmic Y-axis ipred.vs.idv(xpdb, logy=TRUE)   ## Custom colours and symbols, IDs ipred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"histogram distribution individual weighted residuals (IWRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.histogram function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"","code":"iwres.dist.hist(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"object xpose.data object. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"Returns histogram individual weighted residuals (IWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"Displays histogram individual weighted residuals (IWRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.hist","text":"","code":"iwres.dist.hist(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"QQ plot distribution individual weighted residuals (IWRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.qq function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"","code":"iwres.dist.qq(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"object xpose.data object. ... arguments passed link{xpose.plot.qq}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"Returns QQ plot individual weighted residuals (IWRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"Displays QQ plot individual weighted residuals (IWRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.dist.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4 — iwres.dist.qq","text":"","code":"iwres.dist.qq(simpraz.xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"plot individual weighted residuals (IWRES) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function.  options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"","code":"iwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. , default c(0,0), specifying horizontal line y=0. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"Returns xyplot IWRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/iwres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Individual weighted residuals (IWRES) plotted against the independent\nvariable (IDV) for Xpose 4 — iwres.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  iwres.vs.idv(xpdb)   ## A conditioning plot iwres.vs.idv(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"Kaplan-Meier plots (repeated) time--event data.  Includes VPCs.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"","code":"kaplan.plot(   x = \"TIME\",   y = \"DV\",   id = \"ID\",   data = NULL,   evid = \"EVID\",   by = NULL,   xlab = \"Time\",   ylab = \"Default\",   object = NULL,   events.to.plot = \"All\",   sim.data = NULL,   sim.zip.file = NULL,   VPC = FALSE,   nsim.lab = \"simNumber\",   sim.evct.lab = \"counter\",   probs = c(0.025, 0.975),   add.baseline = T,   add.last.area = T,   subset = NULL,   main = \"Default\",   main.sub = \"Default\",   main.sub.cex = 0.8,   nbins = NULL,   real.type = \"l\",   real.lty = 1,   real.lwd = 1,   real.col = \"blue\",   real.se = if (!is.null(sim.data)) F else T,   real.se.type = \"l\",   real.se.lty = 2,   real.se.lwd = 0.5,   real.se.col = \"red\",   cens.type = \"l\",   cens.lty = 1,   cens.col = \"black\",   cens.lwd = 1,   cens.rll = 0.02,   inclZeroWRES = TRUE,   onlyfirst = FALSE,   samp = NULL,   poly.alpha = 1,   poly.fill = \"lightgreen\",   poly.line.col = \"darkgreen\",   poly.lty = 2,   censor.lines = TRUE,   ylim = c(-5, 105),   cov = NULL,   cov.fun = \"mean\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"x independent variable. y dependent variable. event (>0) event (0). id ID variable dataset. data dataset can used instead data Xpose object. Must form xpose data object xpdb@Data. evid EVID data item.  present rows considered events (can censored event). Otherwise, EVID!=0 dropped data set. vector conditioning variables. xlab X-axis label ylab Y-axis label object Xpose object. Needed data supplied. events..plot Vector events plotted.  \"\" means events plotted. sim.data simulated data file.  table file one header row , least, columns headers corresponding x, y, id, (used), nsim.lab sim.evct.lab. sim.zip.file sim.data can \\.zip format xpose unzip file reading data.  Must structure described sim.data. VPC TRUE FALSE.  TRUE Xpose search zipped file name paste(\"simtab\",object@Runno,\".zip\",sep=\"\"), example \"simtab42.zip\". nsim.lab column header sim.data contains simulation number row data. sim.evct.lab column header sim.data contains individual event counter information.  individual event counter increase one event (censored event) occurs. probs probabilities (non-parametric percentiles) use computation prediction intervals simulated data. add.baseline (x=0,y=1) baseline measurement added individual dataset. Otherwise plot begin first event dataset. add.last.area area added VPC extending last PI? subset subset data sim.data use. main title plot. Can also NULL \"Default\". main.sub title subplots. Must list, length number subplots (actual graphs), NULL \"Default\". main.sub.cex size title subplots. nbins number bins use VPC.  NULL, number unique x values sim.data used. real.type Type real data. real.lty Line type (lty) curve original (real) data. real.lwd Line width (lwd) real data. real.col Color curve original (real) data. real.se standard errors real (non simulated) data plotted?  Calculated using survfit. real.se.type Type standard errors. real.se.lty Line type (lty) standard error lines. real.se.lwd Line width (lwd) standard error lines. real.se.col Color standard error lines. cens.type Type censored lines. cens.lty Line type (lty) censored lines. cens.col Color censored lines. cens.lwd Line width censored lines. cens.rll relative line length censored line compared limits y-axis. inclZeroWRES Include WRES=0 rows real data set plots? onlyfirst Include first measurement real data plots? samp Simulated data xpose data object can used \"real\" data.  samp number selecting simulated data set use. poly.alpha transparency VPC shaded region. poly.fill fill color VPC shaded region. poly.line.col line colors VPC region. poly.lty line type VPC region. censor.lines censored observations marked plot? ylim Limits y-axes cov covariate dataset plot instead survival curve. cov.fun summary function covariate dataset plot instead survival curve. ... Additional arguments passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"returns object class \"xpose.multiple.plot\".","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/kaplan.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier plots of (repeated) time-to-event data — kaplan.plot","text":"","code":"if (FALSE) { library(xpose4)  ## Read in the data runno <- \"57\" xpdb <- xpose.data(runno)  #################################### # here are the real data plots ####################################  kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,             events.to.plot=c(1,2),             by=c(\"DOSE==0\",\"DOSE!=0\")) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,             events.to.plot=c(1,2),             by=c(\"DOSE==0\",\"DOSE==10\",             \"DOSE==50\",\"DOSE==200\"))  ## make a PDF of the plots pdf(file=paste(\"run\",runno,\"_kaplan.pdf\",sep=\"\")) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,             by=c(\"DOSE==0\",\"DOSE==10\",             \"DOSE==50\",\"DOSE==200\")) dev.off()  #################################### ## VPC plots ####################################  kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,VPC=T,events.to.plot=c(1)) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,VPC=T,             events.to.plot=c(1,2,3),             by=c(\"DOSE==0\",\"DOSE!=0\")) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,VPC=T,             events.to.plot=c(1),             by=c(\"DOSE==0\",\"DOSE==10\",\"DOSE==50\",\"DOSE==200\"))  ## make a PDF of all plots pdf(file=paste(\"run\",runno,\"_kaplan.pdf\",sep=\"\")) kaplan.plot(x=\"TIME\",y=\"DV\",object=xpdb,VPC=T,             by=c(\"DOSE==0\",\"DOSE==10\",\"DOSE==50\",\"DOSE==200\")) dev.off() }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/make.sb.data.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stacked bar data set. — make.sb.data","title":"Make stacked bar data set. — make.sb.data","text":"Function make stacked bar data set categorical data plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/make.sb.data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stacked bar data set. — make.sb.data","text":"","code":"make.sb.data(data, idv, dv, nbins = 6, by = NULL, by.nbins = 6, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/make.sb.data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make stacked bar data set. — make.sb.data","text":"data Data set transform. idv independent variable. dv dependent variable. nbins number bins. Conditioning variable. .nbins .nbins. ... additional arguments.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/make.sb.data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Make stacked bar data set. — make.sb.data","text":"Xpose team.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"function takes output npc command Perl Speaks NONMEM (PsN) makes coverage plot.  coverage plot NPC looks different prediction intervals (PIs) data point calculates total number data points data set lying outside PIs. plot shows relative amount data points outside PI compared expected amount PI.  addition confidence interval around values computed based simulated data.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"","code":"npc.coverage(   npc.info = \"npc_results.csv\",   main = \"Default\",   main.sub = NULL,   main.sub.cex = 0.85,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"npc.info results file npc command PsN. example, npc_results.csv, file separate directory ./npc_dir1/npc_results.csv. main string giving plot title NULL none. \"Default\" creates default title. main.sub Used names plot using multiple plots. vector c(\"Group 1\",\"Group 2\") main.sub.cex size main.sub titles. ... arguments passed xpose.multiple.plot.default, xyplot others. Please see functions () descriptions can .","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"list plots","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"additional-arguments-for-the-npc-coverage-plots","dir":"Reference","previous_headings":"","what":"Additional arguments for the NPC coverage plots","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"Additional plot features CI Specifies whether confidence intervals (lines, shaded area ) added plot. Allowed values : \"area\",  \"lines\", \"\", NULL. mark.outside.data points outside CI marked different color identify . Allowed values TRUE FALSE. abline line mark value y=1? Possible values TRUE, FALSE NULL. Line area control. See plot, grid.polygon xyplot details. CI.area.col Color area CI. Defaults \"blue\" CI.area.alpha Transparency CI.area.col. Defaults 0.3. ab.lwd width abline. Default 1. ab.lty Line type abline. Default \"dashed\" CI.upper.lty Line type line upper edge CI. CI.upper.col Color line upper edge CI. CI.upper.lwd line width line upper edge CI. CI.lower.lty line type lower edge CI. CI.lower.col color line lower edge CI. CI.lower.lwd line width line lower edge CI. obs.col color observed values. obs.pch type point use observed values. obs.lty type line use observed values. obs.type combination lines points use observed values. Default \"b\" . obs.cex size points use observed values. obs.lwd line width use observed values. .col color observed values lie outside CI. used mark.outside.data=TRUE. .pch type point use observed values lie outside CI. used mark.outside.data = TRUE. .cex size points observed values lie outside CI. used mark.outside.data = TRUE. .lwd line width observed values lie outside CI. used mark.outside.data = TRUE.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/npc.coverage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot the coverage of the Numerical Predictive Check — npc.coverage","text":"","code":"if (FALSE) { library(xpose4)  npc.coverage()  ## to read files in a directory different than the current working directory  npc.file <- \"./another_directory/npc_results.csv\" npc.coverage(npc.info=npc.file) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or set the value of the Nsim slot. — nsim","title":"Extract or set the value of the Nsim slot. — nsim","text":"Extract set value Nsim slot \"xpose.data\" object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or set the value of the Nsim slot. — nsim","text":"","code":"nsim(object)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or set the value of the Nsim slot. — nsim","text":"object \"xpose.data\" object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or set the value of the Nsim slot. — nsim","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/nsim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or set the value of the Nsim slot. — nsim","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Report number of simulations nsim(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"functions plot parameter covariate values stored Xpose data object using histograms.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"","code":"cov.hist(object, onlyfirst = TRUE, main = \"Default\", ...)  parm.hist(object, onlyfirst = TRUE, main = \"Default\", ...)  ranpar.hist(object, onlyfirst = TRUE, main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"object xpose.data object. onlyfirst Logical value indicating first row per individual included plot. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"Delivers stack histograms.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"parameters covariates Xpose data object, specified object@Prefs@Xvardef$parms, object@Prefs@Xvardef$covariates object@Prefs@Xvardef$ranpar evaluated turn, creating stack histograms. wide array extra options controlling histograms available. See xpose.plot.histogram details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"cov.hist(): Covariate distributions parm.hist(): parameter distributions ranpar.hist(): random parameter distributions","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the parameter or covariate distributions using a histogram — par_cov_hist","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## Parameter histograms parm.hist(xpdb)    ## Covariate distribution, in green cov.hist(xpdb, hicol=11, hidcol=\"DarkGreen\", hiborder=\"White\")     ## Random parameter histograms ranpar.hist(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"functions plot parameter covariate values stored Xpose data object using Q-Q plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"","code":"cov.qq(object, onlyfirst = TRUE, main = \"Default\", ...)  parm.qq(object, onlyfirst = TRUE, main = \"Default\", ...)  ranpar.qq(object, onlyfirst = TRUE, main = \"Default\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"object xpose.data object. onlyfirst Logical value indicating first row per individual included plot. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.qq.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"Delivers stack Q-Q plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"parameters covariates Xpose data object, specified object@Prefs@Xvardef$parms, object@Prefs@Xvardef$ranpar object@Prefs@Xvardef$covariates, evaluated turn, creating stack Q-Q plots. wide array extra options controlling Q-Q plots available. See xpose.plot.qq details.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"cov.qq(): Covariate distributions parm.qq(): parameter distributions ranpar.qq(): random parameter distributions","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the parameter or covariate distributions using quantile-quantile (Q-Q)\nplots — par_cov_qq","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## parameter histograms parm.qq(xpdb)    ## A stack of random parameter histograms ranpar.qq(xpdb)   ## Covariate distribution, in green with red line of identity cov.qq(xpdb, col=11, ablcol=2)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"functions plot scatterplot matrices parameters, random parameters covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"","code":"cov.splom(   object,   main = xpose.multiple.plot.title(object = object, plot.text =     \"Scatterplot matrix of covariates\", ...),   varnames = NULL,   onlyfirst = TRUE,   smooth = TRUE,   lmline = NULL,   ... )  parm.splom(   object,   main = xpose.multiple.plot.title(object = object, plot.text =     \"Scatterplot matrix of parameters\", ...),   varnames = NULL,   onlyfirst = TRUE,   smooth = TRUE,   lmline = NULL,   ... )  ranpar.splom(   object,   main = xpose.multiple.plot.title(object = object, plot.text =     \"Scatterplot matrix of random parameters\", ...),   varnames = NULL,   onlyfirst = TRUE,   smooth = TRUE,   lmline = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"object xpose.data object. main string giving plot title NULL none. varnames vector strings containing labels variables scatterplot matrix. onlyfirst Logical value indicating first row per individual included plot. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"Delivers scatterplot matrix.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"parameters covariates Xpose data object, specified object@Prefs@Xvardef$parms, object@Prefs@Xvardef$ranpar object@Prefs@Xvardef$covariates, plotted together scatterplot matrices. wide array extra options controlling scatterplot matrices available. See xpose.plot.splom details. control appearance labels names scatterplot matrix plots can try varname.cex=0.5 axis.text.cex=0.5 (changes tick labels variable names half large normal).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"cov.splom(): scatterplot matrix covariates parm.splom(): scatterplot matrix parameters ranpar.splom(): scatterplot matrix random parameters","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_splom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot scatterplot matrices of parameters, random parameters or covariates — cov.splom","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  ## A scatterplot matrix of parameters, grouped by sex parm.splom(xpdb, groups=\"SEX\")   ## A scatterplot matrix of ETAs, grouped by sex ranpar.splom(xpdb, groups=\"SEX\")   ## Covariate scatterplots, with text customization cov.splom(xpdb, varname.cex=0.4, axis.text.cex=0.4, smooth=NULL, cex=0.4) #> SEX is categorical and will not be #>  shown in the scatterplot #> RACE is categorical and will not be #>  shown in the scatterplot #> SMOK is categorical and will not be #>  shown in the scatterplot #> HCTZ is categorical and will not be #>  shown in the scatterplot #> PROP is categorical and will not be #>  shown in the scatterplot #> CON is categorical and will not be #>  shown in the scatterplot #> OCC is categorical and will not be #>  shown in the scatterplot"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize individual parameter values and covariates — par_cov_summary","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"functions produce tables, printed screen, summarizing individual parameter values covariates dataset Xpose 4.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"","code":"cov.summary(   object,   onlyfirst = TRUE,   subset = xsubset(object),   inclZeroWRES = FALSE,   out.file = \".screen\",   main = \"Default\",   fill = \"gray\",   values.to.use = xvardef(\"covariates\", object),   value.name = \"Covariate\",   ... )  parm.summary(   object,   onlyfirst = TRUE,   subset = xsubset(object),   inclZeroWRES = FALSE,   out.file = \".screen\",   main = \"Default\",   fill = \"gray\",   values.to.use = xvardef(\"parms\", object),   value.name = \"Parameter\",   max.plots.per.page = 1,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"object xpose.data object. onlyfirst Logical value indicating first row per individual included plot. subset string giving subset expression applied data plotting. See xsubset. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. default FALSE. .file results output .  Can \".screen\", \".ask\", \".graph\" filename quotes. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. fill color fill boxes table table printed \".graph\" values..use values summarized value.name name values ... arguments passed Data SData. max.plots.per.page Maximum plots per page.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"Returned matrix values table. parm.summary cov.summary produce summaries parameters covariates, respectively. parm.summary produces less attractive output supports mirror functionality. parm.summary cov.summary utilize print.char.matrix print information screen.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"cov.summary(): Covariate summary parm.summary(): Parameter summary","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/par_cov_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize individual parameter values and covariates — par_cov_summary","text":"","code":"parm.summary(simpraz.xpdb) #>  #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |    |           Mean|    SD|        Q1|     Median|       Q3|           Range| N| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |ETA3|-0.073881265625|0.6318|-0.3498325|   0.035202|0.3588275|  -1.9354-1.0009|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |ETA2|-0.007861181875| 0.349|   -0.2862|   0.021967|0.1990975| -0.78827-0.7406|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |ETA1|0.0075852046875|0.4507|-0.4011075|-0.00122495|  0.37004|-0.70969-0.91423|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |  KA|   1.5882046875|0.8672| 1.0169925|    1.49455| 2.065375|   0.20826-3.925|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |   V|   80.900546875| 28.77|    57.683|     78.503|  93.7155|   34.914-161.06|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+ #> |  CL|   19.810528125| 9.374|    11.884|    17.7265| 25.69625|   8.7284-44.279|64| #> +----+---------------+------+----------+-----------+---------+----------------+--+"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"creates stack plots Bayesian parameter estimates plotted covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"","code":"parm.vs.cov(   object,   onlyfirst = TRUE,   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"object xpose.data object. onlyfirst Logical value indicating whether first row per individual included plot. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. type plot type - defaults points . main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"Returns stack xyplots histograms parameters covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"parameters Xpose data object, specified object@Prefs@Xvardef$parms, plotted covariate present, specified object@Prefs@Xvardef$covariates, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parameters plotted against covariates, for Xpose 4 — parm.vs.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb <- xpose.data(5)  ## A vanilla plot parm.vs.cov(xpdb)  ## Custom colours and symbols, IDs parm.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot parameters vs other parameters — parm.vs.parm","title":"Plot parameters vs other parameters — parm.vs.parm","text":"function plots parameter values stored Xpose data object versus series graphs.  mirror functionality available function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot parameters vs other parameters — parm.vs.parm","text":"","code":"parm.vs.parm(   object,   onlyfirst = TRUE,   abline = FALSE,   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot parameters vs other parameters — parm.vs.parm","text":"object xpose.data object. onlyfirst Logical value indicating whether first row per individual included plot. abline Allows line identity. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. type plot type - defaults points . main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot parameters vs other parameters — parm.vs.parm","text":"Returns stack xyplots histograms parameters parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot parameters vs other parameters — parm.vs.parm","text":"parameters Xpose data object, specified object@Prefs@Xvardef$parms, plotted rest, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot parameters vs other parameters — parm.vs.parm","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/parm.vs.parm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot parameters vs other parameters — parm.vs.parm","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb <- xpose.data(5)   parm.vs.parm(xpdb)   parm.vs.parm(xpdb,mirror=3) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"plot population predictions (PRED) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"","code":"pred.vs.idv(object, smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"object xpose.data object. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"Returns xyplot PRED vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/pred.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population predictions (PRED) plotted against the independent variable (IDV)\nfor Xpose 4 — pred.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  pred.vs.idv(xpdb)   ## A conditioning plot pred.vs.idv(xpdb, by=\"HCTZ\")   ## Logarithmic Y-axis pred.vs.idv(xpdb, logy=TRUE)   ## Custom colours and symbols, IDs pred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"Print Xpose multiple plot object, output function xpose.multiple.plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"","code":"# S3 method for xpose.multiple.plot print(x, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"x Output object function xpose.multiple.plot. ... Additional options passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"Print method plot class.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/print.xpose.multiple.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print an Xpose multiple plot object. — print.xpose.multiple.plot","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"Reads results randtest tool PsN creates histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"","code":"randtest.hist(   results.file = \"raw_results_run1.csv\",   df = 1,   p.val = 0.05,   main = \"Default\",   xlim = NULL,   PCTSlcol = \"black\",   vlcol = c(\"red\", \"orange\"),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"results.file location results file randtest tool PsN df degrees freedom full reduced model used randomization test. p.val p-value like use. main title plot. xlim limits x-axis PCTSlcol Color empirical line vlcol Colors original nominal line ... Additional arguments can passed xpose.plot.histogram, xpose.panel.histogram, histogram lattice-package functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"lattice object","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"PsN","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/randtest.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to create a histogram of results from the randomization test tool\n(randtest) in PsN — randtest.hist","text":"","code":"if (FALSE) { randtest.hist(results.file=\"randtest_dir1/raw_results_run1.csv\",df=2) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"creates stack plots Bayesian random parameter estimates plotted covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"","code":"ranpar.vs.cov(   object,   onlyfirst = TRUE,   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"object xpose.data object. onlyfirst Logical value indicating whether first row per individual included plot. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. type plot type - defaults points . main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"Returns stack xyplots histograms random parameters covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"random parameters (ETAs) Xpose data object, specified object@Prefs@Xvardef$ranpar, plotted covariate present, specified object@Prefs@Xvardef$covariates, creating stack plots. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/ranpar.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random parameters plotted against covariates, for Xpose 4 — ranpar.vs.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb <- xpose.data(5)  ## A vanilla plot ranpar.vs.cov(xpdb)  ## Custom colours and symbols, IDs ranpar.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.TTE.sim.data.html","id":null,"dir":"Reference","previous_headings":"","what":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","title":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","text":"Read (repeated) time--event simulation data files.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.TTE.sim.data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","text":"","code":"read.TTE.sim.data(   sim.file,   subset = NULL,   headers = c(\"REP\", \"ID\", \"DV\", \"TIME\", \"FLAG2\", \"DOSE\"),   xpose.table.file = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.TTE.sim.data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","text":"sim.file Name simulated file. subset subset extract. headers headers file. xpose.table.file xpose table files. ... Extra arguments passed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.TTE.sim.data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read (repeated) time-to-event simulation data files. — read.TTE.sim.data","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":null,"dir":"Reference","previous_headings":"","what":"Reading NONMEM table files — read.nm.tables","title":"Reading NONMEM table files — read.nm.tables","text":"Reads one NONMEM table files, removes duplicated columns merges data data.frame.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reading NONMEM table files — read.nm.tables","text":"","code":"read.nm.tables(   table.files = NULL,   runno = NULL,   tab.suffix = \"\",   table.names = c(\"sdtab\", \"mutab\", \"patab\", \"catab\", \"cotab\", \"mytab\", \"extra\", \"xptab\"),   cwres.name = c(\"cwtab\"),   cwres.suffix = \"\",   quiet = FALSE,   new_methods = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reading NONMEM table files — read.nm.tables","text":"table.files Exact names table files read.  provided exact names created using arguments function. runno Run-number identify sets table files. tab.suffix Table file name suffix. table.names Vector template table file names read. cwres.name Vector CWRES table file names read. cwres.suffix CWRES table file name suffix. quiet Logical value indicate whether warnings quiet . new_methods faster methods reading tables used (uses readr package)? ... Additional arguments passed function","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reading NONMEM table files — read.nm.tables","text":"dataframe.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reading NONMEM table files — read.nm.tables","text":"Reads one table files, removes duplicate columns merges data. function also checks see table files length (required). header lines table files (example data simulated NSUB>1), removed. table file names read constructed file name templates table.names. runno tab.suffix appended file name template checking file readable. Xpose expects, default, find following NONMEM tables working directory able create Xpose data object (using run number 5 example): sdtab5: 'standard' parameters, including IWRE, IPRE, TIME, NONMEM default items (DV, PRED, RES WRES) added NOAPPEND present $TABLE record. $TABLE ID TIME IPRE IWRE NOPRINT ONEHEADER FILE=sdtab5 patab5: empirical Bayes estimates individual model parameter values, posthoc estimates. model parameters, CL, V2, ETA1, etc. $TABLE ID CL V2 KA K F1 ETA1 ETA2 ETA3 NOPRINT NOAPPEND ONEHEADER FILE=patab5 catab5: Categorical covariates, e.g. SEX, RACE. $TABLE ID SEX HIV GRP NOPRINT NOAPPEND ONEHEADER FILE=catab5 cotab5: Continuous covariates, e.g. WT, AGE. $TABLE ID WT AGE BSA HT GGT HB NOPRINT NOAPPEND ONEHEADER FILE=cotab5 mutab5, mytab5, extra5, xptab5: Additional variables kind. might useful covariates can accommodated covariates tables, example, variables added, e.g. CMAX, AUC.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Reading NONMEM table files — read.nm.tables","text":"Niclas Jonsson, Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.nm.tables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reading NONMEM table files — read.nm.tables","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory, and that the table files have ## a suffix of '.dat', e.g. sdtab5.dat  my.dataframe <- read.nm.tables(5, tab.suffix = \".dat\")  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":null,"dir":"Reference","previous_headings":"","what":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"function reads results file running either PsN command vpc npc.  function parses file passes result plotting functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"","code":"read.npc.vpc.results(   vpc.results = NULL,   npc.results = NULL,   verbose = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"vpc.results name results file running PsN command vcp.  Often named vpc_results.csv.  file directory different working directory can define relative absolute path file , example, ./vpc_strat_WT_4_mirror_5/vpc_results.csv. npc.results name results file running PsN command npc.  Often named npc_results.csv.  relative absolute paths file allowed vpc.results. verbose Text messages passed screen . ... arguments passed functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"list values returned. model.file model file PsN ran either npc vpc dv.var dependent variable used calculations. idv.var independent variable used calculations. NULL npc.results used. num.tables number separate tables results file. .interval conditioning interval stratification variable, returned vpc.results used. result.tables results tables results file.  list.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"One vpc.results npc.results necessary.  none defined function nothing NULL returned function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.npc.vpc.results.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read the results file from a Numerical or Visual Predictive Check run in PsN — read.npc.vpc.results","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":null,"dir":"Reference","previous_headings":"","what":"Read the vpctab file from PsN into Xpose — read.vpctab","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"function read vpctab file created PsN gathers information needed make vpc plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"","code":"read.vpctab(   vpctab = NULL,   object = NULL,   vpc.name = \"vpctab\",   vpc.suffix = \"\",   tab.suffix = \"\",   inclZeroWRES = FALSE,   verbose = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"vpctab vpctab file 'vpc' run PsN. object xpose data object. Created xpose.data. One object vpctab required.  present information vpctab -ride xpose data object object (.e. values vpctab replace matching values object@Data portion xpose data object). object present function look vpctab run number one associated object. vpc.name default name vpctab file. Used object supplied. vpc.suffix suffix vpctab file. Used object supplied. tab.suffix table suffix vpctab file. Used object supplied. Final order file paste(vpc.name,object@Runno,vpc.suffix,tab.suffix) inclZeroWRES zero valued weighted residuals object TRUE. verbose Text messages passed screen . ... arguments passed functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"Returned xpose data object vpctab information included.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read.vpctab.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read the vpctab file from PsN into Xpose — read.vpctab","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":null,"dir":"Reference","previous_headings":"","what":"Read NONMEM output files into Xpose 4 — read_NM_output","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"functions read NONMEM output file ('*.lst' file) format input.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"","code":"calc.npar(object)  create.parameter.list(listfile)  read.lst(filename)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"object return value read.lst(filename) listfile NONMEM output file. filename NONMEM output file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"lists read values.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"calc.npar(): calculates number type parameters included NONMEM output file create.parameter.list(): Reads parameters, uncertainty termination messages included NONMEM output file read.lst(): parses information NONMEM output.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_NM_output.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read NONMEM output files into Xpose 4 — read_NM_output","text":"Niclas Jonsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Read NONMEM table files produced from simulation. — read_nm_table","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"function reads NONMEM table files produced $SIM line  NONMEM model file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"","code":"read_nm_table(   nm_table,   only_obs = FALSE,   method = \"default\",   quiet = TRUE,   sim_num = FALSE,   sim_name = \"NSIM\" )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"nm_table NONMEM table file read. text string. only_obs non-observation lines data set removed?  Currently filtered using expected MDV column. TRUE  FALSE. method methods use reading tables,  Can \"readr_1\", \"readr_2\", readr_3\" \"slow\". quiet error message verbose ? sim_num simulation number added simulation tables? sim_name name one use name column simulation number?","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"Returns data frame simulated table added column    simulation number. data frame given class c(\"tbl_df\",    \"tbl\", \"data.frame\") easy use dplyr.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/read_nm_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read NONMEM table files produced from simulation. — read_nm_table","text":"Currently function expects $TABLE header  new simulation. means NOHEADER option  ONEHEADER option table file allowed.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":null,"dir":"Reference","previous_headings":"","what":"Resets Xpose variable definitions to factory settings — reset.graph.par","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"Function reset Xpose's graphics parameters definitions default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"","code":"reset.graph.par(object, classic = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"object xpose.data object. classic logical operator specifying whether function assume classic menu system. internal option need never called command line.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"xpose.data object (classic == FALSE) null (classic == TRUE).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"functions used reset Xpose's graphic settings definitions default values. Graphical settings read file 'xpose.ini' root 'xpose4' package.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"Niclas Jonsson & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/reset.graph.par.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resets Xpose variable definitions to factory settings — reset.graph.par","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Import graphics preferences you saved earlier using export.graph.par xpdb5 <- import.graph.par(xpdb5)  ## Reset to default values xpdb5 <- reset.graph.par(xpdb5)  ## Change WRES definition xpdb5 <- change.wres(xpdb5) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Print run summary in Xpose 4 — runsum","title":"Print run summary in Xpose 4 — runsum","text":"Function build Xpose run summaries.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print run summary in Xpose 4 — runsum","text":"","code":"runsum(   object,   dir = \"\",   modfile = paste(dir, \"run\", object@Runno, \".mod\", sep = \"\"),   listfile = paste(dir, \"run\", object@Runno, \".lst\", sep = \"\"),   main = NULL,   subset = xsubset(object),   show.plots = TRUE,   txt.cex = 0.7,   txt.font = 1,   show.ids = FALSE,   param.table = TRUE,   txt.columns = 2,   force.wres = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print run summary in Xpose 4 — runsum","text":"object xpose.data object. dir directory look model output file NONMEM run. modfile name NONMEM control stream associated current run. listfile name NONMEM output file associated current run. main string giving main heading. NULL none. subset string giving subset expression applied data plotting. See xsubset. show.plots Logical indicating GOF plots shown run summary. txt.cex Number indicating size txt run summary. txt.font Font text run summary. show.ids Logical indicating IDs plotted plots run summary. param.table Logical indicating parameter table shown run summary. txt.columns number text columns run summary. force.wres Plot WRES even residuals available. ... arguments passed various functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print run summary in Xpose 4 — runsum","text":"compound plot containing Xpose run summary created.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print run summary in Xpose 4 — runsum","text":"Niclas Jonsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/runsum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print run summary in Xpose 4 — runsum","text":"","code":"od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-f00e6fae00d8efe8984ec802f708f91a\" #> [2] \"downlit\"                                #> [3] \"file55221cc6cf3b\"                        simprazExample(overwrite=TRUE) # write files (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"run1.ext\"    \"run1.lst\"    \"run1.mod\"    \"simpraz.dta\" \"xptab1\"       xpdb <- xpose.data(1) #>  #> Looking for NONMEM table files. #>     Reading ./xptab1  #> Table files read. #>  #> Looking for NONMEM simulation table files. #> No simulated table files read. #>  runsum(xpdb)    file.remove(new.files) # remove these files #> [1] TRUE TRUE TRUE TRUE TRUE setwd(od)  # restore working directory"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated prazosin Xpose database. — simpraz.xpdb","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"Xpose database NONMEM output model prazosin using simulated data (NONMEM 7.3).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"","code":"simpraz.xpdb"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"xpose.data object","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"database can used test functions Xpose 4.  database slightly different database created reading files created simprazExample using xpose.data.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simpraz.xpdb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulated prazosin Xpose database. — simpraz.xpdb","text":"","code":"xpose.print(simpraz.xpdb) #> The database contains the following observed items: #> ID TIME IPRED IWRES CWRES CL V KA ETA1 ETA2 ETA3 AGE HT WT  #> SECR SEX RACE SMOK HCTZ PROP CON OCC DV PRED RES WRES #>  #> The following variables are defined: #>  #> ID variable: ID  #> Label variable: ID  #> Independent variable: TIME  #> Occasion variable: OCC  #> Dependent variable: DV  #> Population prediction variable: PRED  #> Individual prediction variable: IPRED  #> Weighted population residual variable: WRES  #> Weighted individual residual variable: IWRES  #> Population residual variable: RES  #> Parameters: ETA3 ETA2 ETA1 KA V CL #> Covariates: SEX RACE SMOK HCTZ PROP CON OCC AGE HT WT SECR #>   ( Continuous: AGE HT WT SECR ) #>   ( Categorical: SEX RACE SMOK HCTZ PROP CON OCC ) #> Variability parameters: ETA1 ETA2 ETA3 #> Missing value label: -99  Data(simpraz.xpdb) #>     ID TIME      IPRED       IWRES      CWRES      CL       V      KA #> 2    1    1 6.9193e+01  0.03681300 -0.0646440 13.5790  93.640 1.22490 #> 3    1    2 8.0181e+01 -0.09442400 -0.9411300 13.5790  93.640 1.22490 #> 4    1    3 7.5330e+01  0.16833000  1.1911000 13.5790  93.640 1.22490 #> 5    1    4 6.6916e+01 -0.20602000 -1.5154000 13.5790  93.640 1.22490 #> 6    1    5 5.8398e+01 -0.02685300 -0.0596340 13.5790  93.640 1.22490 #> 7    1    6 5.0666e+01  0.02513800  0.4099300 13.5790  93.640 1.22490 #> 8    1    7 4.3871e+01  0.20557000  1.8304000 13.5790  93.640 1.22490 #> 9    1    9 3.2841e+01 -0.17939000 -1.0219000 13.5790  93.640 1.22490 #> 10   1   11 2.4575e+01  0.06492000  0.8373000 13.5790  93.640 1.22490 #> 12   2    1 9.6073e+01  0.13195000  0.8119500  8.7284  92.501 3.01420 #> 13   2    2 9.2138e+01  0.04842400 -0.0866630  8.7284  92.501 3.01420 #> 14   2    3 8.4073e+01 -0.03655400 -0.5405500  8.7284  92.501 3.01420 #> 15   2    4 7.6514e+01  0.00726360  0.0058889  8.7284  92.501 3.01420 #> 16   2    5 6.9625e+01 -0.07260500 -0.4024500  8.7284  92.501 3.01420 #> 17   2    6 6.3356e+01 -0.20749000 -1.2486000  8.7284  92.501 3.01420 #> 18   2    7 5.7651e+01  0.12019000  1.3843000  8.7284  92.501 3.01420 #> 19   2    8 5.2460e+01 -0.03659500  0.3323700  8.7284  92.501 3.01420 #> 20   2   10 4.3438e+01 -0.04322700  0.4971900  8.7284  92.501 3.01420 #> 21   2   12 3.5967e+01  0.18052000  2.3422000  8.7284  92.501 3.01420 #> 23   3    1 8.7842e+00  0.06440900  0.3927500 11.0240  93.942 2.23050 #> 24   3    2 8.7557e+00 -0.01093200 -0.4033600 11.0240  93.942 2.23050 #> 25   3    3 7.8877e+00  0.03705600  0.0613170 11.0240  93.942 2.23050 #> 26   3    4 7.0253e+00 -0.11889000 -0.9633300 11.0240  93.942 2.23050 #> 27   3    5 6.2486e+00  0.13306000  1.0866000 11.0240  93.942 2.23050 #> 28   3    6 5.5568e+00 -0.15779000 -0.9918700 11.0240  93.942 2.23050 #> 29   3    7 4.9416e+00  0.07253700  0.8514900 11.0240  93.942 2.23050 #> 30   3    8 4.3944e+00 -0.04423900  0.0490370 11.0240  93.942 2.23050 #> 31   3   10 3.4751e+00  0.12801000  1.4633000 11.0240  93.942 2.23050 #> 32   3   12 2.7482e+00  0.00066343  0.5552500 11.0240  93.942 2.23050 #> 34   4    1 6.3799e+01 -0.01017500  0.4475200 19.6070  49.991 1.67760 #> 35   4    2 5.5019e+01  0.13433000  1.2202000 19.6070  49.991 1.67760 #> 36   4    3 3.9396e+01  0.04198600  0.2016600 19.6070  49.991 1.67760 #> 37   4    4 2.7031e+01 -0.10065000 -1.0776000 19.6070  49.991 1.67760 #> 38   4    5 1.8339e+01 -0.00920360 -0.4784300 19.6070  49.991 1.67760 #> 39   4    6 1.2404e+01  0.06662200  0.0711510 19.6070  49.991 1.67760 #> 40   4    7 8.3822e+00  0.01166300 -0.3334000 19.6070  49.991 1.67760 #> 41   4    9 3.8260e+00  0.02455700 -0.1799200 19.6070  49.991 1.67760 #> 42   4   11 1.7462e+00  0.00790240 -0.2506900 19.6070  49.991 1.67760 #> 44   5    1 1.9492e+01  0.10248000  0.9678200 12.4700  84.706 2.43710 #> 45   5    2 1.8528e+01 -0.19743000 -1.5134000 12.4700  84.706 2.43710 #> 46   5    3 1.6141e+01 -0.06013000 -0.4224300 12.4700  84.706 2.43710 #> 47   5    4 1.3944e+01 -0.01893700 -0.0316990 12.4700  84.706 2.43710 #> 48   5    5 1.2036e+01  0.23210000  1.9295000 12.4700  84.706 2.43710 #> 49   5    6 1.0389e+01 -0.05090000 -0.1700800 12.4700  84.706 2.43710 #> 50   5    7 8.9667e+00 -0.02416300  0.0563140 12.4700  84.706 2.43710 #> 51   5    9 6.6798e+00  0.16321000  1.4828000 12.4700  84.706 2.43710 #> 52   5   11 4.9761e+00 -0.12382000 -0.7134000 12.4700  84.706 2.43710 #> 54   6    1 7.1066e+01  0.04875000  0.2570900 14.3950  73.084 0.86832 #> 55   6    2 8.8184e+01 -0.28535000 -2.1306000 14.3950  73.084 0.86832 #> 56   6    3 8.4934e+01  0.18280000  1.5438000 14.3950  73.084 0.86832 #> 57   6    4 7.5002e+01  0.13224000  1.2209000 14.3950  73.084 0.86832 #> 58   6    5 6.3797e+01 -0.07503500 -0.3469000 14.3950  73.084 0.86832 #> 59   6    6 5.3316e+01  0.01038200  0.2649900 14.3950  73.084 0.86832 #> 60   6    7 4.4173e+01  0.02212200  0.3031300 14.3950  73.084 0.86832 #> 61   6    8 3.6439e+01 -0.07021700 -0.4513400 14.3950  73.084 0.86832 #> 62   6   10 2.4659e+01  0.06532200  0.4657100 14.3950  73.084 0.86832 #> 64   7    1 1.6349e+02  0.13020000  1.8578000 14.0930  46.925 2.98340 #> 65   7    2 1.2936e+02  0.10339000  0.9682000 14.0930  46.925 2.98340 #> 66   7    3 9.6217e+01 -0.10431000 -0.7247200 14.0930  46.925 2.98340 #> 67   7    4 7.1277e+01 -0.07894400 -0.5392700 14.0930  46.925 2.98340 #> 68   7    5 5.2787e+01  0.04324800  0.4085000 14.0930  46.925 2.98340 #> 69   7    6 3.9093e+01 -0.02923600 -0.1127600 14.0930  46.925 2.98340 #> 70   7    8 2.1441e+01  0.08438900  0.7694000 14.0930  46.925 2.98340 #> 72   8    1 3.4260e+01  0.10099000 -0.1437400 42.1500 105.580 3.54630 #> 73   8    2 2.3971e+01 -0.20112000 -2.5728000 42.1500 105.580 3.54630 #> 74   8    3 1.6110e+01 -0.10550000 -1.5138000 42.1500 105.580 3.54630 #> 75   8    4 1.0808e+01  0.10012000  0.3458000 42.1500 105.580 3.54630 #> 76   8    5 7.2505e+00 -0.24419000 -2.0503000 42.1500 105.580 3.54630 #> 77   8    6 4.8640e+00  0.01356100  0.0123830 42.1500 105.580 3.54630 #> 78   8    7 3.2630e+00  0.22891000  1.6775000 42.1500 105.580 3.54630 #> 79   8    9 1.4685e+00  0.04868300  0.2684800 42.1500 105.580 3.54630 #> 80   8   11 6.6089e-01 -0.09213400 -0.8524200 42.1500 105.580 3.54630 #> 82   9    1 5.5324e+01 -0.04960600 -0.0015166 21.3300  57.540 1.60740 #> 83   9    2 4.9276e+01  0.17117000  1.2600000 21.3300  57.540 1.60740 #> 84   9    3 3.6235e+01 -0.00013445 -0.3256500 21.3300  57.540 1.60740 #> 85   9    4 2.5457e+01  0.02644900 -0.2390900 21.3300  57.540 1.60740 #> 86   9    5 1.7661e+01  0.02316400 -0.2412900 21.3300  57.540 1.60740 #> 87   9    6 1.2208e+01 -0.11291000 -1.1700000 21.3300  57.540 1.60740 #> 88   9    8 5.8200e+00 -0.05669500 -0.5009300 21.3300  57.540 1.60740 #> 89   9   10 2.7731e+00  0.09986200  0.8232500 21.3300  57.540 1.60740 #> 91  10    1 5.6258e+01  0.09779200 -0.4076800 44.2790  34.914 3.55980 #> 92  10    2 1.7428e+01  0.05579700 -2.5276000 44.2790  34.914 3.55980 #> 93  10    3 4.9485e+00 -0.02596300 -1.8592000 44.2790  34.914 3.55980 #> 94  10    6 1.1030e-01 -0.27472000 -0.4042000 44.2790  34.914 3.55980 #> 95  10    8 8.7302e-03  0.14545000  2.4362000 44.2790  34.914 3.55980 #> 97  12    1 1.6198e+01  0.02666600  0.0415360 18.6950  92.607 1.92540 #> 98  12    2 1.5599e+01  0.05648300  0.1090400 18.6950  92.607 1.92540 #> 99  12    3 1.3092e+01 -0.15138000 -1.4208000 18.6950  92.607 1.92540 #> 100 12    4 1.0749e+01  0.11454000  0.6792500 18.6950  92.607 1.92540 #> 101 12    5 8.7913e+00 -0.12072000 -1.0203000 18.6950  92.607 1.92540 #> 102 12    6 7.1853e+00  0.12451000  0.8966700 18.6950  92.607 1.92540 #> 103 12    7 5.8720e+00 -0.02248100 -0.1728200 18.6950  92.607 1.92540 #> 104 12    9 3.9214e+00 -0.01312000 -0.0634180 18.6950  92.607 1.92540 #> 105 12   11 2.6188e+00  0.04245900  0.3511400 18.6950  92.607 1.92540 #> 107 13    1 1.3355e+01  0.04678600 -0.3388700 34.6730  78.320 1.12170 #> 108 13    2 1.2928e+01 -0.06558300 -1.0392000 34.6730  78.320 1.12170 #> 109 13    3 9.7203e+00 -0.05969700 -0.9090900 34.6730  78.320 1.12170 #> 110 13    4 6.7047e+00  0.07535900  0.1448000 34.6730  78.320 1.12170 #> 111 13    5 4.4567e+00  0.06805200  0.0926130 34.6730  78.320 1.12170 #> 112 13    6 2.9115e+00 -0.14133000 -1.5011000 34.6730  78.320 1.12170 #> 113 13    7 1.8860e+00 -0.05088300 -0.8349800 34.6730  78.320 1.12170 #> 114 13    8 1.2165e+00  0.08504900  0.1676700 34.6730  78.320 1.12170 #> 115 13   10 5.0351e-01  0.23136000  1.2465000 34.6730  78.320 1.12170 #> 116 13   12 2.0789e-01 -0.23037000 -2.1245000 34.6730  78.320 1.12170 #> 118 14    1 2.5038e+01  0.08035500  1.1038000 20.2700  57.701 2.48000 #> 119 14    2 1.9718e+01 -0.01916900 -0.1943000 20.2700  57.701 2.48000 #> 120 14    3 1.4053e+01 -0.01868900 -0.3506400 20.2700  57.701 2.48000 #> 121 14    4 9.9046e+00  0.01366900 -0.1361900 20.2700  57.701 2.48000 #> 122 14    5 6.9719e+00  0.07862100  0.3699900 20.2700  57.701 2.48000 #> 123 14    6 4.9067e+00 -0.22963000 -1.9322000 20.2700  57.701 2.48000 #> 124 14    7 3.4533e+00  0.00485050 -0.1257800 20.2700  57.701 2.48000 #> 125 14    8 2.4303e+00  0.27555000  1.9526000 20.2700  57.701 2.48000 #> 126 14   10 1.2037e+00 -0.16095000 -1.2921000 20.2700  57.701 2.48000 #> 127 14   12 5.9621e-01  0.00635350  0.0374170 20.2700  57.701 2.48000 #> 129 15    1 9.2441e+00 -0.03938900 -0.9602000 17.3920 118.440 0.90406 #> 130 15    2 1.1725e+01 -0.05584300 -1.1138000 17.3920 118.440 0.90406 #> 131 15    3 1.1639e+01  0.22775000  1.1714000 17.3920 118.440 0.90406 #> 132 15    4 1.0663e+01 -0.16442000 -1.6265000 17.3920 118.440 0.90406 #> 133 15    5 9.4555e+00  0.04912800  0.1525200 17.3920 118.440 0.90406 #> 134 15    6 8.2647e+00  0.06476500  0.4188800 17.3920 118.440 0.90406 #> 135 15    7 7.1767e+00 -0.05388800 -0.3485000 17.3920 118.440 0.90406 #> 136 15    8 6.2131e+00 -0.07292500 -0.3804600 17.3920 118.440 0.90406 #> 137 15   10 4.6404e+00 -0.13800000 -0.7021300 17.3920 118.440 0.90406 #> 138 15   12 3.4608e+00  0.16158000  1.6700000 17.3920 118.440 0.90406 #> 140 16    1 1.0618e+01 -0.07795800 -1.3419000 12.6540  70.520 0.53141 #> 141 16    2 1.5114e+01  0.05859200 -0.1268400 12.6540  70.520 0.53141 #> 142 16    3 1.6300e+01 -0.03250400 -0.5043700 12.6540  70.520 0.53141 #> 143 16    4 1.5778e+01  0.08439700  0.6561500 12.6540  70.520 0.53141 #> 144 16    5 1.4454e+01  0.14572000  1.3152000 12.6540  70.520 0.53141 #> 145 16    6 1.2824e+01 -0.10093000 -0.4396400 12.6540  70.520 0.53141 #> 146 16    7 1.1156e+01  0.10349000  1.1475000 12.6540  70.520 0.53141 #> 147 16    8 9.5804e+00 -0.06997600 -0.1771000 12.6540  70.520 0.53141 #> 148 16   10 6.9067e+00  0.02508600  0.4263100 12.6540  70.520 0.53141 #> 149 16   12 4.8984e+00  0.01257600  0.1745800 12.6540  70.520 0.53141 #> 151 17    1 1.5908e+02  0.10746000  1.7128000 10.2860  50.261 2.52330 #> 152 17    2 1.4240e+02 -0.02634200  0.3253200 10.2860  50.261 2.52330 #> 153 17    3 1.1707e+02 -0.03690100  0.1315800 10.2860  50.261 2.52330 #> 154 17    4 9.5486e+01 -0.02194900  0.1821900 10.2860  50.261 2.52330 #> 155 17    5 7.7821e+01  0.11513000  1.1642000 10.2860  50.261 2.52330 #> 156 17    6 6.3419e+01  0.05347000  0.6449400 10.2860  50.261 2.52330 #> 157 17    7 5.1682e+01 -0.09736600 -0.5463300 10.2860  50.261 2.52330 #> 158 17    8 4.2117e+01  0.07865600  0.7392000 10.2860  50.261 2.52330 #> 159 17   10 2.7970e+01  0.13906000  1.1194000 10.2860  50.261 2.52330 #> 160 17   12 1.8575e+01 -0.11604000 -0.8623000 10.2860  50.261 2.52330 #> 162 18    1 5.2404e+01  0.07530700  0.7263000 12.9160  76.863 2.32250 #> 163 18    2 4.9435e+01 -0.04561100 -0.4767900 12.9160  76.863 2.32250 #> 164 18    3 4.2292e+01  0.06191100  0.4013300 12.9160  76.863 2.32250 #> 165 18    4 3.5799e+01  0.17153000  1.3563000 12.9160  76.863 2.32250 #> 166 18    5 3.0267e+01 -0.04052900 -0.1307100 12.9160  76.863 2.32250 #> 167 18    6 2.5585e+01 -0.32149000 -2.1582000 12.9160  76.863 2.32250 #> 168 18    7 2.1628e+01  0.02414000  0.5351100 12.9160  76.863 2.32250 #> 169 18    8 1.8282e+01 -0.01271600  0.3156300 12.9160  76.863 2.32250 #> 170 18   10 1.3064e+01  0.04409100  0.8130900 12.9160  76.863 2.32250 #> 171 18   12 9.3351e+00  0.08837000  1.1591000 12.9160  76.863 2.32250 #> 173 19    1 5.2559e+01  0.07251400  0.7654100 10.6970  67.987 1.54970 #> 174 19    2 5.6065e+01 -0.16134000 -0.9023800 10.6970  67.987 1.54970 #> 175 19    3 5.0272e+01  0.16128000  1.5843000 10.6970  67.987 1.54970 #> 176 19    4 4.3456e+01 -0.15385000 -0.7835300 10.6970  67.987 1.54970 #> 177 19    5 3.7236e+01  0.04603700  0.7231100 10.6970  67.987 1.54970 #> 178 19    6 3.1837e+01 -0.08943100 -0.3239100 10.6970  67.987 1.54970 #> 179 19    7 2.7207e+01  0.20668000  1.8826000 10.6970  67.987 1.54970 #> 180 19    8 2.3247e+01  0.06595600  0.7757300 10.6970  67.987 1.54970 #> 181 19   10 1.6971e+01 -0.02655500 -0.0218010 10.6970  67.987 1.54970 #> 182 19   12 1.2389e+01 -0.02815600 -0.1358400 10.6970  67.987 1.54970 #> 184 20    1 9.9678e+00  0.09152000  0.2013600 19.2850  86.198 0.66435 #> 185 20    2 1.3099e+01 -0.09229900 -1.0564000 19.2850  86.198 0.66435 #> 186 20    3 1.3113e+01 -0.28162000 -2.2840000 19.2850  86.198 0.66435 #> 187 20    4 1.1842e+01  0.15432000  1.1607000 19.2850  86.198 0.66435 #> 188 20    5 1.0168e+01  0.07892700  0.6561800 19.2850  86.198 0.66435 #> 189 20    6 8.4890e+00  0.08493300  0.6992200 19.2850  86.198 0.66435 #> 190 20    7 6.9724e+00 -0.09213100 -0.6898500 19.2850  86.198 0.66435 #> 191 20    9 4.5823e+00  0.08024000  0.4516500 19.2850  86.198 0.66435 #> 192 20   11 2.9624e+00 -0.03794900 -0.6078200 19.2850  86.198 0.66435 #> 194 21    1 2.5587e+00 -0.12846000 -1.8527000 29.9420  96.831 0.34333 #> 195 21    3 3.9986e+00 -0.11719000 -1.9728000 29.9420  96.831 0.34333 #> 196 21    4 3.8485e+00  0.07314200 -0.3671200 29.9420  96.831 0.34333 #> 197 21    5 3.4729e+00  0.10570000  0.0636740 29.9420  96.831 0.34333 #> 198 21    6 3.0089e+00 -0.03619000 -0.8487000 29.9420  96.831 0.34333 #> 199 21    7 2.5347e+00  0.02970500 -0.2268900 29.9420  96.831 0.34333 #> 200 21    8 2.0919e+00  0.19510000  1.1100000 29.9420  96.831 0.34333 #> 201 21   10 1.3640e+00  0.04841300  0.0903160 29.9420  96.831 0.34333 #> 202 21   12 8.5410e-01 -0.22726000 -1.9483000 29.9420  96.831 0.34333 #> 204 22    1 2.1425e+01 -0.07772000 -1.1746000 34.2390  91.155 1.18500 #> 205 22    2 2.1267e+01  0.12806000  0.2941700 34.2390  91.155 1.18500 #> 206 22    3 1.6610e+01 -0.04515000 -1.0190000 34.2390  91.155 1.18500 #> 207 22    4 1.2021e+01 -0.00092257 -0.6403800 34.2390  91.155 1.18500 #> 208 22    5 8.4441e+00 -0.08811800 -1.2237000 34.2390  91.155 1.18500 #> 209 22    6 5.8572e+00  0.07389400  0.0907280 34.2390  91.155 1.18500 #> 210 22    7 4.0406e+00  0.01717400 -0.2559100 34.2390  91.155 1.18500 #> 212 23    1 1.4522e+01  0.06253400 -0.2408300 10.7110 151.960 1.07010 #> 213 23    2 1.8514e+01 -0.05045100 -1.2076000 10.7110 151.960 1.07010 #> 214 23    3 1.8962e+01 -0.05865200 -1.1808000 10.7110 151.960 1.07010 #> 215 23    4 1.8257e+01 -0.04367900 -0.9107800 10.7110 151.960 1.07010 #> 216 23    5 1.7216e+01 -0.15601000 -1.5863000 10.7110 151.960 1.07010 #> 217 23    6 1.6113e+01  0.08731700  0.4262700 10.7110 151.960 1.07010 #> 218 23    7 1.5040e+01  0.05917100  0.3738600 10.7110 151.960 1.07010 #> 219 23    8 1.4025e+01  0.12089000  0.9872800 10.7110 151.960 1.07010 #> 220 23   10 1.2184e+01  0.03577600  0.5918700 10.7110 151.960 1.07010 #> 221 23   12 1.0582e+01  0.01204800  0.6048500 10.7110 151.960 1.07010 #> 223 24    1 1.5286e+01  0.00418570 -0.6209700 11.0160  84.570 0.50433 #> 224 24    2 2.2651e+01 -0.26139000 -2.5830000 11.0160  84.570 0.50433 #> 225 24    3 2.5459e+01  0.16500000  0.8928200 11.0160  84.570 0.50433 #> 226 24    4 2.5717e+01  0.15061000  1.0648000 11.0160  84.570 0.50433 #> 227 24    5 2.4609e+01 -0.01094000  0.0918400 11.0160  84.570 0.50433 #> 228 24    6 2.2832e+01 -0.13234000 -0.6281900 11.0160  84.570 0.50433 #> 229 24    7 2.0785e+01  0.23215000  2.2760000 11.0160  84.570 0.50433 #> 230 24    8 1.8694e+01 -0.30995000 -1.7253000 11.0160  84.570 0.50433 #> 231 24   10 1.4808e+01 -0.02414600  0.5301000 11.0160  84.570 0.50433 #> 232 24   12 1.1558e+01  0.09450900  1.4236000 11.0160  84.570 0.50433 #> 234 25    1 2.7108e+01  0.04507300  0.0620610 25.5610  78.202 1.33080 #> 235 25    2 2.6713e+01 -0.10495000 -1.0418000 25.5610  78.202 1.33080 #> 236 25    3 2.1158e+01  0.06104900  0.1865600 25.5610  78.202 1.33080 #> 237 25    4 1.5759e+01 -0.01327900 -0.4268800 25.5610  78.202 1.33080 #> 238 25    5 1.1498e+01  0.07936300  0.2288400 25.5610  78.202 1.33080 #> 239 25    6 8.3268e+00  0.05323100  0.0056514 25.5610  78.202 1.33080 #> 240 25    7 6.0144e+00 -0.03065300 -0.6326900 25.5610  78.202 1.33080 #> 241 25    8 4.3399e+00 -0.06679800 -0.8923200 25.5610  78.202 1.33080 #> 242 25    9 3.1305e+00  0.07969600  0.2409600 25.5610  78.202 1.33080 #> 243 25   10 2.2578e+00  0.00095464 -0.3248500 25.5610  78.202 1.33080 #> 245 26    1 1.0933e+01 -0.19237000 -2.3438000 12.9680  85.946 0.34318 #> 246 26    2 1.7159e+01 -0.01860000 -1.1484000 12.9680  85.946 0.34318 #> 247 26    3 2.0260e+01  0.23743000  0.9561300 12.9680  85.946 0.34318 #> 248 26    4 2.1327e+01 -0.06645400 -1.0793000 12.9680  85.946 0.34318 #> 249 26    5 2.1111e+01 -0.02041200 -0.4553600 12.9680  85.946 0.34318 #> 250 26    6 2.0120e+01  0.09393600  0.6571500 12.9680  85.946 0.34318 #> 251 26    7 1.8697e+01  0.06649000  0.6510200 12.9680  85.946 0.34318 #> 252 26    8 1.7068e+01 -0.08307000 -0.3302900 12.9680  85.946 0.34318 #> 253 26   10 1.3724e+01  0.05365400  0.8603600 12.9680  85.946 0.34318 #> 254 26   12 1.0703e+01 -0.00499180  0.4230700 12.9680  85.946 0.34318 #> 256 27    1 4.3322e+01 -0.05960300 -0.6760900 19.9050  58.701 0.95710 #> 257 27    2 4.7499e+01  0.14360000  1.1242000 19.9050  58.701 0.95710 #> 258 27    3 4.0227e+01 -0.06929200 -0.4189000 19.9050  58.701 0.95710 #> 259 27    4 3.1112e+01 -0.04216300 -0.2965000 19.9050  58.701 0.95710 #> 260 27    5 2.3106e+01  0.14080000  0.9369300 19.9050  58.701 0.95710 #> 261 27    6 1.6823e+01  0.01289000 -0.1821700 19.9050  58.701 0.95710 #> 262 27    7 1.2124e+01  0.08380300  0.2425000 19.9050  58.701 0.95710 #> 263 27    8 8.6906e+00 -0.08867500 -1.1212000 19.9050  58.701 0.95710 #> 264 27   10 4.4333e+00 -0.05036200 -0.8184200 19.9050  58.701 0.95710 #> 265 27   12 2.2534e+00  0.05620500  0.0758110 19.9050  58.701 0.95710 #> 267 28    1 8.2497e+01  0.02051600 -0.0042412 17.2100  84.631 1.56870 #> 268 28    2 8.4503e+01  0.02008500 -0.0169360 17.2100  84.631 1.56870 #> 269 28    3 7.2533e+01  0.02063200  0.0211160 17.2100  84.631 1.56870 #> 270 28    4 5.9932e+01 -0.02890700 -0.3122100 17.2100  84.631 1.56870 #> 271 28    5 4.9060e+01 -0.04198300 -0.3787200 17.2100  84.631 1.56870 #> 272 28    6 4.0065e+01  0.01261400  0.0544760 17.2100  84.631 1.56870 #> 273 28    7 3.2699e+01 -0.03636500 -0.3066400 17.2100  84.631 1.56870 #> 274 28    8 2.6684e+01  0.13965000  1.0269000 17.2100  84.631 1.56870 #> 275 28   10 1.7767e+01  0.13523000  0.9863600 17.2100  84.631 1.56870 #> 276 28   12 1.1830e+01 -0.13865000 -1.0903000 17.2100  84.631 1.56870 #> 278 29    1 2.3035e+01 -0.04234200 -1.6466000 30.4780 127.080 0.40331 #> 279 29    3 3.6649e+01  0.10262000 -0.5420700 30.4780 127.080 0.40331 #> 280 29    4 3.5703e+01 -0.02782400 -1.2294000 30.4780 127.080 0.40331 #> 281 29    5 3.2680e+01 -0.20164000 -2.2283000 30.4780 127.080 0.40331 #> 282 29    6 2.8777e+01 -0.00268240 -0.4339300 30.4780 127.080 0.40331 #> 283 29    7 2.4689e+01  0.21794000  1.4808000 30.4780 127.080 0.40331 #> 284 29    8 2.0793e+01 -0.04007100 -0.2717400 30.4780 127.080 0.40331 #> 285 29   10 1.4201e+01 -0.29513000 -1.9231000 30.4780 127.080 0.40331 #> 286 29   12 9.3842e+00  0.13063000  1.4558000 30.4780 127.080 0.40331 #> 288 30    1 6.7041e+01 -0.13769000 -1.3960000 12.1630  85.934 0.98067 #> 289 30    2 8.3337e+01  0.24255000  1.6200000 12.1630  85.934 0.98067 #> 290 30    3 8.1768e+01 -0.07054600 -0.5672200 12.1630  85.934 0.98067 #> 291 30    4 7.4514e+01 -0.02044600 -0.0456080 12.1630  85.934 0.98067 #> 292 30    5 6.6006e+01 -0.14356000 -0.8761800 12.1630  85.934 0.98067 #> 293 30    6 5.7792e+01  0.03959500  0.5783300 12.1630  85.934 0.98067 #> 294 30    7 5.0351e+01  0.13285000  1.3292000 12.1630  85.934 0.98067 #> 295 30    8 4.3775e+01 -0.13445000 -0.6652600 12.1630  85.934 0.98067 #> 296 30   10 3.3016e+01  0.17520000  1.6969000 12.1630  85.934 0.98067 #> 297 30   12 2.4880e+01 -0.06713500 -0.1487900 12.1630  85.934 0.98067 #> 299 31    1 1.3951e+01 -0.04378200 -0.7529000 15.1090  83.503 1.04040 #> 300 31    2 1.6571e+01  0.08081500  0.3780700 15.1090  83.503 1.04040 #> 301 31    3 1.5570e+01  0.10664000  0.7438700 15.1090  83.503 1.04040 #> 302 31    4 1.3608e+01 -0.13727000 -0.9921300 15.1090  83.503 1.04040 #> 303 31    5 1.1573e+01 -0.10568000 -0.6911400 15.1090  83.503 1.04040 #> 304 31    6 9.7344e+00  0.10330000  0.9216500 15.1090  83.503 1.04040 #> 305 31    7 8.1504e+00  0.15332000  1.3131000 15.1090  83.503 1.04040 #> 306 31    8 6.8110e+00 -0.05740100 -0.2795700 15.1090  83.503 1.04040 #> 307 31   10 4.7470e+00 -0.10258000 -0.6375700 15.1090  83.503 1.04040 #> 308 31   12 3.3061e+00  0.07678800  0.6893800 15.1090  83.503 1.04040 #> 310 32    1 1.6890e+01  0.02543100 -0.6048700 22.2730 161.060 1.89500 #> 311 32    2 1.7248e+01  0.01692800 -0.8933800 22.2730 161.060 1.89500 #> 312 32    3 1.5402e+01 -0.17284000 -2.1920000 22.2730 161.060 1.89500 #> 313 32    4 1.3470e+01  0.09352900  0.0384960 22.2730 161.060 1.89500 #> 314 32    5 1.1739e+01  0.10912000  0.3648300 22.2730 161.060 1.89500 #> 315 32    6 1.0224e+01 -0.09528100 -0.9989300 22.2730 161.060 1.89500 #> 316 32    7 8.9039e+00 -0.11836000 -1.0185000 22.2730 161.060 1.89500 #> 317 32    8 7.7539e+00  0.08332400  0.6366200 22.2730 161.060 1.89500 #> 318 32   10 5.8803e+00 -0.12250000 -0.7301400 22.2730 161.060 1.89500 #> 319 32   12 4.4595e+00  0.13018000  1.2982000 22.2730 161.060 1.89500 #> 321 33    1 1.0497e+02  0.00922680  0.1798900  9.8142  71.098 1.68210 #> 322 33    2 1.1096e+02  0.08543500  0.6514100  9.8142  71.098 1.68210 #> 323 33    3 1.0028e+02  0.03824500  0.3469400  9.8142  71.098 1.68210 #> 324 33    4 8.8030e+01  0.03498900  0.4304400  9.8142  71.098 1.68210 #> 325 33    5 7.6805e+01  0.09966100  1.0321000  9.8142  71.098 1.68210 #> 326 33    6 6.6926e+01 -0.12456000 -0.5643600  9.8142  71.098 1.68210 #> 327 33    7 5.8301e+01 -0.07446500 -0.1016700  9.8142  71.098 1.68210 #> 328 33    8 5.0785e+01 -0.14837000 -0.5933100  9.8142  71.098 1.68210 #> 329 33   10 3.8534e+01  0.19454000  2.0928000  9.8142  71.098 1.68210 #> 330 33   12 2.9238e+01  0.01854600  0.8048600  9.8142  71.098 1.68210 #> 332 34    2 8.0560e+00  0.05263100  0.1739500 27.2370  67.039 1.45910 #> 333 34    3 5.8506e+00 -0.07873000 -0.7393500 27.2370  67.039 1.45910 #> 334 34    4 4.0098e+00 -0.22938000 -1.9093000 27.2370  67.039 1.45910 #> 335 34    5 2.6972e+00  0.28283000  1.8805000 27.2370  67.039 1.45910 #> 336 34    6 1.8027e+00 -0.00704600 -0.4093900 27.2370  67.039 1.45910 #> 337 34    7 1.2022e+00 -0.10166000 -1.2036000 27.2370  67.039 1.45910 #> 338 34    9 5.3374e-01  0.10541000  0.2932500 27.2370  67.039 1.45910 #> 339 34   10 3.5555e-01 -0.07185700 -1.0503000 27.2370  67.039 1.45910 #> 341 35    1 3.0587e+01 -0.08491100 -1.2655000 26.1020  65.982 0.69311 #> 342 35    2 3.5888e+01  0.19317000  1.0618000 26.1020  65.982 0.69311 #> 343 35    3 3.1810e+01 -0.19333000 -1.6674000 26.1020  65.982 0.69311 #> 344 35    4 2.5240e+01 -0.02260100 -0.3011200 26.1020  65.982 0.69311 #> 345 35    5 1.8906e+01 -0.00823790 -0.2389600 26.1020  65.982 0.69311 #> 346 35    6 1.3685e+01  0.03399500 -0.0551100 26.1020  65.982 0.69311 #> 347 35    7 9.6917e+00  0.18246000  0.8894200 26.1020  65.982 0.69311 #> 348 35   10 3.2064e+00 -0.10180000 -1.6228000 26.1020  65.982 0.69311 #> 350 36    1 1.3023e+02  0.03580100  0.7723900 23.1080  48.141 1.89000 #> 351 36    2 1.0026e+02  0.06935300  0.5669600 23.1080  48.141 1.89000 #> 352 36    3 6.5010e+01 -0.02706900 -0.5179300 23.1080  48.141 1.89000 #> 353 36    4 4.0676e+01 -0.04932000 -0.8703500 23.1080  48.141 1.89000 #> 354 36    5 2.5238e+01  0.04407300 -0.2350300 23.1080  48.141 1.89000 #> 355 36    6 1.5627e+01 -0.09643500 -1.3136000 23.1080  48.141 1.89000 #> 356 36    7 9.6713e+00  0.21390000  1.0438000 23.1080  48.141 1.89000 #> 357 36    8 5.9847e+00  0.01592100 -0.4273000 23.1080  48.141 1.89000 #> 358 36   10 2.2916e+00 -0.08359200 -1.0714000 23.1080  48.141 1.89000 #> 359 36   12 8.7743e-01  0.01432700 -0.1877100 23.1080  48.141 1.89000 #> 361 37    1 2.2057e+01  0.05728300 -0.2077900 15.9890  75.885 0.46891 #> 362 37    2 3.1667e+01 -0.18653000 -1.9498000 15.9890  75.885 0.46891 #> 363 37    3 3.4285e+01  0.07830500  0.3151700 15.9890  75.885 0.46891 #> 364 37    8 1.9366e+01  0.14685000  1.3637000 15.9890  75.885 0.46891 #> 365 37   12 9.1165e+00 -0.06872400 -0.6587500 15.9890  75.885 0.46891 #> 367 38    1 6.5378e+01  0.08476700  1.2398000 19.9830  54.656 2.46260 #> 368 38    2 5.0928e+01 -0.06241000 -0.5106100 19.9830  54.656 2.46260 #> 369 38    3 3.5807e+01  0.14138000  0.8040500 19.9830  54.656 2.46260 #> 370 38    4 2.4883e+01 -0.14639000 -1.4329000 19.9830  54.656 2.46260 #> 371 38    6 1.1979e+01  0.04765000  0.0828920 19.9830  54.656 2.46260 #> 373 39    1 6.3145e+00  0.00087708 -0.4685700 17.7880 110.330 1.46490 #> 374 39    2 6.8335e+00  0.06680700  0.0167570 17.7880 110.330 1.46490 #> 375 39    3 6.1532e+00 -0.09315500 -1.0872000 17.7880 110.330 1.46490 #> 376 39    4 5.3149e+00  0.07621300  0.3247200 17.7880 110.330 1.46490 #> 377 39    5 4.5416e+00 -0.16108000 -1.3518000 17.7880 110.330 1.46490 #> 378 39    7 3.2943e+00  0.05637000  0.4637400 17.7880 110.330 1.46490 #> 379 39    8 2.8040e+00  0.13052000  1.0776000 17.7880 110.330 1.46490 #> 380 39   10 2.0312e+00 -0.04982800 -0.2315300 17.7880 110.330 1.46490 #> 382 40    1 2.9012e+01  0.16710000  0.9159600 22.9910  98.438 3.50040 #> 383 40    2 2.3845e+01 -0.23003000 -2.3360000 22.9910  98.438 3.50040 #> 384 40    3 1.8905e+01  0.02989100 -0.1934200 22.9910  98.438 3.50040 #> 385 40    4 1.4968e+01  0.05892000  0.2017100 22.9910  98.438 3.50040 #> 386 40    5 1.1850e+01 -0.09876900 -0.8478100 22.9910  98.438 3.50040 #> 387 40    6 9.3822e+00 -0.16437000 -1.2339000 22.9910  98.438 3.50040 #> 388 40    7 7.4280e+00  0.05142800  0.4781700 22.9910  98.438 3.50040 #> 389 40    8 5.8808e+00 -0.00694570  0.0886480 22.9910  98.438 3.50040 #> 390 40   10 3.6862e+00  0.24790000  2.0591000 22.9910  98.438 3.50040 #> 391 40   12 2.3105e+00 -0.13873000 -0.8731000 22.9910  98.438 3.50040 #> 393 41    1 4.2425e+01  0.07012900  0.8266800  9.3156  55.911 1.38500 #> 394 41    2 4.6534e+01 -0.05295400  0.0891360  9.3156  55.911 1.38500 #> 395 41    3 4.2051e+01 -0.11346000 -0.3104600  9.3156  55.911 1.38500 #> 396 41    4 3.6263e+01  0.13918000  1.5993000  9.3156  55.911 1.38500 #> 397 41    5 3.0864e+01  0.21047000  2.1102000  9.3156  55.911 1.38500 #> 398 41    6 2.6169e+01 -0.07409700 -0.0830530  9.3156  55.911 1.38500 #> 399 41    7 2.2163e+01  0.01023100  0.5099300  9.3156  55.911 1.38500 #> 400 41    8 1.8764e+01 -0.17610000 -0.9463200  9.3156  55.911 1.38500 #> 401 41   10 1.3447e+01  0.12735000  1.2584000  9.3156  55.911 1.38500 #> 402 41   12 9.6366e+00 -0.00898410  0.1424100  9.3156  55.911 1.38500 #> 404 42    1 1.1185e+02 -0.00774830  0.0406750 37.2660  47.824 2.10400 #> 405 42    2 6.4951e+01  0.11376000 -0.3242400 37.2660  47.824 2.10400 #> 406 42    3 3.1460e+01 -0.01749700 -1.6388000 37.2660  47.824 2.10400 #> 407 42    4 1.4636e+01 -0.04342600 -1.5594000 37.2660  47.824 2.10400 #> 408 42    6 3.0945e+00  0.01146600 -0.3731500 37.2660  47.824 2.10400 #> 409 42    8 6.5147e-01  0.02843600  0.0301440 37.2660  47.824 2.10400 #> 411 43    1 4.8318e+00 -0.02934900 -0.9231600 19.0340 126.070 1.09440 #> 412 43    2 5.7721e+00  0.00482860 -0.7521000 19.0340 126.070 1.09440 #> 413 43    3 5.5047e+00  0.07181300 -0.1372700 19.0340 126.070 1.09440 #> 414 43    4 4.9146e+00  0.06418700 -0.0269800 19.0340 126.070 1.09440 #> 415 43    5 4.2865e+00  0.09178900  0.3631100 19.0340 126.070 1.09440 #> 416 43    6 3.7062e+00 -0.03944200 -0.4556700 19.0340 126.070 1.09440 #> 417 43    7 3.1936e+00 -0.22345000 -1.6905000 19.0340 126.070 1.09440 #> 418 43    8 2.7484e+00 -0.09037400 -0.5471300 19.0340 126.070 1.09440 #> 419 43   10 2.0330e+00  0.05263100  0.7423900 19.0340 126.070 1.09440 #> 420 43   12 1.5033e+00  0.11092000  1.3102000 19.0340 126.070 1.09440 #> 422 44    1 2.9118e+01 -0.00749780 -0.3942700 40.0860  57.104 1.23880 #> 423 44    2 2.2868e+01 -0.07817300 -1.2920000 40.0860  57.104 1.23880 #> 424 44    3 1.3778e+01  0.09526100 -0.2329800 40.0860  57.104 1.23880 #> 425 44    4 7.5363e+00  0.16370000  0.1880900 40.0860  57.104 1.23880 #> 426 44    5 3.9402e+00 -0.13456000 -1.9837000 40.0860  57.104 1.23880 #> 427 44    6 2.0122e+00 -0.08557600 -1.3889000 40.0860  57.104 1.23880 #> 428 44    7 1.0145e+00  0.07446100  0.0680140 40.0860  57.104 1.23880 #> 429 44    8 5.0775e-01 -0.05466100 -0.7355000 40.0860  57.104 1.23880 #> 430 44   10 1.2585e-01  0.11245000  0.5970300 40.0860  57.104 1.23880 #> 431 44   12 3.1006e-02 -0.03243100 -0.5368500 40.0860  57.104 1.23880 #> 433 45    1 5.1880e+01  0.02159500  0.2926200 27.7580  59.022 1.71030 #> 434 45    4 1.7683e+01  0.01680300 -0.9072300 27.7580  59.022 1.71030 #> 435 45    8 2.7139e+00  0.01331400 -0.6966400 27.7580  59.022 1.71030 #> 436 45   11 6.6200e-01  0.01207800 -0.3429100 27.7580  59.022 1.71030 #> 438 46    1 1.5517e+02  0.04311500  1.0758000 11.6740  44.455 1.67430 #> 439 46    2 1.4842e+02  0.06167000  1.2032000 11.6740  44.455 1.67430 #> 440 46    3 1.1959e+02 -0.02258700  0.3841300 11.6740  44.455 1.67430 #> 441 46    4 9.2993e+01  0.09878900  1.1093000 11.6740  44.455 1.67430 #> 442 46    5 7.1708e+01 -0.02814000 -0.0160700 11.6740  44.455 1.67430 #> 443 46    6 5.5183e+01 -0.12491000 -0.8766500 11.6740  44.455 1.67430 #> 444 46    7 4.2445e+01  0.12145000  0.8934700 11.6740  44.455 1.67430 #> 445 46    8 3.2644e+01  0.19411000  1.3792000 11.6740  44.455 1.67430 #> 446 46    9 2.5105e+01 -0.15474000 -1.2994000 11.6740  44.455 1.67430 #> 447 46   11 1.4848e+01 -0.01938900 -0.2983900 11.6740  44.455 1.67430 #> 449 47    1 7.0883e+01  0.04002800 -0.2106100 11.2530 113.650 1.96010 #> 450 47    4 6.2328e+01  0.00709510 -0.3372900 11.2530 113.650 1.96010 #> 451 47    5 5.6480e+01 -0.07436300 -0.7245400 11.2530 113.650 1.96010 #> 452 47    8 4.1969e+01 -0.00331600  0.3640700 11.2530 113.650 1.96010 #> 453 47   12 2.8244e+01  0.09191500  1.5126000 11.2530 113.650 1.96010 #> 455 48    1 9.1965e+01  0.30452000  3.3693000 10.2800  44.757 3.92500 #> 456 48    2 7.4907e+01 -0.26162000 -1.4146000 10.2800  44.757 3.92500 #> 457 48    3 5.9571e+01 -0.12675000 -0.4617800 10.2800  44.757 3.92500 #> 458 48    4 4.7346e+01  0.04358900  0.7934400 10.2800  44.757 3.92500 #> 459 48    5 3.7630e+01  0.07786900  1.0297000 10.2800  44.757 3.92500 #> 460 48    6 2.9907e+01  0.13450000  1.4412000 10.2800  44.757 3.92500 #> 461 48    7 2.3770e+01 -0.02312900  0.2345100 10.2800  44.757 3.92500 #> 462 48    8 1.8892e+01 -0.19436000 -1.0742000 10.2800  44.757 3.92500 #> 463 48    9 1.5015e+01 -0.18414000 -1.0108000 10.2800  44.757 3.92500 #> 464 48   10 1.1933e+01  0.14301000  1.4484000 10.2800  44.757 3.92500 #> 465 48   11 9.4844e+00 -0.18603000 -1.0598000 10.2800  44.757 3.92500 #> 466 48   12 7.5380e+00  0.21783000  1.9742000 10.2800  44.757 3.92500 #> 468 49    1 1.9858e+01 -0.03866100 -1.1673000 18.4130 150.760 1.02910 #> 469 49    2 2.4671e+01  0.07253100 -0.3768900 18.4130 150.760 1.02910 #> 470 49    3 2.4370e+01  0.00410960 -0.7504300 18.4130 150.760 1.02910 #> 471 49    4 2.2474e+01 -0.09095300 -1.2758000 18.4130 150.760 1.02910 #> 472 49    5 2.0214e+01  0.19126000  1.0555000 18.4130 150.760 1.02910 #> 473 49    6 1.8006e+01 -0.24524000 -2.0661000 18.4130 150.760 1.02910 #> 474 49    7 1.5977e+01  0.05402700  0.3575500 18.4130 150.760 1.02910 #> 475 49    8 1.4155e+01 -0.08510900 -0.5588300 18.4130 150.760 1.02910 #> 476 49   10 1.1094e+01 -0.03457500  0.0292160 18.4130 150.760 1.02910 #> 477 49   12 8.6901e+00  0.12196000  1.3404000 18.4130 150.760 1.02910 #> 479 50    1 6.4334e+01  0.02154400  0.4775500 31.1660  39.413 1.70950 #> 480 50    2 4.0817e+01  0.03216600 -0.2933900 31.1660  39.413 1.70950 #> 481 50    3 2.0617e+01 -0.00956410 -1.2297000 31.1660  39.413 1.70950 #> 482 50    4 9.7311e+00  0.18589000 -0.0508730 31.1660  39.413 1.70950 #> 483 50    5 4.4821e+00 -0.02053800 -1.5527000 31.1660  39.413 1.70950 #> 484 50    6 2.0451e+00 -0.05628000 -1.4799000 31.1660  39.413 1.70950 #> 485 50    7 9.2971e-01  0.07560000 -0.0285050 31.1660  39.413 1.70950 #> 486 50    8 4.2203e-01 -0.14699000 -1.2748000 31.1660  39.413 1.70950 #> 487 50   10 8.6844e-02 -0.07880900 -0.1962200 31.1660  39.413 1.70950 #> 488 50   12 1.7862e-02  0.11968000  1.2797000 31.1660  39.413 1.70950 #> 490 51    1 4.1926e+01  0.01631500 -0.0527550 10.5210  80.379 1.30620 #> 491 51    2 4.8138e+01 -0.00951580 -0.2385700 10.5210  80.379 1.30620 #> 492 51    3 4.5308e+01  0.05169300  0.3256300 10.5210  80.379 1.30620 #> 493 51    4 4.0582e+01  0.10787000  0.8809500 10.5210  80.379 1.30620 #> 494 51    5 3.5829e+01  0.02040400  0.3517000 10.5210  80.379 1.30620 #> 495 51    6 3.1494e+01  0.02780700  0.5304000 10.5210  80.379 1.30620 #> 496 51    8 2.4259e+01 -0.18506000 -0.8791500 10.5210  80.379 1.30620 #> 497 51   10 1.8673e+01 -0.08692700  0.0045329 10.5210  80.379 1.30620 #> 498 51   12 1.4372e+01  0.16751000  2.0196000 10.5210  80.379 1.30620 #> 500 52    1 2.9898e+01 -0.06982700 -0.9819300 11.7460 101.520 1.04760 #> 501 52    2 3.7118e+01  0.20049000  1.1901000 11.7460 101.520 1.04760 #> 502 52    3 3.6741e+01 -0.24580000 -2.0019000 11.7460 101.520 1.04760 #> 503 52    4 3.4017e+01  0.04918300  0.3934300 11.7460 101.520 1.04760 #> 504 52    5 3.0753e+01  0.06136400  0.6158800 11.7460 101.520 1.04760 #> 505 52    6 2.7552e+01 -0.27155000 -1.8036000 11.7460 101.520 1.04760 #> 506 52    7 2.4597e+01  0.07777400  0.9120700 11.7460 101.520 1.04760 #> 507 52    8 2.1929e+01  0.12681000  1.3332000 11.7460 101.520 1.04760 #> 508 52   10 1.7407e+01  0.03691100  0.6973700 11.7460 101.520 1.04760 #> 509 52   12 1.3812e+01  0.00054528  0.4086800 11.7460 101.520 1.04760 #> 511 53    1 1.3954e+01  0.02907300  0.7636400 14.3100  46.439 1.52420 #> 512 53    2 1.3293e+01  0.10737000  1.4841000 14.3100  46.439 1.52420 #> 513 53    3 1.0429e+01 -0.13514000 -0.5282400 14.3100  46.439 1.52420 #> 514 53    4 7.8078e+00 -0.02660900  0.0348030 14.3100  46.439 1.52420 #> 515 53    5 5.7685e+00  0.11813000  0.8853300 14.3100  46.439 1.52420 #> 516 53    6 4.2456e+00  0.14472000  0.8928400 14.3100  46.439 1.52420 #> 517 53    7 3.1211e+00  0.03487700 -0.0721810 14.3100  46.439 1.52420 #> 518 53    8 2.2937e+00 -0.08010700 -1.0210000 14.3100  46.439 1.52420 #> 519 53   10 1.2385e+00 -0.06341000 -0.9310700 14.3100  46.439 1.52420 #> 520 53   12 6.6873e-01  0.04676100 -0.0510060 14.3100  46.439 1.52420 #> 522 54    1 1.6696e+01 -0.10158000 -1.5380000 41.2260  90.404 0.48267 #> 523 54    2 2.0886e+01 -0.01319800 -1.0871000 41.2260  90.404 0.48267 #> 524 54    3 1.9596e+01 -0.06103600 -1.4117000 41.2260  90.404 0.48267 #> 525 54    4 1.6344e+01 -0.02105800 -1.0055000 41.2260  90.404 0.48267 #> 526 54    5 1.2781e+01  0.19712000  0.7455100 41.2260  90.404 0.48267 #> 527 54    6 9.5949e+00 -0.01302200 -0.7668500 41.2260  90.404 0.48267 #> 528 54    7 7.0036e+00  0.09372200  0.0975670 41.2260  90.404 0.48267 #> 529 54    8 5.0081e+00 -0.12741000 -1.5299000 41.2260  90.404 0.48267 #> 530 54   10 2.4512e+00  0.18310000  0.9160200 41.2260  90.404 0.48267 #> 531 54   12 1.1520e+00 -0.21007000 -1.9179000 41.2260  90.404 0.48267 #> 533 55    1 9.8934e+01 -0.00630550 -0.0512920 31.5160  57.635 1.58060 #> 534 55    2 7.7626e+01  0.03289900 -0.1628500 31.5160  57.635 1.58060 #> 535 55    3 4.9121e+01  0.10056000  0.0809450 31.5160  57.635 1.58060 #> 536 55    4 2.9293e+01 -0.07725500 -1.3093000 31.5160  57.635 1.58060 #> 537 55    5 1.7132e+01 -0.16705000 -1.8756000 31.5160  57.635 1.58060 #> 538 55    6 9.9522e+00  0.26203000  1.5477000 31.5160  57.635 1.58060 #> 539 55    7 5.7677e+00 -0.23886000 -2.0685000 31.5160  57.635 1.58060 #> 540 55    9 1.9333e+00  0.06035500  0.4120400 31.5160  57.635 1.58060 #> 541 55   11 6.4769e-01  0.00356880  0.0143560 31.5160  57.635 1.58060 #> 543 56    1 1.3860e+01  0.07936600  1.2735000 10.2410  57.740 2.31850 #> 544 56    4 9.2232e+00 -0.05455800 -0.0300320 10.2410  57.740 2.31850 #> 545 56    5 7.7254e+00  0.12097000  1.3176000 10.2410  57.740 2.31850 #> 546 56    8 4.5377e+00 -0.03476200  0.1492300 10.2410  57.740 2.31850 #> 547 56   10 3.1826e+00 -0.04793900  0.0228800 10.2410  57.740 2.31850 #> 548 56   12 2.2321e+00  0.08865700  1.0122000 10.2410  57.740 2.31850 #> 550 57    1 6.9227e+00 -0.13473000 -1.9686000 24.4530 122.680 0.20826 #> 551 57    2 1.1293e+01  0.17418000  0.0249840 24.4530 122.680 0.20826 #> 552 57    3 1.3817e+01 -0.04535400 -1.6880000 24.4530 122.680 0.20826 #> 553 57    4 1.5026e+01 -0.05830900 -1.6810000 24.4530 122.680 0.20826 #> 554 57    5 1.5320e+01  0.06394800 -0.5701300 24.4530 122.680 0.20826 #> 555 57    6 1.4995e+01 -0.04037900 -1.1387000 24.4530 122.680 0.20826 #> 556 57    7 1.4270e+01 -0.11492000 -1.4757000 24.4530 122.680 0.20826 #> 557 57    8 1.3302e+01  0.11408000  0.4727400 24.4530 122.680 0.20826 #> 558 57    9 1.2207e+01  0.10101000  0.5682600 24.4530 122.680 0.20826 #> 559 57   10 1.1063e+01 -0.07171300 -0.5710700 24.4530 122.680 0.20826 #> 561 58    1 2.5711e+01  0.00773680 -0.5293700 31.1610  96.146 2.05250 #> 562 58    4 1.1812e+01  0.10058000 -0.1497500 31.1610  96.146 2.05250 #> 563 58    5 8.5492e+00 -0.02797600 -0.7956400 31.1610  96.146 2.05250 #> 564 58    6 6.1835e+00 -0.26579000 -2.2875000 31.1610  96.146 2.05250 #> 565 58    7 4.4719e+00  0.08902800  0.6540600 31.1610  96.146 2.05250 #> 566 58    8 3.2340e+00 -0.09399600 -0.5368000 31.1610  96.146 2.05250 #> 567 58   10 1.6913e+00  0.12337000  1.3084000 31.1610  96.146 2.05250 #> 569 59    1 3.3550e+01  0.07867700  0.2457100 10.2520  81.606 0.88692 #> 570 59    2 4.3410e+01 -0.07762100 -0.7281200 10.2520  81.606 0.88692 #> 571 59    3 4.3977e+01 -0.18504000 -1.3119000 10.2520  81.606 0.88692 #> 572 59    4 4.1130e+01  0.19036000  1.7016000 10.2520  81.606 0.88692 #> 573 59    6 3.3242e+01 -0.07616000 -0.1300400 10.2520  81.606 0.88692 #> 574 59    7 2.9481e+01  0.01997700  0.6330200 10.2520  81.606 0.88692 #> 575 59    8 2.6068e+01  0.13434000  1.5092000 10.2520  81.606 0.88692 #> 576 59   12 1.5805e+01 -0.01298600  0.2944900 10.2520  81.606 0.88692 #> 578 60    1 6.5817e+01  0.10990000  1.3867000 17.6650  57.699 2.83310 #> 579 60    2 5.2331e+01 -0.01550400 -0.1181400 17.6650  57.699 2.83310 #> 580 60    3 3.8758e+01  0.03101200  0.1366400 17.6650  57.699 2.83310 #> 581 60    4 2.8550e+01 -0.17933000 -1.4640000 17.6650  57.699 2.83310 #> 582 60    5 2.1021e+01  0.07461900  0.4641100 17.6650  57.699 2.83310 #> 583 60    6 1.5478e+01 -0.05411500 -0.5045200 17.6650  57.699 2.83310 #> 584 60    7 1.1396e+01  0.08110500  0.5174700 17.6650  57.699 2.83310 #> 585 60    8 8.3904e+00  0.14178000  0.9693200 17.6650  57.699 2.83310 #> 586 60   10 4.5484e+00 -0.07001200 -0.6543900 17.6650  57.699 2.83310 #> 587 60   12 2.4657e+00  0.00579400 -0.0951060 17.6650  57.699 2.83310 #> 589 61    1 1.0173e+01  0.04297900  0.3955400 26.5870  68.930 2.38350 #> 590 61    2 7.8553e+00  0.02860000 -0.3127000 26.5870  68.930 2.38350 #> 591 61    4 3.6987e+00 -0.10240000 -1.3165000 26.5870  68.930 2.38350 #> 592 61    5 2.5157e+00  0.08119400  0.1839600 26.5870  68.930 2.38350 #> 593 61    6 1.7107e+00 -0.12900000 -1.3038000 26.5870  68.930 2.38350 #> 594 61    7 1.1632e+00  0.08321600  0.3805600 26.5870  68.930 2.38350 #> 595 61    8 7.9094e-01  0.12525000  0.7525200 26.5870  68.930 2.38350 #> 596 61    9 5.3781e-01 -0.08889300 -0.8335900 26.5870  68.930 2.38350 #> 597 61   10 3.6569e-01  0.03913400  0.1555100 26.5870  68.930 2.38350 #> 598 61   11 2.4866e-01  0.00540810 -0.0877290 26.5870  68.930 2.38350 #> 600 62    1 4.8825e+01  0.04207900  0.3835100 11.9300  78.686 1.87730 #> 601 62    2 4.9427e+01  0.02899800  0.1362800 11.9300  78.686 1.87730 #> 602 62    3 4.3616e+01 -0.04439000 -0.3760400 11.9300  78.686 1.87730 #> 603 62    4 3.7655e+01  0.02961200  0.2764900 11.9300  78.686 1.87730 #> 604 62    5 3.2384e+01  0.09960600  0.8946100 11.9300  78.686 1.87730 #> 605 62    6 2.7833e+01 -0.10536000 -0.5843200 11.9300  78.686 1.87730 #> 606 62    7 2.3918e+01  0.03772900  0.5524200 11.9300  78.686 1.87730 #> 607 62    8 2.0553e+01  0.01640100  0.4290800 11.9300  78.686 1.87730 #> 608 62   10 1.5177e+01  0.05226200  0.7367700 11.9300  78.686 1.87730 #> 610 63    1 3.6355e+01  0.08981200  0.7463700 16.9870  56.371 1.19920 #> 611 63    2 3.7855e+01 -0.09363600 -0.2939700 16.9870  56.371 1.19920 #> 612 63    3 3.1309e+01 -0.17276000 -0.8692900 16.9870  56.371 1.19920 #> 613 63    4 2.4159e+01  0.20660000  1.8465000 16.9870  56.371 1.19920 #> 614 63    5 1.8174e+01  0.03557200  0.3308000 16.9870  56.371 1.19920 #> 615 63    7 1.0041e+01  0.05662200  0.0921260 16.9870  56.371 1.19920 #> 616 63    8 7.4372e+00 -0.04936900 -0.8380600 16.9870  56.371 1.19920 #> 617 63   10 4.0733e+00  0.10476000  0.2104300 16.9870  56.371 1.19920 #> 618 63   11 3.0138e+00 -0.09415700 -1.3027000 16.9870  56.371 1.19920 #> 620 64    1 2.3523e+01  0.11166000  0.1218500 20.3520  88.502 0.63827 #> 621 64    2 3.1116e+01 -0.11653000 -1.4177000 20.3520  88.502 0.63827 #> 622 64    3 3.1287e+01 -0.13158000 -1.2630000 20.3520  88.502 0.63827 #> 623 64    4 2.8326e+01  0.07393100  0.5058100 20.3520  88.502 0.63827 #> 624 64    5 2.4338e+01  0.01981500  0.2278000 20.3520  88.502 0.63827 #> 625 64    6 2.0305e+01  0.01994300  0.2857100 20.3520  88.502 0.63827 #> 626 64    8 1.3495e+01 -0.23305000 -1.6602000 20.3520  88.502 0.63827 #> 627 64    9 1.0865e+01  0.30509000  2.3606000 20.3520  88.502 0.63827 #> 628 64   11 6.9591e+00  0.04899200  0.3208300 20.3520  88.502 0.63827 #> 629 64   12 5.5504e+00 -0.18745000 -1.5037000 20.3520  88.502 0.63827 #> 631 65    1 2.2273e+01 -0.10476000 -1.4684000  9.5706 141.070 1.06080 #> 632 65    2 2.8523e+01  0.18992000  0.6221200  9.5706 141.070 1.06080 #> 633 65    3 2.9321e+01  0.02860900 -0.5076000  9.5706 141.070 1.06080 #> 634 65    4 2.8322e+01 -0.17131000 -1.8497000  9.5706 141.070 1.06080 #> 635 65    5 2.6784e+01  0.01702400 -0.2295400  9.5706 141.070 1.06080 #> 636 65    6 2.5138e+01  0.02116600 -0.0012811  9.5706 141.070 1.06080 #> 637 65    7 2.3527e+01 -0.10105000 -0.7373800  9.5706 141.070 1.06080 #> 638 65    9 2.0559e+01 -0.00774140  0.3066900  9.5706 141.070 1.06080 #> 639 65   10 1.9212e+01 -0.05633000  0.0869140  9.5706 141.070 1.06080 #> 640 65   12 1.6775e+01  0.20296000  2.3031000  9.5706 141.070 1.06080 #>           ETA1        ETA2      ETA3 AGE  HT     WT SECR SEX RACE SMOK HCTZ #> 2   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 3   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 4   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 5   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 6   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 7   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 8   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 9   -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 10  -0.2677300  0.19829000 -0.163610  55 154  80.97  1.0   2    2    0    1 #> 12  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 13  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 14  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 15  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 16  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 17  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 18  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 19  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 20  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 21  -0.7096900  0.18606000  0.736900  37 179  93.21  1.2   1    1    1    0 #> 23  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 24  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 25  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 26  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 27  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 28  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 29  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 30  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 31  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 32  -0.4762000  0.20152000  0.435760  35 188  94.35  0.9   1    1    0    0 #> 34   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 35   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 36   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 37   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 38   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 39   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 40   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 41   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 42   0.0995890 -0.42932000  0.150950  67 168  74.39  0.8   2    2    0    0 #> 44  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 45  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 46  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 47  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 48  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 49  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 50  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 51  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 52  -0.3529400  0.09802600  0.524390  69 165  91.85  1.0   2    2    0    0 #> 54  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 55  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 56  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 57  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 58  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 59  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 60  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 61  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 62  -0.2094000 -0.04955100 -0.507630  52 157 104.30  0.8   2    2    0    1 #> 64  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 65  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 66  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 67  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 68  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 69  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 70  -0.2306200 -0.49262000  0.726630  44 140  90.04  0.9   2    2    0    1 #> 72   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 73   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 74   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 75   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 76   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 77   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 78   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 79   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 80   0.8649600  0.31834000  0.899480  50 173  98.88  0.9   2    2    1    1 #> 82   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 83   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 84   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 85   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 86   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 87   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 88   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 89   0.1838300 -0.28868000  0.108170  61 160  81.42  0.9   2    2    0    1 #> 91   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 92   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 93   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 94   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 95   0.9142300 -0.78827000  0.903260  52 168  87.32  1.8   2    2    0    1 #> 97   0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 98   0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 99   0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 100  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 101  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 102  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 103  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 104  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 105  0.0519620  0.18720000  0.288710  59 178  98.43  1.1   1    2    0    0 #> 107  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 108  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 109  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 110  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 111  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 112  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 113  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 114  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 115  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 116  0.6696900  0.01963300 -0.251550  54 159  68.04  1.3   1    2    0    0 #> 118  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 119  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 120  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 121  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 122  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 123  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 124  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 125  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 126  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 127  0.1328700 -0.28590000  0.541810  62 180  81.65  1.1   1    2    0    0 #> 129 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 130 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 131 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 132 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 133 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 134 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 135 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 136 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 137 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 138 -0.0202610  0.43322000 -0.467300  63 172  83.10  1.2   1    1    0    1 #> 140 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 141 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 142 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 143 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 144 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 145 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 146 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 147 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 148 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 149 -0.3382600 -0.08527500 -0.998650  63 170  83.40  1.1   1    1    1    0 #> 151 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 152 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 153 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 154 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 155 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 156 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 157 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 158 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 159 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 160 -0.5454700 -0.42393000  0.559110  63 177 104.10  1.0   1    1    1    1 #> 162 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 163 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 164 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 165 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 166 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 167 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 168 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 169 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 170 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 171 -0.3177900  0.00085736  0.476210  58 187 136.80  1.5   1    2    1    1 #> 173 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 174 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 175 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 176 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 177 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 178 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 179 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 180 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 181 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 182 -0.5062700 -0.12185000  0.071619  66 177  97.30  1.2   1    1    1    0 #> 184  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 185  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 186  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 187  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 188  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 189  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 190  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 191  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 192  0.0830370  0.11548000 -0.775380  67 181  96.10  1.3   1    1    1    0 #> 194  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 195  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 196  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 197  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 198  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 199  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 200  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 201  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 202  0.5230000  0.23181000 -1.435500  57 180  85.90  1.2   1    1    1    1 #> 204  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 205  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 206  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 207  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 208  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 209  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 210  0.6570900  0.17139000 -0.196660  56 170  88.13  0.8   1    2    0    1 #> 212 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 213 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 214 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 215 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 216 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 217 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 218 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 219 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 220 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 221 -0.5050000  0.68246000 -0.298660  57 168  69.08  1.1   2    3    0    1 #> 223 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 224 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 225 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 226 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 227 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 228 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 229 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 230 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 231 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 232 -0.4769500  0.09641500 -1.051000  56 175  74.60  0.8   2    1    0    0 #> 234  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 235  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 236  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 237  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 238  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 239  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 240  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 241  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 242  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 243  0.3648000  0.01813000 -0.080632  61 171  96.62  1.0   1    1    0    1 #> 245 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 246 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 247 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 248 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 249 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 250 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 251 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 252 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 253 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 254 -0.3137700  0.11255000 -1.435900  67 157  66.40  0.9   2    1    0    0 #> 256  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 257  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 258  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 259  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 260  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 261  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 262  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 263  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 264  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 265  0.1147000 -0.26872000 -0.410290  56 177  97.40  1.0   1    1    0    0 #> 267 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 268 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 269 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 270 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 271 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 272 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 273 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 274 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 275 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 276 -0.0307930  0.09713100  0.083805  58 173  78.70  1.4   1    1    0    1 #> 278  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 279  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 280  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 281  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 282  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 283  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 284  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 285  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 286  0.5407200  0.50365000 -1.274500  53 180  87.60  1.2   1    1    0    1 #> 288 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 289 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 290 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 291 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 292 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 293 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 294 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 295 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 296 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 297 -0.3778400  0.11242000 -0.385960  46 175  84.80  1.2   1    1    0    1 #> 299 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 300 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 301 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 302 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 303 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 304 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 305 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 306 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 307 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 308 -0.1609800  0.08372100 -0.326850  30 157  61.70  1.1   2    1    0    0 #> 310  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 311  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 312  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 313  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 314  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 315  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 316  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 317  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 318  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 319  0.2271000  0.74060000  0.272760  56 174  68.72  1.1   2    1    0    1 #> 321 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 322 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 323 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 324 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 325 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 326 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 327 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 328 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 329 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 330 -0.5924400 -0.07710300  0.153600  54 180  76.43  1.0   1    1    0    0 #> 332  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 333  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 334  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 335  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 336  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 337  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 338  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 339  0.4283100 -0.13590000  0.011405  34 170  77.34  1.0   1    1    0    0 #> 341  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 342  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 343  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 344  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 345  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 346  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 347  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 348  0.3857600 -0.15179000 -0.733000  52 183  89.36  1.1   1    1    0    1 #> 350  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 351  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 352  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 353  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 354  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 355  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 356  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 357  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 358  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 359  0.2638900 -0.46702000  0.270130  47 175  93.21  1.1   1    1    0    1 #> 361 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 362 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 363 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 364 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 365 -0.1044000 -0.01194300 -1.123800  66 155  93.44  1.4   1    1    0    1 #> 367  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 368  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 369  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 370  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 371  0.1186100 -0.34010000  0.534780  66 160  62.14  1.1   1    2    1    0 #> 373  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 374  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 375  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 376  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 377  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 378  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 379  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 380  0.0022479  0.36232000  0.015372  51 178  97.07  1.0   1    1    0    1 #> 382  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 383  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 384  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 385  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 386  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 387  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 388  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 389  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 390  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 391  0.2588200  0.24826000  0.886430  24 181  80.29  1.4   1    1    0    0 #> 393 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 394 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 395 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 396 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 397 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 398 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 399 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 400 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 401 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 402 -0.6445800 -0.31740000 -0.040775  33 176  80.74  1.3   1    1    0    0 #> 404  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 405  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 406  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 407  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 408  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 409  0.7418100 -0.47364000  0.377420  41 168  84.37  1.5   1    1    0    1 #> 411  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 412  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 413  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 414  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 415  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 416  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 417  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 418  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 419  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 420  0.0699470  0.49570000 -0.276230  38 173  67.13  1.2   1    1    0    0 #> 422  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 423  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 424  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 425  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 426  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 427  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 428  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 429  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 430  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 431  0.8147600 -0.29630000 -0.152320  54 183 103.40  1.1   1    1    1    0 #> 433  0.4472500 -0.26326000  0.170240  51 170  83.14  1.2   1    2    1    1 #> 434  0.4472500 -0.26326000  0.170240  51 170  83.14  1.2   1    2    1    1 #> 435  0.4472500 -0.26326000  0.170240  51 170  83.14  1.2   1    2    1    1 #> 436  0.4472500 -0.26326000  0.170240  51 170  83.14  1.2   1    2    1    1 #> 438 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 439 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 440 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 441 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 442 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 443 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 444 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 445 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 446 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 447 -0.4189200 -0.54669000  0.148980  58 159  69.31  1.1   2    2    1    1 #> 449 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 450 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 451 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 452 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 453 -0.4556400  0.39196000  0.306570  56 187 108.20  1.0   1    1    1    1 #> 455 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 456 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 457 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 458 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 459 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 460 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 461 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 462 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 463 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 464 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 465 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 466 -0.5460500 -0.53992000  1.000900  63 178  93.80  1.0   1    1    1    1 #> 468  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 469  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 470  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 471  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 472  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 473  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 474  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 475  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 476  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 477  0.0368020  0.67453000 -0.337790  50 157 125.40  0.7   2    1    1    1 #> 479  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 480  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 481  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 482  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 483  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 484  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 485  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 486  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 487  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 488  0.5630700 -0.66706000  0.169760  62 147  51.03  0.8   2    2    0    1 #> 490 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 491 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 492 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 493 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 494 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 495 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 496 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 497 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 498 -0.5228900  0.04559300 -0.099348  48 185  96.30  1.1   1    2    0    1 #> 500 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 501 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 502 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 503 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 504 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 505 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 506 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 507 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 508 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 509 -0.4128000  0.27905000 -0.319890  57 165  70.53  1.0   2    1    0    0 #> 511 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 512 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 513 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 514 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 515 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 516 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 517 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 518 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 519 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 520 -0.2152900 -0.50303000  0.055032  67 160  83.24  1.0   2    1    0    0 #> 522  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 523  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 524  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 525  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 526  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 527  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 528  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 529  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 530  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 531  0.8428000  0.16313000 -1.094900  39 169  78.25  1.0   2    2    1    1 #> 533  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 534  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 535  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 536  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 537  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 538  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 539  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 540  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 541  0.5742300 -0.28704000  0.091381  47 168  72.12  0.7   2    1    0    1 #> 543 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 544 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 545 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 546 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 547 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 548 -0.5498400 -0.28522000  0.474490  36 157  88.45  0.9   2    2    0    1 #> 550  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 551  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 552  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 553  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 554  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 555  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 556  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 557  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 558  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 559  0.3204900  0.46845000 -1.935400  63 173  73.94  0.9   1    1    0    1 #> 561  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 562  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 563  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 564  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 565  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 566  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 567  0.5628900  0.22471000  0.352630  61 178  67.27  1.1   1    1    0    0 #> 569 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 570 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 571 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 572 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 573 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 574 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 575 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 576 -0.5487700  0.06073900 -0.486440  53 157  55.02  0.8   2    1    0    1 #> 578 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 579 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 580 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 581 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 582 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 583 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 584 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 585 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 586 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 587 -0.0046978 -0.28592000  0.674930  55 173  78.70  1.1   1    1    0    0 #> 589  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 590  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 591  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 592  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 593  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 594  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 595  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 596  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 597  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 598  0.4041700 -0.10807000  0.502120  58 179  94.57  1.0   1    1    0    0 #> 600 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 601 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 602 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 603 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 604 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 605 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 606 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 607 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 608 -0.3972100  0.02430100  0.263420  56 179 102.30  1.2   1    1    0    1 #> 610 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 611 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 612 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 613 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 614 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 615 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 616 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 617 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 618 -0.0438400 -0.30920000 -0.184750  66 182  94.80  1.1   1    1    0    0 #> 620  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 621  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 622  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 623  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 624  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 625  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 626  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 627  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 628  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 629  0.1369200  0.14186000 -0.815440  48 183 111.80  1.2   1    1    0    0 #> 631 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 632 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 633 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 634 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 635 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 636 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 637 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 638 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 639 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #> 640 -0.6175700  0.60810000 -0.307400  64 180  99.79  1.1   1    1    0    0 #>     PROP CON OCC     DV     PRED         RES       WRES #> 2      1   1   0  71.74 86.41800 -14.6780000 -0.1046800 #> 3      1   1   0  72.61 89.00800 -16.3980000 -0.7589000 #> 4      1   1   0  88.01 75.46800  12.5420000  1.2075000 #> 5      1   1   0  53.13 61.03600  -7.9060000 -1.5386000 #> 6      1   1   0  56.83 48.71100   8.1188000 -0.1152000 #> 7      1   1   0  51.94 38.72400  13.2160000  0.3580400 #> 8      1   1   0  52.89 30.74800  22.1420000  2.0247000 #> 9      1   1   0  26.95 19.37200   7.5781000 -1.3045000 #> 10     1   1   0  26.17 12.20200  13.9680000  1.3377000 #> 12     0   0   0 108.75 86.41800  22.3320000  0.7885000 #> 13     0   0   0  96.60 89.00800   7.5921000  0.2056300 #> 14     0   0   0  81.00 75.46800   5.5322000 -0.1610800 #> 15     0   0   0  77.07 61.03600  16.0340000  0.3367900 #> 16     0   0   0  64.57 48.71100  15.8590000 -0.4293500 #> 17     0   0   0  50.21 38.72400  11.4860000 -1.9657000 #> 18     0   0   0  64.58 30.74800  33.8320000  1.7130000 #> 19     0   0   0  50.54 24.40700  26.1330000 -0.0245470 #> 20     0   0   0  41.56 15.37500  26.1850000  0.4600500 #> 21     0   0   0  42.46  9.68460  32.7750000  5.4087000 #> 23     0   0   0   9.35  8.64180   0.7082200  0.4329000 #> 24     0   0   0   8.66  8.90080  -0.2407900 -0.2000400 #> 25     0   0   0   8.18  7.54680   0.6332200  0.2378000 #> 26     0   0   0   6.19  6.10360   0.0863950 -0.9516600 #> 27     0   0   0   7.08  4.87110   2.2089000  1.2177000 #> 28     0   0   0   4.68  3.87240   0.8076300 -1.4248000 #> 29     0   0   0   5.30  3.07480   2.2252000  0.8501900 #> 30     0   0   0   4.20  2.44070   1.7593000 -0.2344000 #> 31     0   0   0   3.92  1.53750   2.3825000  2.1772000 #> 32     0   0   0   2.75  0.96846   1.7815000  1.4402000 #> 34     0   0   0  63.15 43.20900  19.9410000  0.4473600 #> 35     0   0   0  62.41 44.50400  17.9060000  1.5121000 #> 36     0   0   0  41.05 37.73400   3.3161000  0.2790000 #> 37     0   0   0  24.31 30.51800  -6.2080000 -0.9520100 #> 38     0   0   0  18.17 24.35600  -6.1856000 -0.5277300 #> 39     0   0   0  13.23 19.36200  -6.1319000 -0.2349800 #> 40     0   0   0   8.48 15.37400  -6.8942000 -0.4056700 #> 41     0   0   0   3.92  9.68590  -5.7659000 -0.0433470 #> 42     0   0   0   1.76  6.10120  -4.3412000  0.3823900 #> 44     0   1   0  21.49 17.28400   4.2064000  1.1866000 #> 45     0   1   0  14.87 17.80200  -2.9316000 -1.4188000 #> 46     0   1   0  15.17 15.09400   0.0764420 -0.3857700 #> 47     0   1   0  13.68 12.20700   1.4728000 -0.0473140 #> 48     0   1   0  14.83  9.74220   5.0878000  2.0694000 #> 49     0   1   0   9.86  7.74470   2.1153000 -0.3700300 #> 50     0   1   0   8.75  6.14970   2.6003000 -0.1412700 #> 51     0   1   0   7.77  3.87440   3.8956000  1.7438000 #> 52     0   1   0   4.36  2.44050   1.9195000 -0.7673200 #> 54     1   1   0  74.53 86.41800 -11.8880000  0.1878600 #> 55     1   1   0  63.02 89.00800 -25.9880000 -2.0608000 #> 56     1   1   0 100.46 75.46800  24.9920000  1.6366000 #> 57     1   1   0  84.92 61.03600  23.8840000  1.3909000 #> 58     1   1   0  59.01 48.71100  10.2990000 -0.4326400 #> 59     1   1   0  53.87 38.72400  15.1460000  0.2855400 #> 60     1   1   0  45.15 30.74800  14.4020000  0.3112900 #> 61     1   1   0  33.88 24.40700   9.4727000 -0.5882100 #> 62     1   1   0  26.27 15.37500  10.8950000  0.4373300 #> 64     1   1   0 184.78 86.41800  98.3620000  2.2384000 #> 65     1   1   0 142.73 89.00800  53.7220000  1.2010000 #> 66     1   1   0  86.18 75.46800  10.7120000 -0.6598200 #> 67     1   1   0  65.65 61.03600   4.6140000 -0.2778000 #> 68     1   1   0  55.07 48.71100   6.3588000  0.6249000 #> 69     1   1   0  37.95 38.72400  -0.7737400 -0.0320410 #> 70     1   1   0  23.25 24.40700  -1.1573000  0.3174400 #> 72     0   0   0  37.72 43.20900  -5.4889000  0.3581300 #> 73     0   0   0  19.15 44.50400 -25.3540000 -1.6325000 #> 74     0   0   0  14.41 37.73400 -23.3240000 -1.1230000 #> 75     0   0   0  11.89 30.51800 -18.6280000 -0.4161400 #> 76     0   0   0   5.48 24.35600 -18.8760000 -1.0534000 #> 77     0   0   0   4.93 19.36200 -14.4320000 -0.4265600 #> 78     0   0   0   4.01 15.37400 -11.3640000 -0.0415680 #> 79     0   0   0   1.54  9.68590  -8.1459000 -0.0933550 #> 80     0   0   0   0.60  6.10120  -5.5012000  0.0182380 #> 82     0   1   0  52.58 43.20900   9.3711000 -0.0440400 #> 83     0   1   0  57.71 44.50400  13.2060000  1.4433000 #> 84     0   1   0  36.23 37.73400  -1.5039000 -0.2411200 #> 85     0   1   0  26.13 30.51800  -4.3880000 -0.2810900 #> 86     0   1   0  18.07 24.35600  -6.2856000 -0.3939600 #> 87     0   1   0  10.83 19.36200  -8.5319000 -0.9388200 #> 88     0   1   0   5.49 12.20400  -6.7137000 -0.3228700 #> 89     0   1   0   3.05  7.68740  -4.6374000  0.4733600 #> 91     1   1   0  61.76 43.20900  18.5510000  1.9490000 #> 92     1   1   0  18.40 44.50400 -26.1040000 -2.2177000 #> 93     1   1   0   4.82 37.73400 -32.9140000 -2.3910000 #> 94     1   1   0   0.08 19.36200 -19.2820000 -0.5936900 #> 95     1   1   0   0.01 12.20400 -12.1940000  0.1593100 #> 97     0   1   0  16.63 17.28400  -0.6535600  0.0527370 #> 98     0   1   0  16.48 17.80200  -1.3216000  0.0625720 #> 99     0   1   0  11.11 15.09400  -3.9836000 -1.2067000 #> 100    0   1   0  11.98 12.20700  -0.2272100  0.5516600 #> 101    0   1   0   7.73  9.74220  -2.0122000 -0.8340600 #> 102    0   1   0   8.08  7.74470   0.3352500  0.7322900 #> 103    0   1   0   5.74  6.14970  -0.4096900 -0.1247500 #> 104    0   1   0   3.87  3.87440  -0.0043778 -0.0254460 #> 105    0   1   0   2.73  2.44050   0.2895200  0.2845800 #> 107    0   0   0  13.98 17.28400  -3.3036000 -0.2248100 #> 108    0   0   0  12.08 17.80200  -5.7216000 -0.6398200 #> 109    0   0   0   9.14 15.09400  -5.9536000 -0.6147700 #> 110    0   0   0   7.21 12.20700  -4.9972000 -0.1663700 #> 111    0   0   0   4.76  9.74220  -4.9822000 -0.3438600 #> 112    0   0   0   2.50  7.74470  -5.2447000 -0.9313700 #> 113    0   0   0   1.79  6.14970  -4.3597000 -0.6245000 #> 114    0   0   0   1.32  4.88150  -3.5615000 -0.2851000 #> 115    0   0   0   0.62  3.07500  -2.4550000  0.1267500 #> 116    0   0   0   0.16  1.93690  -1.7769000  0.1974400 #> 118    0   1   0  27.05 17.28400   9.7664000  1.3162000 #> 119    0   1   0  19.34 17.80200   1.5384000 -0.2032300 #> 120    0   1   0  13.79 15.09400  -1.3036000 -0.2773100 #> 121    0   1   0  10.04 12.20700  -2.1672000 -0.1149000 #> 122    0   1   0   7.52  9.74220  -2.2222000  0.1497800 #> 123    0   1   0   3.78  7.74470  -3.9647000 -1.2185000 #> 124    0   1   0   3.47  6.14970  -2.6797000 -0.2328100 #> 125    0   1   0   3.10  4.88150  -1.7815000  0.6045400 #> 126    0   1   0   1.01  3.07500  -2.0650000 -0.3626600 #> 127    0   1   0   0.60  1.93690  -1.3369000  0.1924700 #> 129    0   1   0   8.88 17.28400  -8.4036000 -0.7630900 #> 130    0   1   0  11.07 17.80200  -6.7316000 -0.9374600 #> 131    0   1   0  14.29 15.09400  -0.8035600  0.7729700 #> 132    0   1   0   8.91 12.20700  -3.2972000 -1.3204000 #> 133    0   1   0   9.92  9.74220   0.1777500  0.1982200 #> 134    0   1   0   8.80  7.74470   1.0553000  0.4668600 #> 135    0   1   0   6.79  6.14970   0.6403100 -0.2555600 #> 136    0   1   0   5.76  4.88150   0.8785400 -0.2871200 #> 137    0   1   0   4.00  3.07500   0.9250300 -0.5526400 #> 138    0   1   0   4.02  1.93690   2.0831000  2.1753000 #> 140    0   1   0   9.79 17.28400  -7.4936000 -0.7231900 #> 141    0   1   0  16.00 17.80200  -1.8016000 -0.2099000 #> 142    0   1   0  15.77 15.09400   0.6764400 -0.5643600 #> 143    0   1   0  17.11 12.20700   4.9028000  0.7863200 #> 144    0   1   0  16.56  9.74220   6.8178000  1.7317000 #> 145    0   1   0  11.53  7.74470   3.7853000 -0.7409200 #> 146    0   1   0  12.31  6.14970   6.1603000  1.5476000 #> 147    0   1   0   8.91  4.88150   4.0285000 -0.4824800 #> 148    0   1   0   7.08  3.07500   4.0050000  0.4530500 #> 149    0   1   0   4.96  1.93690   3.0231000  0.1648600 #> 151    0   0   0 176.18 86.41800  89.7620000  2.3031000 #> 152    0   0   0 138.65 89.00800  49.6420000  0.2759400 #> 153    0   0   0 112.75 75.46800  37.2820000  0.2054100 #> 154    0   0   0  93.39 61.03600  32.3540000  0.3705000 #> 155    0   0   0  86.78 48.71100  38.0690000  1.7808000 #> 156    0   0   0  66.81 38.72400  28.0860000  0.9480500 #> 157    0   0   0  46.65 30.74800  15.9020000 -0.8026900 #> 158    0   0   0  45.43 24.40700  21.0230000  0.8812200 #> 159    0   0   0  31.86 15.37500  16.4850000  1.1844000 #> 160    0   0   0  16.42  9.68460   6.7354000 -1.2682000 #> 162    0   1   0  56.35 43.20900  13.1410000  0.8167300 #> 163    0   1   0  47.18 44.50400   2.6760000 -0.4904200 #> 164    0   1   0  44.91 37.73400   7.1761000  0.4632300 #> 165    0   1   0  41.94 30.51800  11.4220000  1.5409000 #> 166    0   1   0  29.04 24.35600   4.6844000 -0.1086600 #> 167    0   1   0  17.36 19.36200  -2.0019000 -2.4648000 #> 168    0   1   0  22.15 15.37400   6.7758000  0.6181100 #> 169    0   1   0  18.05 12.20400   5.8463000  0.3571800 #> 170    0   1   0  13.64  7.68740   5.9526000  0.9791300 #> 171    0   1   0  10.16  4.84230   5.3177000  1.5056000 #> 173    0   1   0  56.37 43.20900  13.1610000  0.9526300 #> 174    0   1   0  47.02 44.50400   2.5160000 -0.9587600 #> 175    0   1   0  58.38 37.73400  20.6460000  2.0735000 #> 176    0   1   0  36.77 30.51800   6.2520000 -1.1225000 #> 177    0   1   0  38.95 24.35600  14.5940000  0.7999500 #> 178    0   1   0  28.99 19.36200   9.6281000 -0.7622200 #> 179    0   1   0  32.83 15.37400  17.4560000  2.4129000 #> 180    0   1   0  24.78 12.20400  12.5760000  0.8336600 #> 181    0   1   0  16.52  7.68740   8.8326000 -0.1377700 #> 182    0   1   0  12.04  4.84230   7.1977000  0.0893640 #> 184    0   1   0  10.88 17.28400  -6.4036000 -0.3056700 #> 185    0   1   0  11.89 17.80200  -5.9116000 -0.9129400 #> 186    0   1   0   9.42 15.09400  -5.6736000 -1.8347000 #> 187    0   1   0  13.67 12.20700   1.4628000  1.2351000 #> 188    0   1   0  10.97  9.74220   1.2278000  0.8110100 #> 189    0   1   0   9.21  7.74470   1.4653000  0.8004800 #> 190    0   1   0   6.33  6.14970   0.1803100 -0.6032500 #> 191    0   1   0   4.95  3.87440   1.0756000  0.2100400 #> 192    0   1   0   2.85  2.44050   0.4095200 -0.8834200 #> 194    0   1   0   2.23  8.64180  -6.4118000 -1.3363000 #> 195    0   1   0   3.53  7.54680  -4.0168000 -1.5215000 #> 196    0   1   0   4.13  6.10360  -1.9736000 -0.3549100 #> 197    0   1   0   3.84  4.87110  -1.0311000  0.1707500 #> 198    0   1   0   2.90  3.87240  -0.9723700 -0.3125800 #> 199    0   1   0   2.61  3.07480  -0.4648500  0.1279000 #> 200    0   1   0   2.50  2.44070   0.0592690  0.9663800 #> 201    0   1   0   1.43  1.53750  -0.1074800  0.0325670 #> 202    0   1   0   0.66  0.96846  -0.3084600 -1.3870000 #> 204    0   1   0  19.76 30.24600 -10.4860000 -0.7882200 #> 205    0   1   0  23.99 31.15300  -7.1628000  0.1410700 #> 206    0   1   0  15.86 26.41400 -10.5540000 -0.7553300 #> 207    0   1   0  12.01 21.36300  -9.3526000 -0.5786700 #> 208    0   1   0   7.70 17.04900  -9.3489000 -0.8464900 #> 209    0   1   0   6.29 13.55300  -7.2633000 -0.2496300 #> 210    0   1   0   4.11 10.76200  -6.6520000 -0.2844400 #> 212    0   1   0  15.43 30.24600 -14.8160000 -0.5491600 #> 213    0   1   0  17.58 31.15300 -13.5730000 -0.6875500 #> 214    0   1   0  17.85 26.41400  -8.5637000 -0.6179800 #> 215    0   1   0  17.46 21.36300  -3.9026000 -0.5683400 #> 216    0   1   0  14.53 17.04900  -2.5189000 -1.4462000 #> 217    0   1   0  17.52 13.55300   3.9667000  0.2799700 #> 218    0   1   0  15.93 10.76200   5.1680000  0.1594100 #> 219    0   1   0  15.72  8.54260   7.1774000  0.9870900 #> 220    0   1   0  12.62  5.38120   7.2388000  1.0645000 #> 221    0   1   0  10.71  3.38960   7.3204000  2.3721000 #> 223    0   0   0  15.35 30.24600 -14.8960000 -0.3807800 #> 224    0   0   0  16.73 31.15300 -14.4230000 -1.9738000 #> 225    0   0   0  29.66 26.41400   3.2463000  0.7666200 #> 226    0   0   0  29.59 21.36300   8.2274000  1.2045000 #> 227    0   0   0  24.34 17.04900   7.2911000  0.0760030 #> 228    0   0   0  19.81 13.55300   6.2567000 -1.0712000 #> 229    0   0   0  25.61 10.76200  14.8480000  3.3746000 #> 230    0   0   0  12.90  8.54260   4.3574000 -3.2514000 #> 231    0   0   0  14.45  5.38120   9.0688000  0.8283500 #> 232    0   0   0  12.65  3.38960   9.2604000  3.2901000 #> 234    0   1   0  28.33 30.24600  -1.9162000  0.0596700 #> 235    0   1   0  23.91 31.15300  -7.2428000 -0.8256700 #> 236    0   1   0  22.45 26.41400  -3.9637000  0.1329300 #> 237    0   1   0  15.55 21.36300  -5.8126000 -0.3655400 #> 238    0   1   0  12.41 17.04900  -4.6389000  0.0159090 #> 239    0   1   0   8.77 13.55300  -4.7833000 -0.1556000 #> 240    0   1   0   5.83 10.76200  -4.9320000 -0.4580100 #> 241    0   1   0   4.05  8.54260  -4.4926000 -0.4987500 #> 242    0   1   0   3.38  6.78020  -3.4002000 -0.0191180 #> 243    0   1   0   2.26  5.38120  -3.1212000 -0.1289100 #> 245    0   0   0   8.83 30.24600 -21.4160000 -1.0485000 #> 246    0   0   0  16.84 31.15300 -14.3130000 -1.0352000 #> 247    0   0   0  25.07 26.41400  -1.3437000  0.4787100 #> 248    0   0   0  19.91 21.36300  -1.4526000 -1.0219000 #> 249    0   0   0  20.68 17.04900   3.6311000 -0.4199200 #> 250    0   0   0  22.01 13.55300   8.4567000  0.9411800 #> 251    0   0   0  19.94 10.76200   9.1780000  0.9748000 #> 252    0   0   0  15.65  8.54260   7.1074000 -0.5216600 #> 253    0   0   0  14.46  5.38120   9.0788000  1.5548000 #> 254    0   0   0  10.65  3.38960   7.2604000  1.2194000 #> 256    0   0   0  40.74 43.20900  -2.4689000 -0.5567100 #> 257    0   0   0  54.32 44.50400   9.8160000  1.2218000 #> 258    0   0   0  37.44 37.73400  -0.2938900 -0.3753300 #> 259    0   0   0  29.80 30.51800  -0.7180200 -0.2547000 #> 260    0   0   0  26.36 24.35600   2.0044000  0.8036100 #> 261    0   0   0  17.04 19.36200  -2.3219000 -0.1913100 #> 262    0   0   0  13.14 15.37400  -2.2342000  0.0482940 #> 263    0   0   0   7.92 12.20400  -4.2837000 -0.7763400 #> 264    0   0   0   4.21  7.68740  -3.4774000 -0.4802900 #> 265    0   0   0   2.38  4.84230  -2.4623000 -0.0035721 #> 267    0   1   0  84.19 86.41800  -2.2278000 -0.0087724 #> 268    0   1   0  86.20 89.00800  -2.8079000 -0.0150950 #> 269    0   1   0  74.03 75.46800  -1.4378000  0.0250920 #> 270    0   1   0  58.20 61.03600  -2.8360000 -0.2802300 #> 271    0   1   0  47.00 48.71100  -1.7112000 -0.3401600 #> 272    0   1   0  40.57 38.72400   1.8463000  0.0539970 #> 273    0   1   0  31.51 30.74800   0.7615500 -0.2643100 #> 274    0   1   0  30.41 24.40700   6.0027000  0.8814800 #> 275    0   1   0  20.17 15.37500   4.7952000  0.7985900 #> 276    0   1   0  10.19  9.68460   0.5054500 -0.7755900 #> 278    1   1   0  22.06 86.41800 -64.3580000 -1.4038000 #> 279    1   1   0  40.41 75.46800 -35.0580000 -0.7718800 #> 280    1   1   0  34.71 61.03600 -26.3260000 -0.7881300 #> 281    1   1   0  26.09 48.71100 -22.6210000 -1.1968000 #> 282    1   1   0  28.70 38.72400 -10.0240000  0.0137490 #> 283    1   1   0  30.07 30.74800  -0.6784500  1.2974000 #> 284    1   1   0  19.96 24.40700  -4.4473000  0.0819480 #> 285    1   1   0  10.01 15.37500  -5.3648000 -1.1756000 #> 286    1   1   0  10.61  9.68460   0.9254500  0.6298800 #> 288    1   1   0  57.81 86.41800 -28.6080000 -1.0171000 #> 289    1   1   0 103.55 89.00800  14.5420000  1.5037000 #> 290    1   1   0  76.00 75.46800   0.5322100 -0.5235000 #> 291    1   1   0  72.99 61.03600  11.9540000 -0.0428260 #> 292    1   1   0  56.53 48.71100   7.8188000 -1.1581000 #> 293    1   1   0  60.08 38.72400  21.3560000  0.5892500 #> 294    1   1   0  57.04 30.74800  26.2920000  1.5879000 #> 295    1   1   0  37.89 24.40700  13.4830000 -1.1351000 #> 296    1   1   0  38.80 15.37500  23.4250000  2.4769000 #> 297    1   1   0  23.21  9.68460  13.5250000  0.1597400 #> 299    0   0   0  13.34 17.28400  -3.9436000 -0.5981800 #> 300    0   0   0  17.91 17.80200   0.1084200  0.3275900 #> 301    0   0   0  17.23 15.09400   2.1364000  0.7439700 #> 302    0   0   0  11.74 12.20700  -0.4672100 -1.0323000 #> 303    0   0   0  10.35  9.74220   0.6077500 -0.7557100 #> 304    0   0   0  10.74  7.74470   2.9953000  0.9828100 #> 305    0   0   0   9.40  6.14970   3.2503000  1.4048000 #> 306    0   0   0   6.42  4.88150   1.5385000 -0.3303800 #> 307    0   0   0   4.26  3.07500   1.1850000 -0.6478000 #> 308    0   0   0   3.56  1.93690   1.6231000  0.8438100 #> 310    0   1   0  17.32 30.24600 -12.9260000 -0.5028900 #> 311    0   1   0  17.54 31.15300 -13.6130000 -0.7219100 #> 312    0   1   0  12.74 26.41400 -13.6740000 -1.4278000 #> 313    0   1   0  14.73 21.36300  -6.6326000 -0.1076800 #> 314    0   1   0  13.02 17.04900  -4.0289000  0.1655500 #> 315    0   1   0   9.25 13.55300  -4.3033000 -0.6620100 #> 316    0   1   0   7.85 10.76200  -2.9120000 -0.6499700 #> 317    0   1   0   8.40  8.54260  -0.1425600  0.5523300 #> 318    0   1   0   5.16  5.38120  -0.2212000 -0.2945200 #> 319    0   1   0   5.04  3.38960   1.6504000  1.5090000 #> 321    0   0   0 105.94 86.41800  19.5220000  0.2722700 #> 322    0   0   0 120.44 89.00800  31.4320000  0.9209500 #> 323    0   0   0 104.12 75.46800  28.6520000  0.5226400 #> 324    0   0   0  91.11 61.03600  30.0740000  0.5458800 #> 325    0   0   0  84.46 48.71100  35.7490000  1.3160000 #> 326    0   0   0  58.59 38.72400  19.8660000 -1.1113000 #> 327    0   0   0  53.96 30.74800  23.2120000 -0.4733800 #> 328    0   0   0  43.25 24.40700  18.8430000 -1.2681000 #> 329    0   0   0  46.03 15.37500  30.6550000  3.4980000 #> 330    0   0   0  29.78  9.68460  20.0950000  1.8245000 #> 332    0   1   0   8.48  8.90080  -0.4207900  0.3139300 #> 333    0   1   0   5.39  7.54680  -2.1568000 -0.6127600 #> 334    0   1   0   3.09  6.10360  -3.0136000 -1.4096000 #> 335    0   1   0   3.46  4.87110  -1.4111000  0.6260700 #> 336    0   1   0   1.79  3.87240  -2.0824000 -0.4989700 #> 337    0   1   0   1.08  3.07480  -1.9948000 -0.6730500 #> 338    0   1   0   0.59  1.93720  -1.3472000  0.0034753 #> 339    0   1   0   0.33  1.53750  -1.2075000 -0.0664710 #> 341    0   1   0  27.99 43.20900 -15.2190000 -0.9792600 #> 342    0   1   0  42.82 44.50400  -1.6840000  0.8669000 #> 343    0   1   0  25.66 37.73400 -12.0740000 -1.2151000 #> 344    0   1   0  24.67 30.51800  -5.8480000 -0.1331500 #> 345    0   1   0  18.75 24.35600  -5.6056000 -0.1523800 #> 346    0   1   0  14.15 19.36200  -5.2119000 -0.1195700 #> 347    0   1   0  11.46 15.37400  -3.9142000  0.2751800 #> 348    0   1   0   2.88  7.68740  -4.8074000 -0.8732800 #> 350    1   1   0 134.89 86.41800  48.4720000  1.0059000 #> 351    1   1   0 107.21 89.00800  18.2020000  0.7409900 #> 352    1   1   0  63.25 75.46800 -12.2180000 -0.4192800 #> 353    1   1   0  38.67 61.03600 -22.3660000 -0.7827000 #> 354    1   1   0  26.35 48.71100 -22.3610000 -0.5380100 #> 355    1   1   0  14.12 38.72400 -24.6040000 -0.9451500 #> 356    1   1   0  11.74 30.74800 -19.0080000 -0.1392400 #> 357    1   1   0   6.08 24.40700 -18.3270000 -0.3232800 #> 358    1   1   0   2.10 15.37500 -13.2750000  0.1292200 #> 359    1   1   0   0.89  9.68460  -8.7946000  0.7481900 #> 361    1   1   0  23.32 43.20900 -19.8890000 -0.4608500 #> 362    1   1   0  25.76 44.50400 -18.7440000 -1.5352000 #> 363    1   1   0  36.97 37.73400  -0.7638900  0.3634600 #> 364    1   1   0  22.21 12.20400  10.0060000  1.7239000 #> 365    1   1   0   8.49  4.84230   3.6477000 -1.1486000 #> 367    0   1   0  70.92 43.20900  27.7110000  1.5130000 #> 368    0   1   0  47.75 44.50400   3.2460000 -0.5589000 #> 369    0   1   0  40.87 37.73400   3.1361000  0.7627200 #> 370    0   1   0  21.24 30.51800  -9.2780000 -1.1218000 #> 371    0   1   0  12.55 19.36200  -6.8119000 -0.0939310 #> 373    0   1   0   6.32  8.64180  -2.3218000 -0.4219400 #> 374    0   1   0   7.29  8.90080  -1.6108000 -0.0486050 #> 375    0   1   0   5.58  7.54680  -1.9668000 -0.8715900 #> 376    0   1   0   5.72  6.10360  -0.3836000  0.2607500 #> 377    0   1   0   3.81  4.87110  -1.0611000 -1.1438000 #> 378    0   1   0   3.48  3.07480   0.4051500  0.4334100 #> 379    0   1   0   3.17  2.44070   0.7292700  1.0190000 #> 380    0   1   0   1.93  1.53750   0.3925200 -0.0340300 #> 382    0   1   0  33.86 30.24600   3.6138000  0.9143500 #> 383    0   1   0  18.36 31.15300 -12.7930000 -1.8667000 #> 384    0   1   0  19.47 26.41400  -6.9437000 -0.2195900 #> 385    0   1   0  15.85 21.36300  -5.5126000  0.1283900 #> 386    0   1   0  10.68 17.04900  -6.3689000 -0.5156900 #> 387    0   1   0   7.84 13.55300  -5.7133000 -0.7397400 #> 388    0   1   0   7.81 10.76200  -2.9520000  0.2422500 #> 389    0   1   0   5.84  8.54260  -2.7026000 -0.0206860 #> 390    0   1   0   4.60  5.38120  -0.7812000  0.8291700 #> 391    0   1   0   1.99  3.38960  -1.3996000 -0.5577000 #> 393    0   1   0  45.40 30.24600  15.1540000  1.1901000 #> 394    0   1   0  44.07 31.15300  12.9170000  0.1748600 #> 395    0   1   0  37.28 26.41400  10.8660000 -0.5761700 #> 396    0   1   0  41.31 21.36300  19.9470000  2.2912000 #> 397    0   1   0  37.36 17.04900  20.3110000  3.0956000 #> 398    0   1   0  24.23 13.55300  10.6770000 -0.5784000 #> 399    0   1   0  22.39 10.76200  11.6280000  0.4265800 #> 400    0   1   0  15.46  8.54260   6.9174000 -2.0041000 #> 401    0   1   0  15.16  5.38120   9.7788000  2.0242000 #> 402    0   1   0   9.55  3.38960   6.1604000  0.5050800 #> 404    0   1   0 110.98 86.41800  24.5620000  0.8377100 #> 405    0   1   0  72.34 89.00800 -16.6680000 -0.1691800 #> 406    0   1   0  30.91 75.46800 -44.5580000 -1.4801000 #> 407    0   1   0  14.00 61.03600 -47.0360000 -1.5545000 #> 408    0   1   0   3.13 38.72400 -35.5940000 -0.7535500 #> 409    0   1   0   0.67 24.40700 -23.7370000  0.2648300 #> 411    0   1   0   4.69  8.64180  -3.9518000 -0.7363700 #> 412    0   1   0   5.80  8.90080  -3.1008000 -0.6896900 #> 413    0   1   0   5.90  7.54680  -1.6468000 -0.2073600 #> 414    0   1   0   5.23  6.10360  -0.8736000 -0.0407330 #> 415    0   1   0   4.68  4.87110  -0.1911200  0.3268300 #> 416    0   1   0   3.56  3.87240  -0.3123700 -0.3100800 #> 417    0   1   0   2.48  3.07480  -0.5948500 -1.3559000 #> 418    0   1   0   2.50  2.44070   0.0592690 -0.3571200 #> 419    0   1   0   2.14  1.53750   0.6025200  0.8950800 #> 420    0   1   0   1.67  0.96846   0.7015400  1.6255000 #> 422    0   0   0  28.90 30.24600  -1.3462000  0.0715470 #> 423    0   0   0  21.08 31.15300 -10.0730000 -0.6122900 #> 424    0   0   0  15.09 26.41400 -11.3240000 -0.3348000 #> 425    0   0   0   8.77 21.36300 -12.5930000 -0.6169300 #> 426    0   0   0   3.41 17.04900 -13.6390000 -1.3182000 #> 427    0   0   0   1.84 13.55300 -11.7130000 -1.0482000 #> 428    0   0   0   1.09 10.76200  -9.6720000 -0.6426900 #> 429    0   0   0   0.48  8.54260  -8.0626000 -0.3625300 #> 430    0   0   0   0.14  5.38120  -5.2412000  0.3663000 #> 431    0   0   0   0.03  3.38960  -3.3596000  0.9289900 #> 433    0   0   0  53.00 43.20900   9.7911000  0.4838700 #> 434    0   0   0  17.98 30.51800 -12.5380000 -0.9709500 #> 435    0   0   0   2.75 12.20400  -9.4537000 -0.6910300 #> 436    0   0   0   0.67  6.10120  -5.4312000  0.1809200 #> 438    0   1   0 161.86 86.41800  75.4420000  1.3232000 #> 439    0   1   0 157.57 89.00800  68.5620000  1.6001000 #> 440    0   1   0 116.89 75.46800  41.4220000  0.4487300 #> 441    0   1   0 102.18 61.03600  41.1440000  1.5771000 #> 442    0   1   0  69.69 48.71100  20.9790000  0.0291150 #> 443    0   1   0  48.29 38.72400   9.5663000 -1.0206000 #> 444    0   1   0  47.60 30.74800  16.8520000  1.0753000 #> 445    0   1   0  38.98 24.40700  14.5730000  1.4718000 #> 446    0   1   0  21.22 19.37200   1.8481000 -1.2219000 #> 447    0   1   0  14.56 12.20200   2.3576000 -0.2632600 #> 449    1   1   0  73.72 86.41800 -12.6980000 -0.1209400 #> 450    1   1   0  62.77 61.03600   1.7340000 -0.1419800 #> 451    1   1   0  52.28 48.71100   3.5688000 -0.7044700 #> 452    1   1   0  41.83 24.40700  17.4230000  0.1947900 #> 453    1   1   0  30.84  9.68460  21.1550000  2.9455000 #> 455    0   1   0 119.97 43.20900  76.7610000  4.9655000 #> 456    0   1   0  55.31 44.50400  10.8060000 -2.7221000 #> 457    0   1   0  52.02 37.73400  14.2860000 -0.3673400 #> 458    0   1   0  49.41 30.51800  18.8920000  1.7875000 #> 459    0   1   0  40.56 24.35600  16.2040000  2.0447000 #> 460    0   1   0  33.93 19.36200  14.5680000  2.3457000 #> 461    0   1   0  23.22 15.37400   7.8458000  0.4696400 #> 462    0   1   0  15.22 12.20400   3.0163000 -1.3597000 #> 463    0   1   0  12.25  9.68590   2.5641000 -1.4216000 #> 464    0   1   0  13.64  7.68740   5.9526000  1.0879000 #> 465    0   1   0   7.72  6.10120   1.6188000 -1.6642000 #> 466    0   1   0   9.18  4.84230   4.3377000  1.1272000 #> 468    0   1   0  19.09 43.20900 -24.1190000 -0.9170000 #> 469    0   1   0  26.46 44.50400 -18.0440000 -0.4975300 #> 470    0   1   0  24.47 37.73400 -13.2640000 -0.5911800 #> 471    0   1   0  20.43 30.51800 -10.0880000 -0.8948400 #> 472    0   1   0  24.08 24.35600  -0.2756200  0.8406500 #> 473    0   1   0  13.59 19.36200  -5.7719000 -1.6002000 #> 474    0   1   0  16.84 15.37400   1.4658000  0.4026500 #> 475    0   1   0  12.95 12.20400   0.7463500 -0.3675000 #> 476    0   1   0  10.71  7.68740   3.0226000  0.3477300 #> 477    0   1   0   9.75  4.84230   4.9077000  2.1237000 #> 479    0   1   0  65.72 43.20900  22.5110000  1.2341000 #> 480    0   1   0  42.13 44.50400  -2.3740000  0.0473630 #> 481    0   1   0  20.42 37.73400 -17.3140000 -1.0224000 #> 482    0   1   0  11.54 30.51800 -18.9780000 -0.8857400 #> 483    0   1   0   4.39 24.35600 -19.9660000 -1.2817000 #> 484    0   1   0   1.93 19.36200 -17.4320000 -1.0146000 #> 485    0   1   0   1.00 15.37400 -14.3740000 -0.5565500 #> 486    0   1   0   0.36 12.20400 -11.8440000 -0.1967700 #> 487    0   1   0   0.08  7.68740  -7.6074000  0.5830400 #> 488    0   1   0   0.02  4.84230  -4.8223000  1.1530000 #> 490    1   1   0  42.61 43.20900  -0.5988900  0.0577150 #> 491    1   1   0  47.68 44.50400   3.1760000 -0.1116400 #> 492    1   1   0  47.65 37.73400   9.9161000  0.4419800 #> 493    1   1   0  44.96 30.51800  14.4420000  1.0462000 #> 494    1   1   0  36.56 24.35600  12.2040000  0.2868600 #> 495    1   1   0  32.37 19.36200  13.0080000  0.4651900 #> 496    1   1   0  19.77 12.20400   7.5663000 -1.6853000 #> 497    1   1   0  17.05  7.68740   9.3626000 -0.0898180 #> 498    1   1   0  16.78  4.84230  11.9380000  4.0157000 #> 500    0   1   0  27.81 43.20900 -15.3990000 -0.7768300 #> 501    0   1   0  44.56 44.50400   0.0560410  1.0472000 #> 502    0   1   0  27.71 37.73400 -10.0240000 -1.6992000 #> 503    0   1   0  35.69 30.51800   5.1720000  0.5004700 #> 504    0   1   0  32.64 24.35600   8.2844000  0.6479400 #> 505    0   1   0  20.07 19.36200   0.7081300 -2.4367000 #> 506    0   1   0  26.51 15.37400  11.1360000  0.9104500 #> 507    0   1   0  24.71 12.20400  12.5060000  1.5632000 #> 508    0   1   0  18.05  7.68740  10.3630000  1.0647000 #> 509    0   1   0  13.82  4.84230   8.9777000  1.3942000 #> 511    0   1   0  14.36  8.64180   5.7182000  0.8826800 #> 512    0   1   0  14.72  8.90080   5.8192000  1.9432000 #> 513    0   1   0   9.02  7.54680   1.4732000 -0.7363200 #> 514    0   1   0   7.60  6.10360   1.4964000  0.0942140 #> 515    0   1   0   6.45  4.87110   1.5789000  1.0280000 #> 516    0   1   0   4.86  3.87240   0.9876300  0.9027700 #> 517    0   1   0   3.23  3.07480   0.1551500 -0.0544550 #> 518    0   1   0   2.11  2.44070  -0.3307300 -0.7851500 #> 519    0   1   0   1.16  1.53750  -0.3774800 -0.5854900 #> 520    0   1   0   0.70  0.96846  -0.2684600 -0.0266090 #> 522    0   1   0  15.00 43.20900 -28.2090000 -1.0821000 #> 523    0   1   0  20.61 44.50400 -23.8940000 -0.9548000 #> 524    0   1   0  18.40 37.73400 -19.3340000 -0.9368400 #> 525    0   1   0  16.00 30.51800 -14.5180000 -0.5804400 #> 526    0   1   0  15.30 24.35600  -9.0556000  0.3365000 #> 527    0   1   0   9.47 19.36200  -9.8919000 -0.3709900 #> 528    0   1   0   7.66 15.37400  -7.7142000 -0.0645240 #> 529    0   1   0   4.37 12.20400  -7.8337000 -0.6603100 #> 530    0   1   0   2.90  7.68740  -4.7874000 -0.0700970 #> 531    0   1   0   0.91  4.84230  -3.9323000 -0.5516600 #> 533    0   1   0  98.31 86.41800  11.8920000  0.2379000 #> 534    0   1   0  80.18 89.00800  -8.8279000  0.0857670 #> 535    0   1   0  54.06 75.46800 -21.4080000 -0.0943810 #> 536    0   1   0  27.03 61.03600 -34.0060000 -1.1052000 #> 537    0   1   0  14.27 48.71100 -34.4410000 -1.3095000 #> 538    0   1   0  12.56 38.72400 -26.1640000 -0.3468900 #> 539    0   1   0   4.39 30.74800 -26.3580000 -0.8313800 #> 540    0   1   0   2.05 19.37200 -17.3220000  0.1240400 #> 541    0   1   0   0.65 12.20200 -11.5520000  0.7394800 #> 543    0   1   0  14.96  8.64180   6.3182000  1.6118000 #> 544    0   1   0   8.72  6.10360   2.6164000 -0.0301420 #> 545    0   1   0   8.66  4.87110   3.7889000  1.8568000 #> 546    0   1   0   4.38  2.44070   1.9393000  0.0263380 #> 547    0   1   0   3.03  1.53750   1.4925000 -0.1252700 #> 548    0   1   0   2.43  0.96846   1.4615000  1.3828000 #> 550    0   1   0   5.99 43.20900 -37.2190000 -1.2801000 #> 551    0   1   0  13.26 44.50400 -31.2440000 -1.0617000 #> 552    0   1   0  13.19 37.73400 -24.5440000 -1.3239000 #> 553    0   1   0  14.15 30.51800 -16.3680000 -1.0914000 #> 554    0   1   0  16.30 24.35600  -8.0556000 -0.2757500 #> 555    0   1   0  14.39 19.36200  -4.9719000 -0.4439900 #> 556    0   1   0  12.63 15.37400  -2.7442000 -0.6194200 #> 557    0   1   0  14.82 12.20400   2.6163000  1.0388000 #> 558    0   1   0  13.44  9.68590   3.7541000  1.2499000 #> 559    0   1   0  10.27  7.68740   2.5826000  0.2346500 #> 561    0   1   0  25.91 30.24600  -4.3362000 -0.3109500 #> 562    0   1   0  13.00 21.36300  -8.3626000 -0.4001300 #> 563    0   1   0   8.31 17.04900  -8.7389000 -0.7156300 #> 564    0   1   0   4.54 13.55300  -9.0133000 -1.2258000 #> 565    0   1   0   4.87 10.76200  -5.8920000 -0.0065673 #> 566    0   1   0   2.93  8.54260  -5.6126000 -0.2676700 #> 567    0   1   0   1.90  5.38120  -3.4812000  0.4590200 #> 569    0   1   0  36.19 43.20900  -7.0189000  0.0874200 #> 570    0   1   0  40.04 44.50400  -4.4640000 -0.4862900 #> 571    0   1   0  35.84 37.73400  -1.8939000 -1.2469000 #> 572    0   1   0  48.96 30.51800  18.4420000  2.1969000 #> 573    0   1   0  30.71 19.36200  11.3480000 -0.6482500 #> 574    0   1   0  30.07 15.37400  14.6960000  0.3818100 #> 575    0   1   0  29.57 12.20400  17.3660000  1.8318000 #> 576    0   1   0  15.60  4.84230  10.7580000  0.8741700 #> 578    0   1   0  73.05 43.20900  29.8410000  1.6030000 #> 579    0   1   0  51.52 44.50400   7.0160000 -0.1923400 #> 580    0   1   0  39.96 37.73400   2.2261000  0.2353700 #> 581    0   1   0  23.43 30.51800  -7.0880000 -1.1412000 #> 582    0   1   0  22.59 24.35600  -1.7656000  0.4611200 #> 583    0   1   0  14.64 19.36200  -4.7219000 -0.3316100 #> 584    0   1   0  12.32 15.37400  -3.0542000  0.2655800 #> 585    0   1   0   9.58 12.20400  -2.6237000  0.4182900 #> 586    0   1   0   4.23  7.68740  -3.4574000 -0.4033600 #> 587    0   1   0   2.48  4.84230  -2.3623000 -0.1445200 #> 589    0   1   0  10.61  8.64180   1.9682000  0.5627700 #> 590    0   1   0   8.08  8.90080  -0.8207900 -0.2251000 #> 591    0   1   0   3.32  6.10360  -2.7836000 -1.0066000 #> 592    0   1   0   2.72  4.87110  -2.1511000 -0.2507500 #> 593    0   1   0   1.49  3.87240  -2.3824000 -0.8504700 #> 594    0   1   0   1.26  3.07480  -1.8148000 -0.1793900 #> 595    0   1   0   0.89  2.44070  -1.5507000 -0.0099798 #> 596    0   1   0   0.49  1.93720  -1.4472000 -0.2413300 #> 597    0   1   0   0.38  1.53750  -1.1575000  0.0962790 #> 598    0   1   0   0.25  1.22020  -0.9702400  0.2072600 #> 600    0   1   0  50.88 43.20900   7.6711000  0.4675800 #> 601    0   1   0  50.86 44.50400   6.3560000  0.2393600 #> 602    0   1   0  41.68 37.73400   3.9461000 -0.3567800 #> 603    0   1   0  38.77 30.51800   8.2520000  0.3178200 #> 604    0   1   0  35.61 24.35600  11.2540000  1.0120000 #> 605    0   1   0  24.90 19.36200   5.5381000 -0.8488200 #> 606    0   1   0  24.82 15.37400   9.4458000  0.5769800 #> 607    0   1   0  20.89 12.20400   8.6863000  0.4577500 #> 608    0   1   0  15.97  7.68740   8.2826000  1.0815000 #> 610    0   1   0  39.62 30.24600   9.3738000  0.7999400 #> 611    0   1   0  34.31 31.15300   3.1572000 -0.3034300 #> 612    0   1   0  25.90 26.41400  -0.5137300 -0.9329500 #> 613    0   1   0  29.15 21.36300   7.7874000  2.0214000 #> 614    0   1   0  18.82 17.04900   1.7711000  0.3739400 #> 615    0   1   0  10.61 10.76200  -0.1519600  0.0408840 #> 616    0   1   0   7.07  8.54260  -1.4726000 -0.6580500 #> 617    0   1   0   4.50  5.38120  -0.8812000 -0.0747730 #> 618    0   1   0   2.73  4.27080  -1.5408000 -0.7953800 #> 620    0   1   0  26.15 43.20900 -17.0590000 -0.3798200 #> 621    0   1   0  27.49 44.50400 -17.0140000 -1.1552000 #> 622    0   1   0  27.17 37.73400 -10.5640000 -0.9538400 #> 623    0   1   0  30.42 30.51800  -0.0980230  0.6314000 #> 624    0   1   0  24.82 24.35600   0.4643800  0.4346800 #> 625    0   1   0  20.71 19.36200   1.3481000  0.4559700 #> 626    0   1   0  10.35 12.20400  -1.8537000 -1.3981000 #> 627    0   1   0  14.18  9.68590   4.4941000  1.8678000 #> 628    0   1   0   7.30  6.10120   1.1988000 -0.0470870 #> 629    0   1   0   4.51  4.84230  -0.3322800 -1.4289000 #> 631    0   1   0  19.94 43.20900 -23.2690000 -1.0476000 #> 632    0   1   0  33.94 44.50400 -10.5640000  0.4024500 #> 633    0   1   0  30.16 37.73400  -7.5739000 -0.1806400 #> 634    0   1   0  23.47 30.51800  -7.0480000 -1.4358000 #> 635    0   1   0  27.24 24.35600   2.8844000 -0.1906500 #> 636    0   1   0  25.67 19.36200   6.3081000 -0.1221800 #> 637    0   1   0  21.15 15.37400   5.7758000 -1.1615000 #> 638    0   1   0  20.40  9.68590  10.7140000  0.3550800 #> 639    0   1   0  18.13  7.68740  10.4430000  0.2946700 #> 640    0   1   0  20.18  4.84230  15.3380000  5.9987000 str(simpraz.xpdb) #> Formal class 'xpose.data' [package \".GlobalEnv\"] with 8 slots #>   ..@ Data           :'data.frame':\t640 obs. of  26 variables: #>   .. ..$ ID   : num [1:640] 1 1 1 1 1 1 1 1 1 1 ... #>   .. ..$ TIME : num [1:640] 0 1 2 3 4 5 6 7 9 11 ... #>   .. ..$ IPRED: num [1:640] 0 69.2 80.2 75.3 66.9 ... #>   .. ..$ IWRES: num [1:640] 0 0.0368 -0.0944 0.1683 -0.206 ... #>   .. ..$ CWRES: num [1:640] 0 -0.0646 -0.9411 1.1911 -1.5154 ... #>   .. ..$ CL   : num [1:640] 13.6 13.6 13.6 13.6 13.6 ... #>   .. ..$ V    : num [1:640] 93.6 93.6 93.6 93.6 93.6 ... #>   .. ..$ KA   : num [1:640] 1.22 1.22 1.22 1.22 1.22 ... #>   .. ..$ ETA1 : num [1:640] -0.268 -0.268 -0.268 -0.268 -0.268 ... #>   .. ..$ ETA2 : num [1:640] 0.198 0.198 0.198 0.198 0.198 ... #>   .. ..$ ETA3 : num [1:640] -0.164 -0.164 -0.164 -0.164 -0.164 ... #>   .. ..$ AGE  : num [1:640] 55 55 55 55 55 55 55 55 55 55 ... #>   .. ..$ HT   : num [1:640] 154 154 154 154 154 154 154 154 154 154 ... #>   .. ..$ WT   : num [1:640] 81 81 81 81 81 ... #>   .. ..$ SECR : num [1:640] 1 1 1 1 1 1 1 1 1 1 ... #>   .. ..$ SEX  : Factor w/ 2 levels \"1\",\"2\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ RACE : Factor w/ 3 levels \"1\",\"2\",\"3\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ SMOK : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ... #>   .. ..$ HCTZ : Factor w/ 2 levels \"0\",\"1\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ PROP : Factor w/ 2 levels \"0\",\"1\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ CON  : Factor w/ 2 levels \"0\",\"1\": 2 2 2 2 2 2 2 2 2 2 ... #>   .. ..$ OCC  : Factor w/ 1 level \"0\": 1 1 1 1 1 1 1 1 1 1 ... #>   .. ..$ DV   : num [1:640] 0 71.7 72.6 88 53.1 ... #>   .. ..$ PRED : num [1:640] 0 86.4 89 75.5 61 ... #>   .. ..$ RES  : num [1:640] 0 -14.68 -16.4 12.54 -7.91 ... #>   .. ..$ WRES : num [1:640] 0 -0.105 -0.759 1.208 -1.539 ... #>   ..@ SData          : NULL #>   ..@ Data.firstonly :'data.frame':\t64 obs. of  12 variables: #>   .. ..$ SUBJECT_NO: int [1:64] 1 2 3 4 5 6 7 8 9 10 ... #>   .. ..$ ID        : int [1:64] 1 2 3 4 5 6 7 8 9 10 ... #>   .. ..$ ETA.1.    : num [1:64] -0.2677 -0.7097 -0.4762 0.0996 -0.3529 ... #>   .. ..$ ETA.2.    : num [1:64] 0.198 0.186 0.202 -0.429 0.098 ... #>   .. ..$ ETA.3.    : num [1:64] -0.164 0.737 0.436 0.151 0.524 ... #>   .. ..$ ETC.1.1.  : num [1:64] 0.00412 0.00646 0.00401 0.00321 0.00328 ... #>   .. ..$ ETC.2.1.  : num [1:64] -0.002413 -0.002923 -0.001677 0.003449 -0.000594 ... #>   .. ..$ ETC.2.2.  : num [1:64] 0.00971 0.00622 0.00661 0.00605 0.00691 ... #>   .. ..$ ETC.3.1.  : num [1:64] -0.00947 -0.01828 -0.01274 -0.00658 -0.01295 ... #>   .. ..$ ETC.3.2.  : num [1:64] 0.01757 0.00998 0.01247 0.00237 0.01117 ... #>   .. ..$ ETC.3.3.  : num [1:64] 0.0821 0.3497 0.1956 0.0754 0.2295 ... #>   .. ..$ OBJ       : num [1:64] 54.04 60.73 9.13 27.62 25.53 ... #>   ..@ SData.firstonly: NULL #>   ..@ Runno          : num 1 #>   ..@ Nsim           : NULL #>   ..@ Doc            : NULL #>   ..@ Prefs          :Formal class 'xpose.prefs' [package \".GlobalEnv\"] with 9 slots #>   .. .. ..@ Xvardef      :List of 14 #>   .. .. .. ..$ id        : chr \"ID\" #>   .. .. .. ..$ idlab     : chr \"ID\" #>   .. .. .. ..$ idv       : chr \"TIME\" #>   .. .. .. ..$ occ       : chr \"OCC\" #>   .. .. .. ..$ dv        : chr \"DV\" #>   .. .. .. ..$ pred      : chr \"PRED\" #>   .. .. .. ..$ ipred     : chr \"IPRED\" #>   .. .. .. ..$ iwres     : chr \"IWRES\" #>   .. .. .. ..$ wres      : chr \"WRES\" #>   .. .. .. ..$ cwres     : chr \"CWRES\" #>   .. .. .. ..$ res       : chr \"RES\" #>   .. .. .. ..$ parms     : chr [1:6] \"ETA3\" \"ETA2\" \"ETA1\" \"KA\" ... #>   .. .. .. ..$ covariates: chr [1:11] \"SEX\" \"RACE\" \"SMOK\" \"HCTZ\" ... #>   .. .. .. ..$ ranpar    : chr [1:3] \"ETA1\" \"ETA2\" \"ETA3\" #>   .. .. ..@ Labels       :List of 29 #>   .. .. .. ..$ OCC  : chr \"Occasion\" #>   .. .. .. ..$ TIME : chr \"Time\" #>   .. .. .. ..$ PRED : chr \"Population predictions\" #>   .. .. .. ..$ IPRED: chr \"Individual predictions\" #>   .. .. .. ..$ IPRE : chr \"Individual predictions\" #>   .. .. .. ..$ WRES : chr \"Weighted residuals\" #>   .. .. .. ..$ CWRES: chr \"Conditional weighted residuals\" #>   .. .. .. ..$ IWRES: chr \"Individual weighted residuals\" #>   .. .. .. ..$ IWRE : chr \"Individual weighted residuals\" #>   .. .. .. ..$ DV   : chr \"Observations\" #>   .. .. .. ..$ RES  : chr \"Residuals\" #>   .. .. .. ..$ CL   : chr \"Clearance\" #>   .. .. .. ..$ V    : chr \"Volume\" #>   .. .. .. ..$ TAD  : chr \"Time after dose\" #>   .. .. .. ..$ ID   : chr \"ID\" #>   .. .. .. ..$ KA   : chr \"KA\" #>   .. .. .. ..$ ETA1 : chr \"ETA1\" #>   .. .. .. ..$ ETA2 : chr \"ETA2\" #>   .. .. .. ..$ ETA3 : chr \"ETA3\" #>   .. .. .. ..$ AGE  : chr \"AGE\" #>   .. .. .. ..$ HT   : chr \"HT\" #>   .. .. .. ..$ WT   : chr \"WT\" #>   .. .. .. ..$ SECR : chr \"SECR\" #>   .. .. .. ..$ SEX  : chr \"SEX\" #>   .. .. .. ..$ RACE : chr \"RACE\" #>   .. .. .. ..$ SMOK : chr \"SMOK\" #>   .. .. .. ..$ HCTZ : chr \"HCTZ\" #>   .. .. .. ..$ PROP : chr \"PROP\" #>   .. .. .. ..$ CON  : chr \"CON\" #>   .. .. ..@ Graph.prefs  :List of 102 #>   .. .. .. ..$ type     : chr \"b\" #>   .. .. .. ..$ pch      : num 1 #>   .. .. .. ..$ cex      : num 0.8 #>   .. .. .. ..$ lty      : num 1 #>   .. .. .. ..$ lwd      : num 1 #>   .. .. .. ..$ col      : num 4 #>   .. .. .. ..$ fill     : chr \"lightblue\" #>   .. .. .. ..$ grid     : logi FALSE #>   .. .. .. ..$ aspect   : chr \"fill\" #>   .. .. .. ..$ condvar  : NULL #>   .. .. .. ..$ byordfun : chr \"median\" #>   .. .. .. ..$ ordby    : NULL #>   .. .. .. ..$ shingnum : num 6 #>   .. .. .. ..$ shingol  : num 0.5 #>   .. .. .. ..$ abline   : NULL #>   .. .. .. ..$ abllwd   : num 1 #>   .. .. .. ..$ ablcol   : num 1 #>   .. .. .. ..$ abllty   : num 1 #>   .. .. .. ..$ smlwd    : num 2 #>   .. .. .. ..$ smcol    : num 2 #>   .. .. .. ..$ smlty    : num 1 #>   .. .. .. ..$ smspan   : num 0.667 #>   .. .. .. ..$ smdegr   : num 1 #>   .. .. .. ..$ lmline   : NULL #>   .. .. .. ..$ lmlwd    : num 2 #>   .. .. .. ..$ lmcol    : num 2 #>   .. .. .. ..$ lmlty    : num 1 #>   .. .. .. ..$ suline   : NULL #>   .. .. .. ..$ sulwd    : num 2 #>   .. .. .. ..$ sucol    : num 3 #>   .. .. .. ..$ sulty    : num 1 #>   .. .. .. ..$ suspan   : num 0.667 #>   .. .. .. ..$ sudegr   : num 1 #>   .. .. .. ..$ ids      : logi FALSE #>   .. .. .. ..$ idsmode  : NULL #>   .. .. .. ..$ idsext   : num 0.05 #>   .. .. .. ..$ idscex   : num 0.7 #>   .. .. .. ..$ idsdir   : chr \"both\" #>   .. .. .. ..$ dilfrac  : num 0.7 #>   .. .. .. ..$ diltype  : NULL #>   .. .. .. ..$ dilci    : num 0.95 #>   .. .. .. ..$ PIuplty  : num 2 #>   .. .. .. ..$ PIdolty  : num 2 #>   .. .. .. ..$ PImelty  : num 1 #>   .. .. .. ..$ PIuptyp  : chr \"l\" #>   .. .. .. ..$ PIdotyp  : chr \"l\" #>   .. .. .. ..$ PImetyp  : chr \"l\" #>   .. .. .. ..$ PIupcol  : chr \"black\" #>   .. .. .. ..$ PIdocol  : chr \"black\" #>   .. .. .. ..$ PImecol  : chr \"black\" #>   .. .. .. ..$ PIuplwd  : num 2 #>   .. .. .. ..$ PIdolwd  : num 2 #>   .. .. .. ..$ PImelwd  : num 2 #>   .. .. .. ..$ PIupltyR : num 1 #>   .. .. .. ..$ PIdoltyR : num 1 #>   .. .. .. ..$ PImeltyR : num 2 #>   .. .. .. ..$ PIuptypR : chr \"l\" #>   .. .. .. ..$ PIdotypR : chr \"l\" #>   .. .. .. ..$ PImetypR : chr \"l\" #>   .. .. .. ..$ PIupcolR : chr \"blue\" #>   .. .. .. ..$ PIdocolR : chr \"blue\" #>   .. .. .. ..$ PImecolR : chr \"blue\" #>   .. .. .. ..$ PIuplwdR : num 2 #>   .. .. .. ..$ PIdolwdR : num 2 #>   .. .. .. ..$ PImelwdR : num 2 #>   .. .. .. ..$ PIupltyM : num 1 #>   .. .. .. ..$ PIdoltyM : num 1 #>   .. .. .. ..$ PImeltyM : num 2 #>   .. .. .. ..$ PIuptypM : chr \"l\" #>   .. .. .. ..$ PIdotypM : chr \"l\" #>   .. .. .. ..$ PImetypM : chr \"l\" #>   .. .. .. ..$ PIupcolM : chr \"darkgreen\" #>   .. .. .. ..$ PIdocolM : chr \"darkgreen\" #>   .. .. .. ..$ PImecolM : chr \"darkgreen\" #>   .. .. .. ..$ PIuplwdM : num 0.5 #>   .. .. .. ..$ PIdolwdM : num 0.5 #>   .. .. .. ..$ PImelwdM : num 0.5 #>   .. .. .. ..$ PIarcol  : chr \"lightgreen\" #>   .. .. .. ..$ PIlimits : num [1:2] 0.025 0.975 #>   .. .. .. ..$ bwhoriz  : logi FALSE #>   .. .. .. ..$ bwratio  : num 1.5 #>   .. .. .. ..$ bwvarwid : logi FALSE #>   .. .. .. ..$ bwdotpch : num 16 #>   .. .. .. ..$ bwdotcol : chr \"black\" #>   .. .. .. ..$ bwdotcex : num 1 #>   .. .. .. ..$ bwreccol : chr \"blue\" #>   .. .. .. ..$ bwrecfill: chr \"transparent\" #>   .. .. .. ..$ bwreclty : num 1 #>   .. .. .. ..$ bwreclwd : num 1 #>   .. .. .. ..$ bwumbcol : chr \"blue\" #>   .. .. .. ..$ bwumblty : num 1 #>   .. .. .. ..$ bwumblwd : num 1 #>   .. .. .. ..$ bwoutcol : chr \"blue\" #>   .. .. .. ..$ bwoutcex : num 0.8 #>   .. .. .. ..$ bwoutpch : num 1 #>   .. .. .. ..$ hicol    : num 5 #>   .. .. .. ..$ hiborder : chr \"black\" #>   .. .. .. ..$ hilty    : num 1 #>   .. .. .. ..$ hilwd    : num 1 #>   .. .. .. .. [list output truncated] #>   .. .. ..@ Miss         : num -99 #>   .. .. ..@ Cat.levels   : num 4 #>   .. .. ..@ DV.Cat.levels: num 7 #>   .. .. ..@ Subset       : NULL #>   .. .. ..@ Gam.prefs    :List of 21 #>   .. .. .. ..$ onlyfirst : logi TRUE #>   .. .. .. ..$ wts       : logi FALSE #>   .. .. .. ..$ start.mod : NULL #>   .. .. .. ..$ steppit   : logi TRUE #>   .. .. .. ..$ disp      : NULL #>   .. .. .. ..$ nmods     : num 3 #>   .. .. .. ..$ smoother1 : num 0 #>   .. .. .. ..$ smoother2 : num 1 #>   .. .. .. ..$ smoother3 : chr \"ns\" #>   .. .. .. ..$ smoother4 : chr \"ns\" #>   .. .. .. ..$ arg1      : NULL #>   .. .. .. ..$ arg2      : NULL #>   .. .. .. ..$ arg3      : chr \"df=2\" #>   .. .. .. ..$ arg4      : chr \"df=3\" #>   .. .. .. ..$ excl1     : NULL #>   .. .. .. ..$ excl2     : NULL #>   .. .. .. ..$ excl3     : NULL #>   .. .. .. ..$ excl4     : NULL #>   .. .. .. ..$ extra     : NULL #>   .. .. .. ..$ plot.ids  : logi TRUE #>   .. .. .. ..$ medianNorm: logi TRUE #>   .. .. ..@ Bootgam.prefs:List of 10 #>   .. .. .. ..$ n             : num 100 #>   .. .. .. ..$ algo          : chr \"fluct.ratio\" #>   .. .. .. ..$ conv.value    : num 1.04 #>   .. .. .. ..$ check.interval: num 20 #>   .. .. .. ..$ start.check   : num 50 #>   .. .. .. ..$ liif          : num 0.2 #>   .. .. .. ..$ ljif.conv     : num 25 #>   .. .. .. ..$ seed          : NULL #>   .. .. .. ..$ start.mod     : NULL #>   .. .. .. ..$ excluded.ids  : NULL"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to create files for the simulated prazosin example in Xpose — simprazExample","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"Creates NONMEM data, model output files model prazosin using simulated data.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"","code":"simprazExample(overwrite = FALSE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"overwrite Logical. function overwrite files names already current working directory?","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"Creates files current working directory named: run1.ext run1.lst run1.mod simpraz.dta xptab1","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"Niclas Jonsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/simprazExample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to create files for the simulated prazosin example in Xpose — simprazExample","text":"","code":"od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-f00e6fae00d8efe8984ec802f708f91a\" #> [2] \"downlit\"                                #> [3] \"file55221cc6cf3b\"                        simprazExample(overwrite=TRUE) # write files  (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"run1.ext\"    \"run1.lst\"    \"run1.mod\"    \"simpraz.dta\" \"xptab1\"       file.remove(new.files) # remove these files #> [1] TRUE TRUE TRUE TRUE TRUE setwd(od)  # restore working directory"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate the population parameter estimates — tabulate.parameters","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"function provides summary model's parameter estimates precision.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"","code":"tabulate.parameters(object, prompt = FALSE, outfile = NULL, dir = \"\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"object xpose.data object. prompt Ask printing. outfile file output (NULL means screen). dir directory NONMEM output file located. \"\" means current working directory getwd().","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"table summarizing parameters precision.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"Niclas Jonsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/tabulate.parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate the population parameter estimates — tabulate.parameters","text":"","code":"od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-f00e6fae00d8efe8984ec802f708f91a\" #> [2] \"downlit\"                                #> [3] \"file55221cc6cf3b\"                        simprazExample(overwrite=TRUE) # write files (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"run1.ext\"    \"run1.lst\"    \"run1.mod\"    \"simpraz.dta\" \"xptab1\"      xpdb <- xpose.data(1) # read in files to xpose database #>  #> Looking for NONMEM table files. #>     Reading ./xptab1  #> Table files read. #>  #> Looking for NONMEM simulation table files. #> No simulated table files read. #>   tabulate.parameters(xpdb) #> +---------+-----+-----+ #> |Parameter|Value| RSE | #> +---------+-----+-----+ #> |    TH1  | 17.7|0.059| #> +---------+-----+-----+ #> |    TH2  | 76.8|0.052| #> +---------+-----+-----+ #> |    TH3  |  1.4|0.108| #> +---------+-----+-----+ #> |  OM1:1  | 0.45| 0.13| #> +---------+-----+-----+ #> |  OM2:2  | 0.37| 0.19| #> +---------+-----+-----+ #> |  OM3:3  | 0.77| 0.29| #> +---------+-----+-----+ #> |  SI1:1  | 0.13|0.065| #> +---------+-----+-----+  file.remove(new.files) # remove these files #> [1] TRUE TRUE TRUE TRUE TRUE setwd(od)  # restore working directory"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"histogram distribution weighted residuals (WRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.histogram function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"","code":"wres.dist.hist(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"object xpose.data object. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"Returns histogram weighted residuals (WRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"Displays histogram weighted residuals (WRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of weighted residuals (WRES), for Xpose 4 — wres.dist.hist","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.dist.hist(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"QQ plot distribution weighted residuals (WRES) dataset, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.qq function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"","code":"wres.dist.qq(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"object xpose.data object. ... arguments passed link{xpose.plot.qq}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"Returns QQ plot weighted residuals (WRES).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"Displays QQ plot weighted residuals (WRES).","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.dist.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plot of weighted residuals (WRES), for Xpose 4 — wres.dist.qq","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.dist.qq(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"creates stack plots weighted residuals (WRES) plotted covariates, specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default xpose.plot.histogram functions. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"","code":"wres.vs.cov(   object,   ylb = \"WRES\",   smooth = TRUE,   type = \"p\",   main = \"Default\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"object xpose.data object. ylb string giving label y-axis. NULL none. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. main title plot.  \"Default\" default title plotted. Otherwise value string like \"title\" NULL plot title. ... arguments passed link{xpose.plot.default} link{xpose.plot.histogram}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"Returns stack xyplots histograms CWRES versus covariates.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"Weighted residuals (WRES) plotted covariate present, specified object@Prefs@Xvardef$covariates, creating stack plots. wide array extra options controlling xyplots histograms available. See xpose.plot.default xpose.plot.histogram details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.cov.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weighted residuals (WRES) plotted against covariates, for Xpose 4 — wres.vs.cov","text":"","code":"if (FALSE) { ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Here we load the example xpose database  data(simpraz.xpdb) xpdb <- simpraz.xpdb  ## A vanilla plot wres.vs.cov(xpdb)  ## Custom colours and symbols, IDs wres.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"creates box whisker plot weighted residuals (WRES) vs independent variable (IDV), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"","code":"wres.vs.idv.bw(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"object xpose.data object. ... arguments passed link{xpose.plot.bw}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"Returns stack box--whisker plots WRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"creates box whisker plot weighted residuals (WRES) vs independent variable (IDV), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments. wide array extra options controlling bwplots available. See xpose.plot.bw xpose.panel.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Box-and-whisker plot of weighted residuals vs the independent variable for\nXpose 4 — wres.vs.idv.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.vs.idv.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":null,"dir":"Reference","previous_headings":"","what":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"plot population weighted residuals (WRES) vs independent variable (IDV), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"","code":"wres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"object xpose.data object. abline Vector arguments panel.abline function. abline drawn NULL. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"Returns xyplot WRES vs IDV.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"Weighted residuals (WRES) plotted independent variable, specified object@Prefs@Xvardef$idv. wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.idv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population weighted residuals (WRES) plotted against the independent\nvariable (IDV) for Xpose 4 — wres.vs.idv","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.vs.idv(xpdb)   ## A conditioning plot wres.vs.idv(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"creates box whisker plot weighted residuals (WRES) vs population predictions (PRED), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"","code":"wres.vs.pred.bw(object, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"object xpose.data object. ... arguments passed link{xpose.plot.bw}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"Returns box--whisker plot WRES vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"creates box whisker plot weighted residuals (WRES) vs population predictions (PRED), specific function Xpose 4.  wrapper encapsulating arguments xpose.plot.bw function. options take default values xpose.data object may overridden supplying arguments. wide array extra options controlling bwplots available. See xpose.plot.bw xpose.panel.bw details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Box-and-whisker plot of weighted residuals vs population predictions for\nXpose 4 — wres.vs.pred.bw","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.vs.pred.bw(xpdb)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":null,"dir":"Reference","previous_headings":"","what":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"plot population weighted residuals (WRES) vs population predictions (PRED), specific function Xpose 4. wrapper encapsulating arguments xpose.plot.default function. options take default values xpose.data object may overridden supplying arguments.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"","code":"wres.vs.pred(object, smooth = TRUE, abline = c(0, 0), ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"object xpose.data object. smooth Logical value indicating whether x-y smooth superimposed.  default TRUE. abline Vector arguments panel.abline function. abline drawn NULL. ... arguments passed link{xpose.plot.default}.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"Returns xyplot WRES vs PRED.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"wide array extra options controlling xyplots available. See xpose.plot.default xpose.panel.default details.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/wres.vs.pred.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Population weighted residuals (WRES) plotted against population predictions\n(PRED) for Xpose 4 — wres.vs.pred","text":"","code":"## Here we load the example xpose database  xpdb <- simpraz.xpdb  wres.vs.pred(xpdb)   ## A conditioning plot wres.vs.pred(xpdb, by=\"HCTZ\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract and set labels for Xpose data items. — xlabel","title":"Extract and set labels for Xpose data items. — xlabel","text":"function extracts sets label definitions Xpose data objects.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract and set labels for Xpose data items. — xlabel","text":"","code":"xlabel(x, object)  xlabel(object) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract and set labels for Xpose data items. — xlabel","text":"x Name variable assign label . object xpose.data object. value two element vector first element name variable second label","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract and set labels for Xpose data items. — xlabel","text":"label specified column.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract and set labels for Xpose data items. — xlabel","text":"x string exactly matching name column data.frame Data slot xpose.data object. name columns defined xpose variable definitions (see xpose.data) can extracted using xvardef function used xlabel function, e.g. xlabel(xvardef(\"dv\",object),object), give label dv variable.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract and set labels for Xpose data items. — xlabel","text":"xlabel(object) <- value: sets label definitions Xpose data objects.  assigned value two-element vector  first element name variable second label","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract and set labels for Xpose data items. — xlabel","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xlabel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract and set labels for Xpose data items. — xlabel","text":"","code":"xpdb <- simpraz.xpdb  ## Display label for dependent variable in the Xpose data object xlabel(\"DV\", xpdb) #> [1] \"Observations\"  ## Set label for dependent variable xlabel(xpdb) <- c(\"DV\", \"Concentration (mg/L)\") xlabel(\"DV\", xpdb) # how has this chnaged? #> [1] \"Concentration (mg/L)\""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlations between covariate coefficients — xp.boot.par.est.corr","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"function creates plot showing correlations estimates covariate coefficients, obtained first step (univariate testing) scm performed bootscm.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"","code":"xp.boot.par.est.corr(   bootgam.obj = NULL,   sd.norm = TRUE,   by.cov.type = FALSE,   cov.plot = NULL,   ask.covs = FALSE,   dotpch = 19,   col = rgb(0.2, 0.2, 0.9, 0.75),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"bootgam.obj object created using bootscm.import(), hold data plotting. sd.norm Perform normalization covariate coefficients (default TRUE). TRUE, estimated covariate coefficients multiplied standard deviation specific covariate (continuous categorical covariates). .cov.type Split plot continuous dichotomous covariates. Default FALSE. cov.plot character vector lists covariates include plot. none specified (NULL), covariate coefficients included plot. ask.covs Ask user covariates include plot. Default FALSE. dotpch character used plotting. col colors used plotting. ... Additional plotting arguments may passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"value returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correlations between covariate coefficients — xp.boot.par.est.corr","text":"","code":"if (FALSE) { xp.boot.par.est.corr(current.bootscm, sd.norm = TRUE,                           cov.plot = c(\"CLSEX\", \"VSEX\", \"CLWT\"))  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"function creates plot estimates covariate coefficients, obtained first step (univariate testing) scm performed bootscm. normalized standard deviation, plots can used compare strength covariate relationship. Coloring based covariate included final model (blue) included (red).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"","code":"xp.boot.par.est(   bootgam.obj = NULL,   sd.norm = TRUE,   by.cov.type = FALSE,   abs.values = FALSE,   show.data = TRUE,   show.means = TRUE,   show.bias = TRUE,   dotpch = c(1, 19),   labels = NULL,   pch.mean = \"|\",   xlab = NULL,   ylab = NULL,   col = c(rgb(0.8, 0.5, 0.5), rgb(0.2, 0.2, 0.7), rgb(0.2, 0.2, 0.7), rgb(0.6, 0.6, 0.6)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"bootgam.obj object created using bootscm.import(), hold data plotting. sd.norm Perform normalization covariate coefficients (default TRUE). TRUE, estimated covariate coefficients multiplied standard deviation specific covariate (continuous categorical covariates). .cov.type Split plot continuous dichotomous covariates. Default FALSE. abs.values Show covariate coefficient absolute values. Default FALSE. show.data Show actual covariate coefficients plot. Default TRUE. show.means Show means included covariates (blue) covariates (grey) plot. Default TRUE. show.bias Show estimated bias text plot. Default TRUE. dotpch character used plotting. labels Custom labels parameter-covariate relationships, (character vector) pch.mean character used plotting mean. xlab Custom x-axis label ylab Custom y-axis label col color scheme. ... Additional plotting arguments may passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"value returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"Optionally, estimated bias plotted graph (text). Bias also shown difference mean parameter estimates covariate included (blue diamond), opposed mean parameter estimates (grey diamond) Note: dichotomous covariates, default PsN implementation use common covariate value base, effect value, estimated theta. Xpose (bootscm.import) however recalculates estimated parameters, parametrization lowest value dichotomous covariate base (e.g. 0), estimated THETA denotes proportional change, covariate value (e.g. 1).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.boot.par.est.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare parameter estimates for covariate coefficients — xp.boot.par.est","text":"","code":"xp.boot.par.est() #> boot.type bootgam.objData not available. Did you import the bootSCM data? #> NULL"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.daic.npar.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution of difference in AIC — xp.daic.npar.plot","title":"Distribution of difference in AIC — xp.daic.npar.plot","text":"Distribution difference AIC","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.daic.npar.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution of difference in AIC — xp.daic.npar.plot","text":"","code":"xp.daic.npar.plot(   bootscm.obj = NULL,   main = NULL,   xlb = \"Difference in AIC\",   ylb = \"Density\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.daic.npar.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution of difference in AIC — xp.daic.npar.plot","text":"bootscm.obj bootscm object. main title plot xlb x-label plot ylb y-label plot ... Additional parameters passed panel.xyplot xyplot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.daic.npar.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution of difference in AIC — xp.daic.npar.plot","text":"lattice plot object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"function creates kernel smoothed plot number covariates included final model gam/scm bootgam/bootscm procedure.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"","code":"xp.distr.mod.size(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   bw = 0.5,   xlb = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main Plot title. bw smoothing bandwidth used kernel. xlb x-axis label. ... Additional plotting parameter may passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.distr.mod.size.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of model size distribution for a bootgam or bootscm — xp.distr.mod.size","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.npar.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution of difference in OFV — xp.dofv.npar.plot","title":"Distribution of difference in OFV — xp.dofv.npar.plot","text":"Distribution difference OFV","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.npar.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution of difference in OFV — xp.dofv.npar.plot","text":"","code":"xp.dofv.npar.plot(   bootscm.obj = NULL,   main = NULL,   xlb = \"Difference in OFV\",   ylb = \"Density\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.npar.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution of difference in OFV — xp.dofv.npar.plot","text":"bootscm.obj bootscm object. main title plot xlb x-label plot ylb y-label plot ... Additional parameters passed panel.xyplot xyplot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.npar.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution of difference in OFV — xp.dofv.npar.plot","text":"lattice plot object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"OFV difference (optimism) plot. — xp.dofv.plot","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"plot difference OFV final bootscm models reference final scm model.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"","code":"xp.dofv.plot(   bootscm.obj = NULL,   main = NULL,   xlb = \"Difference in OFV\",   ylb = \"Density\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"bootscm.obj bootgam bootscm object. main Plot title. xlb Label x-axis. ylb Label y-axis. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.dofv.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"OFV difference (optimism) plot. — xp.dofv.plot","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.get.disp.html","id":null,"dir":"Reference","previous_headings":"","what":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","title":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","text":"Default function calculating dispersion xpose.gam.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.get.disp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","text":"","code":"xp.get.disp(gamdata, parnam, covnams, family = \"gaussian\", ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.get.disp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","text":"gamdata data used GAM parnam ONE (one) model parameter name. covnams Covariate names test parameter. family Assumption parameter distribution. ... Used pass arguments basic functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.get.disp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default function for calculating dispersion in xpose.gam. — xp.get.disp","text":"list including dispersion","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.cond.stab.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace plots for conditional indices — xp.inc.cond.stab.cov","title":"Trace plots for conditional indices — xp.inc.cond.stab.cov","text":"Trace plots conditional indices","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.cond.stab.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace plots for conditional indices — xp.inc.cond.stab.cov","text":"","code":"xp.inc.cond.stab.cov(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   xlb = \"Bootstrap replicate number\",   ylb = \"Conditional inclusion frequency\",   normalize = TRUE,   split.plots = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.cond.stab.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace plots for conditional indices — xp.inc.cond.stab.cov","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main title plot xlb x-label plot ylb y-label plot normalize one normalize? split.plots plots split? ... Additional parameters passed panel.xyplot xyplot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.cond.stab.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trace plots for conditional indices — xp.inc.cond.stab.cov","text":"lattice plot object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.ind.cond.stab.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","title":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","text":"Trace plots conditional indices rper replicate number","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.ind.cond.stab.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","text":"","code":"xp.inc.ind.cond.stab.cov(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   xlb = \"Bootstrap replicate number\",   ylb = \"Conditional inclusion frequency\",   limits = c(0.2, 0.8),   normalize = TRUE,   split.plots = FALSE,   start = 25,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.ind.cond.stab.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main title plot xlb x-label plot ylb y-label plot limits Limits inclusion index. normalize one normalize? split.plots plots split? start start. ... Arguments passed functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.ind.cond.stab.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trace plots for conditional indices rper replicate number — xp.inc.ind.cond.stab.cov","text":"lattice plot object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":null,"dir":"Reference","previous_headings":"","what":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"Plot inclusion frequency common 2-covariate combinations.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"","code":"xp.inc.prob.comb.2(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   col = \"#6495ED\",   xlb = NULL,   ylb = \"Covariate combination\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main Plot title col Color used plot. xlb Label x-axis. ylb Label y-axis. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.comb.2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inclusion frequency plot for combination of covariates. — xp.inc.prob.comb.2","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":null,"dir":"Reference","previous_headings":"","what":"Inclusion frequency plot — xp.inc.prob","title":"Inclusion frequency plot — xp.inc.prob","text":"Plot inclusion frequencies covariates final models obtained bootgam bootscm. Covariates ordered inclusion frequency.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inclusion frequency plot — xp.inc.prob","text":"","code":"xp.inc.prob(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   col = \"#6495ED\",   xlb = NULL,   ylb = \"Covariate\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inclusion frequency plot — xp.inc.prob","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main Plot title col Color used plot. xlb Label x-axis. ylb Label y-axis. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inclusion frequency plot — xp.inc.prob","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.prob.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inclusion frequency plot — xp.inc.prob","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"Inclusion stability plot plot inclusion frequency covariates vs bootgam/bootscm   iteration number. plot can used evaluate whether sufficient   iterations performed.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"","code":"xp.inc.stab.cov(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   normalize = TRUE,   split.plots = FALSE,   xlb = \"Bootstrap replicate number\",   ylb = \"Difference of estimate with final\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL,  means user asked make choice. main Plot title normalize plot normalized? split.plots plots split? xlb label x-axis. ylb label y-axis. ... Additional plotting parameters","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"lattice plot object returned.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.inc.stab.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inclusion stability plot\n  \n  A plot of the inclusion frequency of covariates vs bootgam/bootscm\n  iteration number. This plot can be used to evaluate whether sufficient\n  iterations have been performed. — xp.inc.stab.cov","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"plot showing range inclusion indices individuals covariates. plot can used evaluate whether covariates influenced constituency bootstrapped dataset others.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"","code":"xp.incl.index.cov.comp(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   xlb = \"Individual inclusion index\",   ylb = \"ID\",   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main title plot. xlb label x-axis. ylb label y-axis. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"lattice plot object returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.comp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inclusion index individuals, compare between covariates. — xp.incl.index.cov.comp","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of inclusion index of covariates. — xp.incl.index.cov","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"Covariate inclusion indices show correlation inclusion covariate final model bootgam bootscm.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"","code":"xp.incl.index.cov(   bootgam.obj = NULL,   boot.type = NULL,   main = NULL,   xlb = \"Index\",   ylb = \"Covariate\",   add.ci = FALSE,   incl.range = NULL,   return_plot = TRUE,   results.tab = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. main Plot title. xlb Label x-axis. ylb Label y-axis. add.ci Add confidence interval plotted data. incl.range Included range return_plot function return plot? results.tab Specify results table. ... Additional plotting information.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"lattice plot object returned.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of inclusion index of covariates. — xp.incl.index.cov","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual inclusion index — xp.incl.index.cov.ind","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"function generate plot individual inclusion indexes specific covariate, can used identify influential individuals inclusion covariate. index individual calculated observed number inclusions individual specific covariate included minus expected number inclusions (based total bootstrap inclusions), divided expected.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"","code":"xp.incl.index.cov.ind(   bootgam.obj = NULL,   boot.type = NULL,   cov.name = NULL,   main = NULL,   ylb = \"ID\",   xlb = \"Individual inclusion index\",   return_plot = TRUE,   results.tab = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"bootgam.obj bootgam bootscm object. boot.type Either \"bootgam\" \"bootscm\". Default NULL, means user asked make choice. cov.name name covariate create plot. main title plot. ylb label x-axis. xlb label y-axis. return_plot plot object returned? results.tab Supply results table. ... Additional plotting parameters.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"lattice plot object returned.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.incl.index.cov.ind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Individual inclusion index — xp.incl.index.cov.ind","text":"Ron Keizer","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.scope3.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","title":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","text":"Define scope gam.  Used default input scope argument  xpose.gam","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.scope3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","text":"","code":"xp.scope3(   object,   covnam = xvardef(\"covariates\", object),   nmods = 3,   smoother1 = 0,   arg1 = NULL,   smoother2 = 1,   arg2 = NULL,   smoother3 = \"ns\",   arg3 = \"df=2\",   smoother4 = \"ns\",   arg4 = \"df=3\",   excl1 = NULL,   excl2 = NULL,   excl3 = NULL,   excl4 = NULL,   extra = NULL,   subset = xsubset(object),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.scope3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","text":"object xpose.data object. covnam Covariate names test. nmods Number models examine. smoother1 Smoother model. arg1 Argument model 1. smoother2 Smoother model. arg2 Argument model 2. smoother3 Smoother model. arg3 Argument model 3. smoother4 Smoother model. arg4 Argument model 4. excl1 Covariate exclusion model 1. excl2 Covariate exclusion model 2. excl3 Covariate exclusion model 3. excl4 Covariate exclusion model 4. extra Extra exclusion criteria. subset Subset data. ... Used pass arguments basic functions.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xp.scope3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a scope for the gam.  Used as default input to the scope argument in \nxpose.gam — xp.scope3","text":"","code":"xp.scope3(simpraz.xpdb) #> $SEX #> ~1 + SEX #>  #>  #> $RACE #> ~1 + RACE #>  #>  #> $SMOK #> ~1 + SMOK #>  #>  #> $HCTZ #> ~1 + HCTZ #>  #>  #> $PROP #> ~1 + PROP #>  #>  #> $CON #> ~1 + CON #>  #>  #> $AGE #> ~1 + AGE + ns(AGE, df = 2) #>  #>  #> $HT #> ~1 + HT + ns(HT, df = 2) #>  #>  #> $WT #> ~1 + WT + ns(WT, df = 2) #>  #>  #> $SECR #> ~1 + SECR + ns(SECR, df = 2) #>  #>"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":null,"dir":"Reference","previous_headings":"","what":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"Xpose Visual Predictive Check (VPC) continuous Limit Quantification (BLQ ALQ) data.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"","code":"xpose.VPC.both(   vpc.info = \"vpc_results.csv\",   vpctab = dir(pattern = \"^vpctab\")[1],   object = NULL,   subset = NULL,   main = \"Default\",   main.sub = NULL,   inclZeroWRES = FALSE,   cont.logy = F,   hline = \"default\",   add.args.cont = list(),   add.args.cat = list(),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"vpc.info Name PSN file use.  File come VPC command PsN. vpctab Name vpctab file produced PsN. object Xpose data object. subset Subset data look . main Title plot. main.sub Used names plot using multiple plots. vector, e.g. c(\"title 1\",\"title 2\"). inclZeroWRES Include WRES=0 rows computations plots? cont.logy continuous plot y-axis log scale? hline Horizontal line marking limits quantification.  defined, must vector values. add.args.cont Additional arguments continuous plot. xpose.VPC. add.args.cat Additional arguments categorical plot. xpose.VPC.categorical. ... Additional arguments plots.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.both.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xpose Visual Predictive Check (VPC) for both continuous and Limit of\nQuantification data. — xpose.VPC.both","text":"","code":"if (FALSE) { library(xpose4)  ## move to the directory where results from PsN ## are found cur.dir <- getwd() setwd(paste(cur.dir,\"/vpc_cont_LLOQ/\",sep=\"\"))  xpose.VPC() xpose.VPC.categorical(censored=T)  xpose.VPC.both()  xpose.VPC.both(subset=\"DV>1.75\")  xpose.VPC.both(add.args.cont=list(ylim=c(0,80)))  xpose.VPC.both(add.args.cont = list(ylim = c(0.01, 80)), xlim = c(0,      40), add.args.cat = list(ylim = c(0, 0.4)), cont.logy = T)  xpose.VPC.both(cont.logy=T) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"Xpose visual predictive check categorical data (binary, ordered categorical count data).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"","code":"xpose.VPC.categorical(   vpc.info = \"vpc_results.csv\",   vpctab = dir(pattern = \"^vpctab\")[1],   object = NULL,   subset = NULL,   main = \"Default\",   main.sub = \"Default\",   main.sub.cex = 0.85,   real.col = 4,   real.lty = \"b\",   real.cex = 1,   real.lwd = 1,   median.line = FALSE,   median.col = \"darkgrey\",   median.lty = 1,   ci.lines = FALSE,   ci.col = \"blue\",   ci.lines.col = \"darkblue\",   ci.lines.lty = 3,   xlb = \"Default\",   ylb = \"Proportion of Total\",   force.x.continuous = FALSE,   level.to.plot = NULL,   max.plots.per.page = 1,   rug = TRUE,   rug.col = \"orange\",   censored = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"vpc.info Name PSN file use.  File come VPC command PsN. vpctab Name vpctab file produced PsN. object Xpose data object. subset Subset data look . main Title plot. main.sub Used names plot using multiple plots. vector, e.g. c(\"title 1\",\"title 2\"). main.sub.cex Size main.sub real.col Color real line. real.lty Real line type. real.cex Size real line. real.lwd Width real line. median.line Dray median line? median.col Color median line. median.lty median line type. ci.lines Lines marking confidence interval? ci.col Color CI area. ci.lines.col Color CI lines. ci.lines.lty Type CI lines. xlb X-axis label.  \"default\"\" passed directly xyplot. ylb Y-axis label. Passed directly xyplot. force.x.continuous x variable continuous. level..plot levels variable plot. Smallest level 1, largest number_of_levels.  example, 4 levels, largest level 4, smallest 1. max.plots.per.page number plots per page. rug markings plot showing intervals VPC ? rug.col Color rug. censored censored data?  Censored data can limit quantification. ... Additional information passed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.categorical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xpose visual predictive check for categorical data. — xpose.VPC.categorical","text":"","code":"if (FALSE) { library(xpose4)  ## move to the directory where results from PsN ## are found cur.dir <- getwd() setwd(paste(cur.dir,\"/binary/vpc_36\",sep=\"\"))  xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4) xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4,by=\"DOSE\")  ## ordered categorical plots setwd(paste(cur.dir,\"/ordered_cat/vpc_45\",sep=\"\")) xpose.VPC.categorical()   ## count setwd(paste(cur.dir,\"/count/vpc65b\",sep=\"\")) xpose.VPC.categorical()  setwd(paste(cur.dir,\"/count/vpc65a\",sep=\"\")) xpose.VPC.categorical()  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":null,"dir":"Reference","previous_headings":"","what":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"Function used create VPC xpose using output  vpc command Pearl Speaks NONMEM (PsN).  function reads  output files created PsN creates plot data.  dependent  variable, independent variable conditioning variable automatically  determined PsN files.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"","code":"xpose.VPC(   vpc.info = \"vpc_results.csv\",   vpctab = dir(pattern = \"^vpctab\")[1],   object = NULL,   ids = FALSE,   type = \"p\",   by = NULL,   PI = NULL,   PI.ci = \"area\",   PI.ci.area.smooth = FALSE,   PI.real = TRUE,   subset = NULL,   main = \"Default\",   main.sub = NULL,   main.sub.cex = 0.85,   inclZeroWRES = FALSE,   force.x.continuous = FALSE,   funy = NULL,   logy = FALSE,   ylb = \"Default\",   verbose = FALSE,   PI.x.median = TRUE,   PI.rug = \"Default\",   PI.identify.outliers = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"vpc.info results file vpc command PsN.  example vpc_results.csv, file separate directory  ./vpc_dir1/vpc_results.csv. vpctab vpctab vpc command PsN.   example vpctab5, file separate directory  ./vpc_dir1/vpctab5.  Can NULL.  default looks  current working directory takes first file starts  vpctab finds.  Note default can result  wrong files read multiple vpctab files  directory. One object vpctab required.   present information vpctab -ride  xpose data object object (.e. values vpctab  replace matching values object\\@Data portion xpose data object). object xpose data object. Created xpose.data.  One object vpctab required.  present  information vpctab -ride xpose data  object object (.e. values vpctab replace  matching values object\\@Data portion xpose data  object). ids logical value indicating whether text ID labels used  plotting symbols (variable used symbols indicated  idlab xpose data variable). Can FALSE TRUE. type Character string describing way points plot  displayed. details, see plot. Use  type=\"n\" want observations plot. string vector strings name(s)  conditioning variables. example = c(\"SEX\",\"WT\").  function automatically determines conditioning variable PsN  input file specified vpc.info, command can control  separate plots created condition (=NULL),  conditioning plot created (=\"WT\" example).   vpc.info file conditioning variable must match  variable.  conditioning variable vpc.info  PI conditioned plot PI  entire data set (conditioning subset). PI Either \"lines\", \"area\" \"\" specifying whether prediction  intervals (lines, shaded area ) added plot.  NULL means prediction interval. PI.ci Plot confidence interval simulated data's percentiles bin (simulated data set compute percentiles bin, , percentiles simulated datasets compute 95% CI percentiles). Values can \"\", \"area\" \"lines\". CIs can used asses PI.real values model misspecification. Note observations per bin CIs approximate  percentiles bin approximate. example, 95th  percentile 4 data points always largest 4 data  points. PI.ci.area.smooth \"area\" PI.ci smoothed match \"lines\" argument? Allowed values TRUE/FALSE. \"area\" set default show bins used PI.ci computation.  smoothing, information lost , general, confidence intervals smaller reality. PI.real Plot percentiles real data various bins.  values can NULL TRUE.  Note bin actual  observations percentiles approximate.  example, 95th  percentile 4 data points always largest 4 data  points. subset string giving subset expression applied data plotting. See xsubset. main string giving plot title NULL none.  \"Default\" creates default title. main.sub Used names plot using multiple plots.  vector c(\"Group 1\",\"Group 2\") main.sub.cex size main.sub titles. inclZeroWRES Logical value indicating whether rows WRES=0  included plot. force.x.continuous Logical value indicating whether x-values converted continuous variables, even defined factors. funy String function apply Y data. example \"abs\" logy Logical value indicating whether y-axis  logarithmic, base 10. ylb Label y-axis verbose warning messages diagnostic information  passed screen? (TRUE FALSE) PI.x.median x-location percentile lines bin  marked median x-values? (TRUE FALSE) PI.rug markings plot showing binning intervals  VPC  (locations independent variable used VPC calculation binning used)? PI.identify.outliers outlying percentiles real data highlighted? (TRUE FALSE) ... arguments passed xpose.panel.default,  xpose.plot.default others. Please see functions descriptions can .","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"plot list plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"additional-arguments","dir":"Reference","previous_headings":"","what":"Additional arguments","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"additional arguments can control look    feel VPC.  See    xpose.panel.default potential options. Additional graphical elements available VPC plots.  PI.mirror = NULL, TRUE .INTEGER.VALUE Plot percentiles    one simulated data set bin. TRUE takes first mirror   vpc_results.csv .INTEGER.VALUE can 1, 2,    ...{} n n number mirror's output    vpc_results.csv file. PI.limits = c(0.025, 0.975) vector two    values describe limits prediction interval    displayed.  limits found vpc_results.csv    file. limits also used percentages PI.real,    PI.mirror PI.ci.  However, confidence interval    PI.ci always one defined vpc_results.csv file. Additional options control look feel PI.    See See grid.polygon plot    details.  PI.arcol color PI area PI..lty upper line type. can \"dotted\" \"dashed\", etc. PI..type upper type used plotting.  Defaults line. PI..col upper line color PI..lwd upper line width PI..lty lower line type. can \"dotted\" \"dashed\", etc. PI..type lower type used plotting. Defaults line. PI..col lower line color PI..lwd lower    line width PI.med.lty median line type. can \"dotted\"    \"dashed\", etc. PI.med.type median type used plotting.    Defaults line. PI.med.col median line color PI.med.lwd median line width Additional options control look feel    PI.ci. See See grid.polygon    plot details.  PI.ci..arcol color upper PI.ci. PI.ci.med.arcol color median PI.ci. PI.ci..arcol color lower PI.ci. PI.ci..lty upper line type. can \"dotted\" \"dashed\", etc. PI.ci..type upper type used plotting.  Defaults    line. PI.ci..col upper line color PI.ci..lwd    upper line width PI.ci..lty lower line type. can    \"dotted\" \"dashed\", etc. PI.ci..type lower type used   plotting.  Defaults line. PI.ci..col lower line    color PI.ci..lwd lower line width PI.ci.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.ci.med.type median type used plotting.  Defaults    line. PI.ci.med.col median line color PI.ci.med.lwd median line width PI.ci.area.smooth    \"area\" PI.ci smoothed match \"lines\" argument? Allowed    values TRUE/FALSE. \"area\" set default show bins   used PI.ci computation.  smoothing, information lost    , general, confidence intervals smaller    reality. Additional options control look feel    PI.real. See See grid.polygon    plot details.  PI.real..lty upper line type. can \"dotted\"    \"dashed\", etc. PI.real..type upper type used plotting.    Defaults line. PI.real..col upper line color PI.real..lwd upper line width PI.real..lty lower    line type. can \"dotted\" \"dashed\", etc. PI.real..type   lower type used plotting.  Defaults line. PI.real..col lower line color PI.real..lwd lower    line width PI.real.med.lty median line type. can \"dotted\"    \"dashed\", etc. PI.real.med.type median type used    plotting.  Defaults line. PI.real.med.col median line    color PI.real.med.lwd median line width Additional options control look feel    PI.mirror. See See plot    details.  PI.mirror..lty upper line type. can \"dotted\"    \"dashed\", etc. PI.mirror..type upper type used plotting.   Defaults line. PI.mirror..col upper line color PI.mirror..lwd upper line width PI.mirror..lty    lower line type. can \"dotted\" \"dashed\", etc. PI.mirror..type lower type used plotting.  Defaults    line. PI.mirror..col lower line color PI.mirror..lwd lower line width PI.mirror.med.lty    median line type. can \"dotted\" \"dashed\", etc. PI.mirror.med.type median type used plotting.  Defaults    line. PI.mirror.med.col median line color PI.mirror.med.lwd median line width","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.VPC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visual Predictive Check (VPC) using XPOSE — xpose.VPC","text":"","code":"if (FALSE) { library(xpose4)  xpose.VPC()  ## to be more clear about which files should be read in vpc.file <- \"vpc_results.csv\" vpctab <- \"vpctab5\" xpose.VPC(vpc.info=vpc.file,vpctab=vpctab)  ## with lines and a shaded area for the prediction intervals xpose.VPC(vpc.file,vpctab=vpctab,PI=\"both\")  ## with the percentages of the real data xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T)  ## with mirrors (if supplied in 'vpc.file') xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.mirror=5)  ## with CIs xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci=\"area\") xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci=\"area\",PI=NULL)  ## stratification (if 'vpc.file' is stratified) cond.var <- \"WT\" xpose.VPC(vpc.file,vpctab=vpctab) xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var) xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI=\"both\",by=cond.var,type=\"n\")  ## with no data points in the plot xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var,PI.real=T,PI.ci=\"area\",PI=NULL,type=\"n\")  ## with different DV and IDV, just read in new files and plot vpc.file <- \"vpc_results.csv\" vpctab <- \"vpctab5\" cond.var <- \"WT\" xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI=\"both\",by=cond.var) xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI=\"both\")  ## to use an xpose data object instead of vpctab ## ## In this example ## we expect to find the required NONMEM run and table files for run ## 5 in the current working directory runnumber <- 5 xpdb <- xpose.data(runnumber) xpose.VPC(vpc.file,object=xpdb)  ## to read files in a directory different than the current working directory  vpc.file <- \"./vpc_strat_WT_4_mirror_5/vpc_results.csv\" vpctab <- \"./vpc_strat_WT_4_mirror_5/vpctab5\" xpose.VPC(vpc.info=vpc.file,vpctab=vpctab)  ## to rearrange order of factors in VPC plot xpdb@Data$SEX <- factor(xpdb@Data$SEX,levels=c(\"2\",\"1\")) xpose.VPC(by=\"SEX\",object=xpdb)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to ask the user for the name of a file — xpose.ask.for.filename","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"Asks user name file.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"","code":"xpose.ask.for.filename(   object,   listfile = paste(\"run\", object@Runno, \".lst\", sep = \"\"),   modfile = paste(\"run\", object@Runno, \".mod\", sep = \"\"),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"object xpose.data object. listfile NONMEM output file modfile NONMEM model file ... Additional arguments passed function","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"name file exists, otherwise nothing returned.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"Function checks file exists, filename returned function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.ask.for.filename.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to ask the user for the name of a file — xpose.ask.for.filename","text":"Niclas Jonsson, Justin Wilkins, Mats Karlsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":null,"dir":"Reference","previous_headings":"","what":"Title — xpose.bootgam","title":"Title — xpose.bootgam","text":"Title","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Title — xpose.bootgam","text":"","code":"xpose.bootgam(   object,   n = n,   id = object@Prefs@Xvardef$id,   oid = \"OID\",   seed = NULL,   parnam = xvardef(\"parms\", object)[1],   covnams = xvardef(\"covariates\", object),   conv.value = object@Prefs@Bootgam.prefs$conv.value,   check.interval = as.numeric(object@Prefs@Bootgam.prefs$check.interval),   start.check = as.numeric(object@Prefs@Bootgam.prefs$start.check),   algo = object@Prefs@Bootgam.prefs$algo,   start.mod = object@Prefs@Bootgam.prefs$start.mod,   liif = as.numeric(object@Prefs@Bootgam.prefs$liif),   ljif.conv = as.numeric(object@Prefs@Bootgam.prefs$ljif.conv),   excluded.ids = as.numeric(object@Prefs@Bootgam.prefs$excluded.ids),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Title — xpose.bootgam","text":"object xpose.data object. n number bootstrap iterations id column name id oid create new column original ID data seed random seed parnam ONE (one) model parameter name. covnams Covariate names test parameter. conv.value Convergence value check.interval often check convergence start.check start checking algo algorithm use start.mod start model liif liif value ljif.conv convergence value liif excluded.ids ID values exclude. ... Used pass arguments basic functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Title — xpose.bootgam","text":"list results bootstrap GAM.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.bootgam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Title — xpose.bootgam","text":"","code":"if (FALSE) { ## filter out occasion as a covariate as only one value all_covs <- xvardef(\"covariates\",simpraz.xpdb) some_covs <- all_covs[!(all_covs %in% \"OCC\") ]   ## here only running n=5 replicates to see that things work ##   use something like n=100 for resonable results boot_gam_obj <- xpose.bootgam(simpraz.xpdb,5,parnam=\"KA\",covnams=some_covs,seed=1234) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions to create labels for plots — xpose.create.title","title":"Functions to create labels for plots — xpose.create.title","text":"Functions create labels plots","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions to create labels for plots — xpose.create.title","text":"","code":"xpose.create.title(   x,   y,   object,   subset = NULL,   funx = NULL,   funy = NULL,   no.runno = FALSE,   ... )  xpose.create.label(   x,   object,   fun,   logx,   autocorr.x = FALSE,   autocorr.y = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions to create labels for plots — xpose.create.title","text":"x Column name x-variable y Column name y variable object Xpose data object subset Subset used plot funx Function applied x data funy Function applied y data .runno include run number label ... additional arguments passed function. fun Function applied data","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions to create labels for plots — xpose.create.title","text":"Plot titles labels.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Functions to create labels for plots — xpose.create.title","text":"xpose.create.label(): Create label values","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions to create labels for plots — xpose.create.title","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.text.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Xpose title text for plots. — xpose.create.title.text","title":"Create Xpose title text for plots. — xpose.create.title.text","text":"Create Xpose title text plots.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Xpose title text for plots. — xpose.create.title.text","text":"","code":"xpose.create.title.text(x, y, text, object, subset, text2 = NULL, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Xpose title text for plots. — xpose.create.title.text","text":"x x-axis variable name. y y-axis variable name. text Initial text title. object Xpose data object xpose.data. subset Subset definition. text2 Text end title. ... Additional options passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.create.title.text.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create Xpose title text for plots. — xpose.create.title.text","text":"Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class xpose.data — xpose.data-class","title":"Class xpose.data — xpose.data-class","text":"xpose.data class fundamental data object Xpose 4. contains data preferences used creation Xpose plots analyses.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data-class.html","id":"objects-from-the-class","dir":"Reference","previous_headings":"","what":"Objects from the Class","title":"Class xpose.data — xpose.data-class","text":"Objects easily created xpose.data function, reads appropriate NONMEM table files populates slots object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Class xpose.data — xpose.data-class","text":"Niclas Jonsson Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an Xpose data object — xpose.data","title":"Create an Xpose data object — xpose.data","text":"Creates xpose.data object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an Xpose data object — xpose.data","text":"","code":"xpose.data(   runno,   tab.suffix = \"\",   sim.suffix = \"sim\",   cwres.suffix = \"\",   directory = \".\",   quiet = TRUE,   table.names = c(\"sdtab\", \"mutab\", \"patab\", \"catab\", \"cotab\", \"mytab\", \"extra\", \"xptab\",     \"cwtab\"),   cwres.name = c(\"cwtab\"),   mod.prefix = \"run\",   mod.suffix = \".mod\",   phi.suffix = \".phi\",   phi.file = NULL,   nm7 = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an Xpose data object — xpose.data","text":"runno Run number table files read. tab.suffix Suffix appended table file names  \"real\" data. sim.suffix Suffix appended table file names  simulated data. cwres.suffix Suffix appended table file names  CWRES data. directory files located. quiet logical value indicating diagnostic messages printed running function. table.names Default text Xpose looks searching table files. cwres.name default text xpose looks searching CWRES  table files. mod.prefix Start model file name. mod.suffix End model file name. phi.suffix End .phi file name. phi.file name .phi file. NULL supersedes paste(mod.prefix,runno,phi.suffix,sep=\"\"). nm7 T/F table files NONMEM 7/6, NULL undefined. ... Extra arguments passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an Xpose data object — xpose.data","text":"xpose.data object.  Default values object    created file called 'xpose.ini'.  file can found root   directory 'xpose4' package:  system.file(\"xpose.ini\",package=\"xpose4\"). can modified fit users    wants placed home folder user working directory,    override default settings.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create an Xpose data object — xpose.data","text":"Xpose expects, default, find least one following NONMEM  tables working directory able create Xpose data object  (using run number '5' example): sdtab5: 'standard' parameters, including IWRE, IPRE, TIME, NONMEM default items (DV, PRED, RES WRES) added NOAPPEND  present $TABLE record. $TABLE ID TIME IPRE IWRE NOPRINT ONEHEADER FILE=sdtab5 patab5: empirical Bayes estimates individual model parameter values,  posthoc estimates. model parameters, CL, V2, ETA1, etc. $TABLE ID CL V2 KA K F1 ETA1 ETA2 ETA3 NOPRINT NOAPPEND ONEHEADER  FILE=patab5 catab5: Categorical covariates, e.g. SEX, RACE. $TABLE ID SEX HIV GRP NOPRINT NOAPPEND ONEHEADER FILE=catab5 cotab5: Continuous covariates, e.g. WT, AGE. $TABLE ID WT AGE BSA HT GGT HB NOPRINT NOAPPEND ONEHEADER FILE=cotab5 mutab5, mytab5, extra5, xptab5: Additional variables kind. might useful covariates can accommodated  covariates tables, example, variables added, e.g. CMAX, AUC. default names table files can changed changing default  values function.  files Xpose looks default : paste(table.names, runno, tab.suffix, sep=\"\") default CWRES table file name called: paste(cwres.name,runno,cwres.suffix,tab.suffix,sep=\"\") simulation files present Xpose looks files  named: paste(table.names, runno, sim.suffix, tab.suffix, sep=\"\")  paste(cwres.name,runno,sim.suffix,cwres.suffix,tab.suffix,sep=\"\") basically wrapper function read.nm.tables,  Data SData functions. See information. Also reads .phi file associated run (Individual OFVs,  parameters, variances parameters.)","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create an Xpose data object — xpose.data","text":"Niclas Jonsson, Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an Xpose data object — xpose.data","text":"","code":"# Here we create files from an example NONMEM run   od = setwd(tempdir()) # move to a temp directory (cur.files <- dir()) # current files in temp directory #> [1] \"bslib-f00e6fae00d8efe8984ec802f708f91a\" #> [2] \"downlit\"                                #> [3] \"file55221cc6cf3b\"                        simprazExample(overwrite=TRUE) # write files (new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here? #> [1] \"run1.ext\"    \"run1.lst\"    \"run1.mod\"    \"simpraz.dta\" \"xptab1\"       xpdb <- xpose.data(1) #>  #> Looking for NONMEM table files. #>     Reading ./xptab1  #> Table files read. #>  #> Looking for NONMEM simulation table files. #> No simulated table files read. #>    file.remove(new.files) # remove these files #> [1] TRUE TRUE TRUE TRUE TRUE setwd(od)  # restore working directory   if (FALSE) {  # We expect to find the required NONMEM run and table files for run # 5 in the current working directory, and that the table files have # a suffix of '.dat', e.g. sdtab5.dat xpdb5 <- xpose.data(5, tab.suffix = \".dat\")  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.dev.new.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a new graphical device for an Xpose plot. — xpose.dev.new","title":"Create a new graphical device for an Xpose plot. — xpose.dev.new","text":"function uses code dev.new(). function make dev.new() back compatible older versions R (2.8.0).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.dev.new.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a new graphical device for an Xpose plot. — xpose.dev.new","text":"","code":"xpose.dev.new(...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.dev.new.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a new graphical device for an Xpose plot. — xpose.dev.new","text":"... Additional arguments new graphical device.  see dev.new.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.dev.new.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a new graphical device for an Xpose plot. — xpose.dev.new","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"Function takes Xpose object performs generalized additive model (GAM) stepwise search influential covariates single model parameter.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"","code":"xpose.gam(   object,   parnam = xvardef(\"parms\", object)[1],   covnams = xvardef(\"covariates\", object),   trace = TRUE,   scope = NULL,   disp = object@Prefs@Gam.prefs$disp,   start.mod = object@Prefs@Gam.prefs$start.mod,   family = \"gaussian\",   wts.data = object@Data.firstonly,   wts.col = NULL,   steppit = object@Prefs@Gam.prefs$steppit,   subset = xsubset(object),   onlyfirst = object@Prefs@Gam.prefs$onlyfirst,   medianNorm = object@Prefs@Gam.prefs$medianNorm,   nmods = object@Prefs@Gam.prefs$nmods,   smoother1 = object@Prefs@Gam.prefs$smoother1,   smoother2 = object@Prefs@Gam.prefs$smoother2,   smoother3 = object@Prefs@Gam.prefs$smoother3,   smoother4 = object@Prefs@Gam.prefs$smoother4,   arg1 = object@Prefs@Gam.prefs$arg1,   arg2 = object@Prefs@Gam.prefs$arg2,   arg3 = object@Prefs@Gam.prefs$arg3,   arg4 = object@Prefs@Gam.prefs$arg4,   excl1 = object@Prefs@Gam.prefs$excl1,   excl2 = object@Prefs@Gam.prefs$excl2,   excl3 = object@Prefs@Gam.prefs$excl3,   excl4 = object@Prefs@Gam.prefs$excl4,   extra = object@Prefs@Gam.prefs$extra,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"object xpose.data object. parnam ONE (one) model parameter name. covnams Covariate names test parameter. trace TRUE want GAM output screen. scope Scope GAM search. disp dispersion used GAM object. start.mod Starting model. family Assumption parameter distribution. wts.data Weights least squares fitting parameter vs. covariate. Often one can use variances individual parameter values weights. data frame must column name ID subset variable well variable defined wts.col. wts.col column wts.data use. steppit TRUE stepwise search, false search. subset Subset data. onlyfirst TRUE first row individual's data used. medianNorm Normalize median parameter covariates. nmods Number models examine. smoother1 Smoother model. smoother2 Smoother model. smoother3 Smoother model. smoother4 Smoother model. arg1 Argument model 1. arg2 Argument model 2. arg3 Argument model 3. arg4 Argument model 4. excl1 Covariate exclusion model 1. excl2 Covariate exclusion model 2. excl3 Covariate exclusion model 3. excl4 Covariate exclusion model 4. extra Extra exclusion criteria. ... Used pass arguments basic functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"Returned step.Gam object. object  step-wise-selected model returned, two additional  components. \"anova\" component  corresponding steps taken search, well  \"keep\" component \"keep=\" argument supplied call.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stepwise GAM search for covariates on a parameter (Xpose 4) — xpose.gam","text":"","code":"## Run a GAM using the example xpose database  gam_ka <- xpose.gam(simpraz.xpdb, parnam=\"KA\") #> Start:  KA ~ 1; AIC= 166.381  #> Step:1 KA ~ RACE ; AIC= 165.4162  #> Step:2 KA ~ RACE + SECR ; AIC= 164.8582  #> Step:3 KA ~ RACE + AGE + SECR ; AIC= 164.8219   ## Summarize GAM xp.summary(gam_ka) #>  #> SUMMARY #> Call: gam(formula = KA ~ RACE + AGE + SECR, data = gamdata, trace = FALSE) #> Deviance Residuals: #>      Min       1Q   Median       3Q      Max  #> -1.63145 -0.65128 -0.05558  0.41369  2.68692  #>  #> (Dispersion Parameter for gaussian family taken to be 0.6916) #>  #>     Null Deviance: 47.3804 on 63 degrees of freedom #> Residual Deviance: 40.8068 on 59 degrees of freedom #> AIC: 164.8219  #>  #> Number of Local Scoring Iterations: 2  #>  #> Anova for Parametric Effects #>           Df Sum Sq Mean Sq F value  Pr(>F)   #> RACE       2  3.537 1.76829  2.5567 0.08613 . #> AGE        1  1.629 1.62924  2.3556 0.13018   #> SECR       1  1.408 1.40786  2.0355 0.15893   #> Residuals 59 40.807 0.69164                   #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>  #>  #> PATH TO FINAL MODEL #> Stepwise Model Path  #> Analysis of Deviance Table #>  #> Initial Model: #> KA ~ 1 #>  #> Final Model: #> KA ~ RACE + AGE + SECR #>  #> Scale: 0.7520703 #>  #>   From      To Df  Deviance Resid. Df Resid. Dev      AIC #> 1                           63   47.38043 166.3810 #> 2         RACE -2 -3.536572        61   43.84386 165.4162 #> 3         SECR -1 -1.717840        60   42.12602 164.8582 #> 4          AGE -1 -1.319258        59   40.80676 164.8219 #>  #> COEFFICIENTS #> (Intercept)       RACE2       RACE3         AGE        SECR  #> -0.08272054  0.53724306 -0.32761296 -0.01411650  0.74934090  #>  #> PRERUN RESULTS #> Dispersion:  #>  #> DATA #> Subset expression:  #> Only first value of covariate considered #> for each individual: TRUE  #> Covariates normalized to median: TRUE   ## GAM residuals of base model vs. covariates xp.plot(gam_ka)   ## An Akaike plot of the results xp.akaike.plot(gam_ka)   ## Studentized residuals xp.ind.stud.res(gam_ka)   ## Individual influence on GAM fit xp.ind.inf.fit(gam_ka) #>  #> For ID 23: #>    Cook distance is Inf #>    Leverage is Inf #>    => the point is not included in the plot   ## Individual influence on GAM terms xp.ind.inf.terms(gam_ka)   ## Individual parameters to GAM fit xp.cook(gam_ka) #>     (Intercept)     RACE2     RACE3      AGE       SECR #> 2           NaN 0.4069038 0.4069038 6.384659 0.15108146 #> 12          NaN 0.6632264 0.6632264 6.166696 0.13341637 #> 23          NaN 0.5152555 0.5152555 5.976245 0.14359840 #> 34          NaN 0.4083608 0.4083608 5.526933 0.15078901 #> 44          NaN 0.4491550 0.4491550 4.878261 0.14571869 #> 54          NaN 0.4336340 0.4336340 6.669965 0.14805262 #> 64          NaN 0.3461857 0.3461857 4.527164 0.15895947 #> 72          NaN 0.3522048 0.3522048 3.950764 0.15794212 #> 82          NaN 0.4026346 0.4026346 5.765467 0.15160252 #> 91          NaN 0.4588266 0.4588266 4.012911 0.14621651 #> 97          NaN 0.4048793 0.4048793 5.617073 0.15128375 #> 107         NaN 0.3911857 0.3911857 6.913509 0.15265331 #> 118         NaN 0.4255037 0.4255037 4.985381 0.14871870 #> 129         NaN 0.2775285 0.2775285 5.384752 0.16549514 #> 140         NaN 0.2162661 0.2162661 5.215029 0.17375389 #> 151         NaN 0.8291616 0.8291616 6.405338 0.11860555 #> 162         NaN 0.4094348 0.4094348 5.503623 0.15077884 #> 173         NaN 0.4687459 0.4687459 5.750293 0.14515016 #> 184         NaN 0.2013301 0.2013301 5.245030 0.17645826 #> 194         NaN 0.1656837 0.1656837 5.095194 0.18085408 #> 204         NaN 0.4118900 0.4118900 6.223995 0.15061385 #> 212         NaN 0.4044946 0.4044946 5.637613 0.15133188 #> 223         NaN 0.2613920 0.2613920 5.230492 0.16681641 #> 234         NaN 0.4215614 0.4215614 5.674149 0.14967349 #> 245         NaN 0.2141738 0.2141738 5.160388 0.17440596 #> 256         NaN 0.3187987 0.3187987 5.435324 0.16015507 #> 267         NaN 0.3931962 0.3931962 5.619256 0.15244641 #> 278         NaN 0.1791398 0.1791398 5.118928 0.17802496 #> 288         NaN 0.2786389 0.2786389 5.355595 0.16394934 #> 299         NaN 0.3020364 0.3020364 5.331388 0.15913854 #> 310         NaN 0.5316421 0.5316421 5.886831 0.14016374 #> 321         NaN 0.4808547 0.4808547 5.806053 0.14449849 #> 332         NaN 0.3755979 0.3755979 5.551405 0.15355223 #> 341         NaN 0.2468930 0.2468930 5.263710 0.16836440 #> 350         NaN 0.4810597 0.4810597 5.803645 0.14479950 #> 361         NaN 0.1363992 0.1363992 5.119674 0.18758251 #> 367         NaN 0.4390024 0.4390024 4.949578 0.14706063 #> 373         NaN 0.4171210 0.4171210 5.667151 0.15017331 #> 382         NaN 0.6554549 0.6554549 6.115453 0.13743260 #> 393         NaN 0.3212037 0.3212037 5.450513 0.15824381 #> 404         NaN 0.4468825 0.4468825 5.703860 0.14773504 #> 411         NaN 0.2944807 0.2944807 5.373341 0.16129232 #> 422         NaN 0.3582801 0.3582801 5.537411 0.15588547 #> 433         NaN 0.4071513 0.4071513 6.098040 0.15089973 #> 438         NaN 0.4004948 0.4004948 5.913169 0.15183038 #> 449         NaN 0.5671897 0.5671897 5.977558 0.13739557 #> 455         NaN 1.4642283 1.4642283 7.298495 0.08745328 #> 468         NaN 0.3706551 0.3706551 5.533304 0.15447763 #> 479         NaN 0.4053951 0.4053951 5.565423 0.15118627 #> 490         NaN 0.4250737 0.4250737 6.548404 0.14867238 #> 500         NaN 0.3402770 0.3402770 5.489249 0.15785681 #> 511         NaN 0.5110472 0.5110472 5.847233 0.14135623 #> 522         NaN 0.5370821 0.5370821 7.805795 0.13603361 #> 533         NaN 0.4734972 0.4734972 5.849134 0.14556442 #> 543         NaN 0.3813159 0.3813159 5.346115 0.15430963 #> 550         NaN 0.1893831 0.1893831 5.069682 0.17774347 #> 561         NaN 0.6178044 0.6178044 6.027150 0.13309655 #> 569         NaN 0.3328130 0.3328130 5.436414 0.15842242 #> 578         NaN 0.8144037 0.8144037 6.370737 0.12069337 #> 589         NaN 0.7148646 0.7148646 6.242226 0.12660672 #> 600         NaN 0.5095002 0.5095002 5.829433 0.14194991 #> 610         NaN 0.3837543 0.3837543 5.596823 0.15345795 #> 620         NaN 0.2205120 0.2205120 5.213856 0.17129241 #> 631         NaN 0.3398401 0.3398401 5.505575 0.15815307"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":null,"dir":"Reference","previous_headings":"","what":"Displays the Xpose license and citation information — xpose.license.citation","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"function displays copy Xpose's end user license agreement (EULA).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"","code":"xpose.license.citation()"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"EULA.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.license.citation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Displays the Xpose license and citation information — xpose.license.citation","text":"","code":"xpose.license.citation() #>  #> This program is free software: you can redistribute it and/or modify #>   it under the terms of the GNU Lesser General Public License as published by #>   the Free Software Foundation, either version 3 of the License, or #>   (at your option) any later version. #>    #>   This program is distributed in the hope that it will be useful, #>   but WITHOUT ANY WARRANTY; without even the implied warranty of #>   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the #>   GNU Lesser General Public License for more details. #>    #>   A copy of the GNU Lesser General Public License can be found in the R #>   installation directory (/opt/R/4.3.2/lib/R/share)under licenses.   #>   If not, see . #>  #> To cite xpose4 in publications, use: #>  #>   Jonsson, E.N. & Karlsson, M.O. (1999) Xpose--an S-PLUS based #>   population pharmacokinetic/pharmacodynamic model building aid for #>   NONMEM. Computer Methods and Programs in Biomedicine. 58(1):51-64. #>  #>   Keizer RJ, Karlsson MO, Hooker AC (2013). “Modeling and Simulation #>   Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose.” _CPT: #>   Pharmacometrics & Systems Pharmacology_, *2*(6). #>   doi:10.1038/psp.2013.24 . #>  #> To see these entries in BibTeX format, use 'print(, #> bibtex=TRUE)', 'toBibtex(.)', or set #> 'options(citation.bibtex.max=999)'."},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class for creating multiple plots in xpose — xpose.multiple.plot-class","title":"Class for creating multiple plots in xpose — xpose.multiple.plot-class","text":"Class creating multiple plots xpose","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class for creating multiple plots in xpose — xpose.multiple.plot-class","text":"plotList list lattice plots plotTitle plot title prompt prompts used new.first.window Create new first window? max.plots.per.page many plots per page? title title mirror mirror plots create bql.layout use bql.layout","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"Function takes list lattice plot objects prints multiple plot layout title.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"","code":"xpose.multiple.plot.default(   plotList,   plotTitle = NULL,   prompt = FALSE,   new.first.window = FALSE,   max.plots.per.page = 4,   title = list(title.x = unit(0.5, \"npc\"), title.y = unit(0.5, \"npc\"), title.gp =     gpar(cex = 1.2, fontface = \"bold\"), title.just = c(\"center\", \"center\")),   mirror = FALSE,   bql.layout = FALSE,   page.numbers = TRUE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"plotList list lattice plot objects plot object can called plotList[[]] plotTitle title used multiple plot layout prompt one page needed want prompt command line next page printed new.first.window first page plot already opened window new window created max.plots.per.page Maximum number plots per page multiple layout title Look title using grid. mirror list contains mirror plots bql.layout use layout optimized BQL measurements? page.numbers add page numbers multiple page plots? ... arguments passed code function","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"returns nothing","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"Additional arguments: title.x title placed title grid region title.y title placed title grid region title.just title justified title.gp par parameters title (see grid)","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.default.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Xpose 4 generic function for plotting multiple lattice objects on one page — xpose.multiple.plot.default","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create and object with class ","title":"Create and object with class ","text":"Create object class \"xpose.multiple.plot\".","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create and object with class ","text":"","code":"xpose.multiple.plot(   plotList,   plotTitle = NULL,   nm7 = TRUE,   prompt = FALSE,   new.first.window = FALSE,   max.plots.per.page = 4,   title = list(title.x = unit(0.5, \"npc\"), title.y = unit(0.5, \"npc\"), title.gp =     gpar(cex = 1.2, fontface = \"bold\"), title.just = c(\"center\", \"center\")),   mirror = FALSE,   bql.layout = FALSE,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create and object with class ","text":"plotList list lattice plots. plotTitle Main title plots. nm7 TRUE using NONMEM 7 prompt printing prompt new page plot? new.first.window TRUE FALSE. max.plots.per.page number.  Max value 9. title Title properties. mirror mirror plots plot list? bql.layout use layout optimized plots BQL (limit quantification) measurements? ... Additional options passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create and object with class ","text":"object class \"xpose.multiple.plot\".","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.multiple.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create and object with class ","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","title":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","text":"box--whisker panel function Xpose 4. intended used outside xpose.plot.bw function.  arguments take default values xpose.data object can overridden supplying arguments xpose.plot.bw.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","text":"","code":"xpose.panel.bw(   x,   y,   object,   subscripts,   groups = NULL,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   xvarnam = NULL,   yvarnam = NULL,   type = object@Prefs@Graph.prefs$type,   col = object@Prefs@Graph.prefs$col,   pch = object@Prefs@Graph.prefs$pch,   cex = object@Prefs@Graph.prefs$cex,   lty = object@Prefs@Graph.prefs$lty,   fill = object@Prefs@Graph.prefs$col,   ids = NULL,   idsmode = object@Prefs@Graph.prefs$idsmode,   idsext = object@Prefs@Graph.prefs$idsext,   idscex = object@Prefs@Graph.prefs$idscex,   idsdir = object@Prefs@Graph.prefs$idsdir,   bwhoriz = object@Prefs@Graph.prefs$bwhoriz,   bwratio = object@Prefs@Graph.prefs$bwratio,   bwvarwid = object@Prefs@Graph.prefs$bwvarwid,   bwdotpch = object@Prefs@Graph.prefs$bwdotpch,   bwdotcol = object@Prefs@Graph.prefs$bwdotcol,   bwdotcex = object@Prefs@Graph.prefs$bwdotcex,   bwreccol = object@Prefs@Graph.prefs$bwreccol,   bwrecfill = object@Prefs@Graph.prefs$bwrecfill,   bwreclty = object@Prefs@Graph.prefs$bwreclty,   bwreclwd = object@Prefs@Graph.prefs$bwreclwd,   bwumbcol = object@Prefs@Graph.prefs$bwumbcol,   bwumblty = object@Prefs@Graph.prefs$bwumblty,   bwumblwd = object@Prefs@Graph.prefs$bwumblwd,   bwoutcol = object@Prefs@Graph.prefs$bwoutcol,   bwoutcex = object@Prefs@Graph.prefs$bwoutcex,   bwoutpch = object@Prefs@Graph.prefs$bwoutpch,   grid = object@Prefs@Graph.prefs$grid,   logy = FALSE,   logx = FALSE,   force.x.continuous = TRUE,   binvar = NULL,   bins = 10,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","text":"x Name(s) x-variable. y Name(s) y-variable. object xpose.data object. subscripts standard Trellis subscripts argument (see xyplot). groups Name variable used superpose plots. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. onlyfirst Logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. xvarnam Character string name x-variable. yvarnam Character string name y-variable. type Character value indicating type display use: \"l\"=lines, \"p\"=points, \"b\"=points lines. col Colour lines plot symbols. pch Plot character use. cex Size plot characters. lty Line type. fill Fill colour. ids Character value name variable label data points . idsmode Determines way text labels added plots. NULL means extreme points labelled. Non-NULL means data points labelled. (See link{xpose.plot.default}) idsext See link{xpose.plot.bw} idscex Size text labels. idsdir value \"\" (default) means high low extreme points labelled \"\" \"\" labels high low extreme points respectively. See xpose.plot.bw bwhoriz logical value indicating whether box whiskers horizontal . default FALSE. bwratio Ratio box height inter-box space. default 1.5. argument panel.bwplot. bwvarwid Logical. TRUE, widths boxplots proportional number points used creating . default FALSE. argument panel.bwplot. bwdotpch Graphical parameter controlling dot plotting character 'bwdotpch=\"|\"' treated specially, replacing dot line. default 16. argument panel.bwplot. bwdotcol Graphical parameter controlling dot colour - integer string. See 'col'. default black. argument panel.bwplot. bwdotcex amount plotting text symbols scaled relative default. 'NULL' 'NA' equivalent '1.0'. argument panel.bwplot. bwreccol colour use box rectangle - integer string.  default blue. See trellis.par.get \"box.rectangle\". bwrecfill colour use filling box rectangle - integer string. default transparent (none). See trellis.par.get \"box.rectangle\". bwreclty line type box rectangle - integer string. default solid. See trellis.par.get \"box.rectangle\". bwreclwd width lines box rectangle - integer. default 1. See trellis.par.get \"box.rectangle\". bwumbcol colour use umbrellas - integer string. default blue. See trellis.par.get \"box.umbrella\". bwumblty line type umbrellas - integer string. default solid.See trellis.par.get \"box.umbrella\". bwumblwd width lines umbrellas - integer. default 1. See trellis.par.get \"box.umbrella\". bwoutcol colour use outliers - integer string. default blue. See trellis.par.get \"box.symbol\". bwoutcex amount outlier points scaled relative default. 'NULL' 'NA' equivalent '1.0'. default 0.8. See trellis.par.get \"box.symbol\". bwoutpch plotting character, symbol, use outlier points.  Specified integer. See R help 'points'. default open circle. See trellis.par.get \"box.symbol\". grid logical value indicating whether visual reference grid added graph. (use arguments line type, color etc). logy Logical value indicating whether y-axis logarithmic. logx Logical value indicating whether x-axis logarithmic. force.x.continuous Logical value indicating whether x-values taken continuous, even categorical. binvar Variable used binning. bins number bins used. default 10. ... arguments may needed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default box-and-whisker panel function for Xpose 4 — xpose.panel.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Default panel function for Xpose 4 — xpose.panel.default","title":"Default panel function for Xpose 4 — xpose.panel.default","text":"panel function Xpose 4. intended ised outside xpose.plot.default function. arguments take default values xpose.data object can overridden supplying argument xpose.plot.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default panel function for Xpose 4 — xpose.panel.default","text":"","code":"xpose.panel.default(   x,   y,   object,   subscripts,   groups = object@Prefs@Xvardef$id,   grp.col = NULL,   iplot = NULL,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   xvarnam = NULL,   yvarnam = NULL,   PI = NULL,   PI.subset = NULL,   PI.bin.table = NULL,   PI.real = NULL,   PI.mirror = NULL,   PI.ci = NULL,   PPI = NULL,   PI.mean = FALSE,   PI.delta.mean = FALSE,   PI.x.median = TRUE,   PI.rug = \"Default\",   PI.rug.col = \"orange\",   PI.rug.lwd = 3,   PI.identify.outliers = TRUE,   PI.outliers.col = \"red\",   PI.outliers.pch = 8,   PI.outliers.cex = 1,   PI.limits = c(0.025, 0.975),   PI.arcol = \"lightgreen\",   PI.up.lty = 2,   PI.up.type = \"l\",   PI.up.col = \"black\",   PI.up.lwd = 2,   PI.down.lty = 2,   PI.down.type = \"l\",   PI.down.col = \"black\",   PI.down.lwd = 2,   PI.med.lty = 1,   PI.med.type = \"l\",   PI.med.col = \"black\",   PI.med.lwd = 2,   PI.mean.lty = 3,   PI.mean.type = \"l\",   PI.mean.col = \"black\",   PI.mean.lwd = 2,   PI.delta.mean.lty = 3,   PI.delta.mean.type = \"l\",   PI.delta.mean.col = \"black\",   PI.delta.mean.lwd = 2,   PI.real.up.lty = 2,   PI.real.up.type = \"l\",   PI.real.up.col = \"red\",   PI.real.up.lwd = 2,   PI.real.down.lty = 2,   PI.real.down.type = \"l\",   PI.real.down.col = \"red\",   PI.real.down.lwd = 2,   PI.real.med.lty = 1,   PI.real.med.type = \"l\",   PI.real.med.col = \"red\",   PI.real.med.lwd = 2,   PI.real.mean.lty = 3,   PI.real.mean.type = \"l\",   PI.real.mean.col = \"red\",   PI.real.mean.lwd = 2,   PI.real.delta.mean.lty = 3,   PI.real.delta.mean.type = \"l\",   PI.real.delta.mean.col = \"red\",   PI.real.delta.mean.lwd = 2,   PI.mirror.up.lty = 2,   PI.mirror.up.type = \"l\",   PI.mirror.up.col = \"darkgreen\",   PI.mirror.up.lwd = 1,   PI.mirror.down.lty = 2,   PI.mirror.down.type = \"l\",   PI.mirror.down.col = \"darkgreen\",   PI.mirror.down.lwd = 1,   PI.mirror.med.lty = 1,   PI.mirror.med.type = \"l\",   PI.mirror.med.col = \"darkgreen\",   PI.mirror.med.lwd = 1,   PI.mirror.mean.lty = 3,   PI.mirror.mean.type = \"l\",   PI.mirror.mean.col = \"darkgreen\",   PI.mirror.mean.lwd = 1,   PI.mirror.delta.mean.lty = 3,   PI.mirror.delta.mean.type = \"l\",   PI.mirror.delta.mean.col = \"darkgreen\",   PI.mirror.delta.mean.lwd = 1,   PI.ci.up.arcol = \"blue\",   PI.ci.up.lty = 3,   PI.ci.up.type = \"l\",   PI.ci.up.col = \"darkorange\",   PI.ci.up.lwd = 2,   PI.ci.down.arcol = \"blue\",   PI.ci.down.lty = 3,   PI.ci.down.type = \"l\",   PI.ci.down.col = \"darkorange\",   PI.ci.down.lwd = 2,   PI.ci.med.arcol = \"red\",   PI.ci.med.lty = 4,   PI.ci.med.type = \"l\",   PI.ci.med.col = \"darkorange\",   PI.ci.med.lwd = 2,   PI.ci.mean.arcol = \"purple\",   PI.ci.mean.lty = 4,   PI.ci.mean.type = \"l\",   PI.ci.mean.col = \"darkorange\",   PI.ci.mean.lwd = 2,   PI.ci.delta.mean.arcol = \"purple\",   PI.ci.delta.mean.lty = 4,   PI.ci.delta.mean.type = \"l\",   PI.ci.delta.mean.col = \"darkorange\",   PI.ci.delta.mean.lwd = 2,   PI.ci.area.smooth = FALSE,   type = object@Prefs@Graph.prefs$type,   col = object@Prefs@Graph.prefs$col,   pch = object@Prefs@Graph.prefs$pch,   cex = object@Prefs@Graph.prefs$cex,   lty = object@Prefs@Graph.prefs$lty,   lwd = object@Prefs@Graph.prefs$lwd,   fill = object@Prefs@Graph.prefs$fill,   ids = NULL,   idsmode = object@Prefs@Graph.prefs$idsmode,   idsext = object@Prefs@Graph.prefs$idsext,   idscex = object@Prefs@Graph.prefs$idscex,   idsdir = object@Prefs@Graph.prefs$idsdir,   abline = object@Prefs@Graph.prefs$abline,   abllwd = object@Prefs@Graph.prefs$abllwd,   abllty = object@Prefs@Graph.prefs$abllty,   ablcol = object@Prefs@Graph.prefs$ablcol,   smooth = object@Prefs@Graph.prefs$smooth,   smlwd = object@Prefs@Graph.prefs$smlwd,   smlty = object@Prefs@Graph.prefs$smlty,   smcol = object@Prefs@Graph.prefs$smcol,   smspan = object@Prefs@Graph.prefs$smspan,   smdegr = object@Prefs@Graph.prefs$smdegr,   smooth.for.groups = NULL,   lmline = object@Prefs@Graph.prefs$lmline,   lmlwd = object@Prefs@Graph.prefs$lmlwd,   lmlty = object@Prefs@Graph.prefs$lmlty,   lmcol = object@Prefs@Graph.prefs$lmcol,   suline = object@Prefs@Graph.prefs$suline,   sulwd = object@Prefs@Graph.prefs$sulwd,   sulty = object@Prefs@Graph.prefs$sulty,   sucol = object@Prefs@Graph.prefs$sucol,   suspan = object@Prefs@Graph.prefs$suspan,   sudegr = object@Prefs@Graph.prefs$sudegr,   grid = object@Prefs@Graph.prefs$grid,   logy = FALSE,   logx = FALSE,   force.x.continuous = FALSE,   bwhoriz = object@Prefs@Graph.prefs$bwhoriz,   bwratio = object@Prefs@Graph.prefs$bwratio,   bwvarwid = object@Prefs@Graph.prefs$bwvarwid,   bwdotpch = object@Prefs@Graph.prefs$bwdotpch,   bwdotcol = object@Prefs@Graph.prefs$bwdotcol,   bwdotcex = object@Prefs@Graph.prefs$bwdotcex,   bwreccol = object@Prefs@Graph.prefs$bwreccol,   bwrecfill = object@Prefs@Graph.prefs$bwrecfill,   bwreclty = object@Prefs@Graph.prefs$bwreclty,   bwreclwd = object@Prefs@Graph.prefs$bwreclwd,   bwumbcol = object@Prefs@Graph.prefs$bwumbcol,   bwumblty = object@Prefs@Graph.prefs$bwumblty,   bwumblwd = object@Prefs@Graph.prefs$bwumblwd,   bwoutcol = object@Prefs@Graph.prefs$bwoutcol,   bwoutcex = object@Prefs@Graph.prefs$bwoutcex,   bwoutpch = object@Prefs@Graph.prefs$bwoutpch,   autocorr = FALSE,   vline = NULL,   vllwd = 3,   vllty = 2,   vlcol = \"grey\",   hline = NULL,   hllwd = 3,   hllty = 1,   hlcol = \"grey\",   pch.ip.sp = pch,   cex.ip.sp = cex,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default panel function for Xpose 4 — xpose.panel.default","text":"x Name(s) x-variable. y Name(s) y-variable. object xpose.data object. subscripts standard Trellis subscripts argument (see xyplot) groups Name variable used superpose plots. grp.col Logical value indicating whether use colour highlighting groups specified. NULL means highlighting, TRUE identify group members colour. iplot individual plots matrix? Internal use . inclZeroWRES Logical value indicating whether rows WRES=0 included plot. onlyfirst Logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. xvarnam Character string name x-variable. yvarnam Character string name y-variable. PI Either \"lines\", \"area\" \"\" specifying whether prediction intervals (lines, shaded area ) computed data SData added display. NULL means prediction interval. PI.subset subset used PI. PI.bin.table table used create VPC plots.  specific format created read.npc.vpc.results PI.real Plot percentiles real data various bins. values can NULL TRUE.  Note bin actual observations percentiles approximate.  example, 95th percentile 4 data points always largest 4 data points. PI.mirror Plot percentiles one simulated data set bin. values allowed NULL, TRUE .INTEGER.VALUE. TRUE takes first mirror PI.bin.table .INTEGER.VALUE can 1, 2, ...{} n n number mirror's output PI.bin.table.  Used mainly xpose.VPC. PI.ci Plot prediction interval simulated data's percentiles bin. Values can \"\", \"area\" \"lines\" can thought prediction interval PI.real confidence interval PI.  However, note increasing number simulations CI go towards zero interval also dependent size data set. PPI plot prediction interval. specific format must followed.  See setup.PPI. PI.mean mean plotted VPCs? TRUE FALSE. PI.delta.mean delta mean plotted VPCs? TRUE FALSE. PI.x.median x-location percentile lines bin  marked median x-values? (TRUE FALSE) PI.rug markings plot showing binning intervals  VPC  (locations independent variable used VPC calculation binning used)? PI.rug.col Color PI.rug. PI.rug.lwd Linw width PI.rug. PI.identify.outliers outlying percentiles real data highlighted? (TRUE FALSE) PI.outliers.col Color PI.identify.outliers points PI.outliers.pch pch PI.identify.outliers points PI.outliers.cex cex PI.identify.outliers points PI.limits vector two values describe limits prediction interval displayed.  example c(0.025, 0.975).  limits found PI.bin.table table. limits also used percentages PI.real, PI.mirror PI.ci.  However, confidence interval PI.ci always one defined PI.bin.table. PI.arcol color PI area PI..lty upper line type. can \"dotted\" \"dashed\", etc. PI..type upper type used plotting.  Defaults line. PI..col upper line color PI..lwd upper line width PI..lty lower line type. can \"dotted\" \"dashed\", etc. PI..type lower type used plotting.  Defaults line. PI..col lower line color PI..lwd lower line width PI.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.med.type median type used plotting.  Defaults line. PI.med.col median line color PI.med.lwd median line width PI.mean.lty mean line type. can \"dotted\" \"dashed\", etc. PI.mean.type mean type used plotting.  Defaults line. PI.mean.col mean line color PI.mean.lwd mean line width PI.delta.mean.lty delta.mean line type. can \"dotted\" \"dashed\", etc. PI.delta.mean.type delta.mean type used plotting.  Defaults line. PI.delta.mean.col delta.mean line color PI.delta.mean.lwd delta.mean line width PI.real..lty upper line type. can \"dotted\" \"dashed\", etc. PI.real..type upper type used plotting.  Defaults line. PI.real..col upper line color PI.real..lwd upper line width PI.real..lty lower line type. can \"dotted\" \"dashed\", etc. PI.real..type lower type used plotting.  Defaults line. PI.real..col lower line color PI.real..lwd lower line width PI.real.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.real.med.type median type used plotting.  Defaults line. PI.real.med.col median line color PI.real.med.lwd median line width PI.real.mean.lty mean line type. can \"dotted\" \"dashed\", etc. PI.real.mean.type mean type used plotting.  Defaults line. PI.real.mean.col mean line color PI.real.mean.lwd mean line width PI.real.delta.mean.lty delta.mean line type. can \"dotted\" \"dashed\", etc. PI.real.delta.mean.type delta.mean type used plotting. Defaults line. PI.real.delta.mean.col delta.mean line color PI.real.delta.mean.lwd delta.mean line width PI.mirror..lty upper line type. can \"dotted\" \"dashed\", etc. PI.mirror..type upper type used plotting.  Defaults line. PI.mirror..col upper line color PI.mirror..lwd upper line width PI.mirror..lty lower line type. can \"dotted\" \"dashed\", etc. PI.mirror..type lower type used plotting.  Defaults line. PI.mirror..col lower line color PI.mirror..lwd lower line width PI.mirror.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.mirror.med.type median type used plotting.  Defaults line. PI.mirror.med.col median line color PI.mirror.med.lwd median line width PI.mirror.mean.lty mean line type. can \"dotted\" \"dashed\", etc. PI.mirror.mean.type mean type used plotting.  Defaults line. PI.mirror.mean.col mean line color PI.mirror.mean.lwd mean line width PI.mirror.delta.mean.lty delta.mean line type. can \"dotted\" \"dashed\", etc. PI.mirror.delta.mean.type delta.mean type used plotting. Defaults line. PI.mirror.delta.mean.col delta.mean line color PI.mirror.delta.mean.lwd delta.mean line width PI.ci..arcol color upper PI.ci. PI.ci..lty upper line type. can \"dotted\" \"dashed\", etc. PI.ci..type upper type used plotting.  Defaults line. PI.ci..col upper line color PI.ci..lwd upper line width PI.ci..arcol color lower PI.ci. PI.ci..lty lower line type. can \"dotted\" \"dashed\", etc. PI.ci..type lower type used plotting.  Defaults line. PI.ci..col lower line color PI.ci..lwd lower line width PI.ci.med.arcol color median PI.ci. PI.ci.med.lty median line type. can \"dotted\" \"dashed\", etc. PI.ci.med.type median type used plotting.  Defaults line. PI.ci.med.col median line color PI.ci.med.lwd median line width PI.ci.mean.arcol color mean PI.ci. PI.ci.mean.lty mean line type. can \"dotted\" \"dashed\", etc. PI.ci.mean.type mean type used plotting.  Defaults line. PI.ci.mean.col mean line color PI.ci.mean.lwd mean line width PI.ci.delta.mean.arcol color delta.mean PI.ci. PI.ci.delta.mean.lty delta.mean line type. can \"dotted\" \"dashed\", etc. PI.ci.delta.mean.type delta.mean type used plotting. Defaults line. PI.ci.delta.mean.col delta.mean line color PI.ci.delta.mean.lwd delta.mean line width PI.ci.area.smooth \"area\" PI.ci smoothed match \"lines\" argument? Allowed values TRUE/FALSE. \"area\" set default show bins used PI.ci computation.  smoothing, information lost , general, confidence intervals smaller reality. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. col color lines points. Specified integer text string. full list obtained R command colours(). default blue (col=4). pch plotting character, symbol, use. Specified integer. See R help points. default open circle. cex amount plotting text symbols scaled relative default. 'NULL' 'NA' equivalent '1.0'. lty line type. Line types can either specified integer (0=blank, 1=solid, 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash) one character strings '\"blank\"', '\"solid\"', '\"dashed\"', '\"dotted\"', '\"dotdash\"', '\"longdash\"', '\"twodash\"', '\"blank\"' uses 'invisible lines' (.e., draw ). lwd width lines. Specified integer. default 1. fill fill areas plot ids Logical value specifying whether label data points. idsmode Determines way text labels added plots. NULL means extreme points labelled. Non-NULL means data points labelled. (See link{xpose.plot.default}) idsext specifies extent extremes used labelling points. default 0.05 (extreme 5% points labelled). idscex amount labels scaled relative default. 'NULL' 'NA' equivalent '1.0'. idsdir string indicating directions extremes include labelling. Possible values \"\", \"\" \"\". abline Vector arguments panel.abline function. abline drawn NULL. abllwd Line width abline. abllty Line type abline. ablcol Line colour abline. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. smlwd Line width x-y smooth. smlty Line type x-y smooth. smcol Line color x-y smooth. smspan smoothness parameter x-y smooth. default 0.667. argument panel.loess. smdegr degree polynomials used x-y smooth, 2. default 1. argument panel.loess. smooth..groups smooth group drawn? lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) lmlwd Line width lmline. lmlty Line type lmline. lmcol Line colour lmline. suline NULL value indicates superposed line added graph. non-NULL vector (length y) data points used smoothed superposed line. sulwd Line width superposed smooth. sulty Line type superposed smooth. sucol Line color superposed smooth. suspan smoothness parameter. default 0.667. argument panel.loess. sudegr degree polynomials used, 2. default 1. argument panel.loess. grid logical value indicating whether visual reference grid added graph. (use arguments line type, color etc). logy Logical value indicating whether y-axis logarithmic. logx Logical value indicating whether y-axis logarithmic. force.x.continuous Logical value indicating whether x-values taken continuous, even categorical. bwhoriz logical value indicating whether box whiskers horizontal . default FALSE. bwratio Ratio box height inter-box space. default 1.5. argument panel.bwplot. bwvarwid Logical. TRUE, widths boxplots proportional number points used creating . default FALSE. argument panel.bwplot. bwdotpch Graphical parameter controlling dot plotting character boxplots. 'bwdotpch=\"|\"' treated specially, replacing dot line. default 16. argument panel.bwplot. bwdotcol Graphical parameter controlling dot colour boxplots - integer string. See 'col'. default black. argument panel.bwplot. bwdotcex amount plotting text symbols scaled relative default boxplots. 'NULL' 'NA' equivalent '1.0'. argument panel.bwplot. bwreccol colour use box rectangle boxplots - integer string.  default blue. See trellis.par.get \"box.rectangle\". bwrecfill colour use filling box rectangle boxplots - integer string. default transparent (none). See trellis.par.get \"box.rectangle\". bwreclty line type box rectangle boxplots - integer string.  default solid. See trellis.par.get \"box.rectangle\". bwreclwd width lines box rectangle boxplots - integer. default 1. See trellis.par.get \"box.rectangle\". bwumbcol colour use umbrellas boxplots - integer string.  default blue. See trellis.par.get \"box.umbrella\". bwumblty line type umbrellas boxplots - integer string. default solid.See trellis.par.get \"box.umbrella\". bwumblwd width lines umbrellas boxplots - integer. default 1. See trellis.par.get \"box.umbrella\". bwoutcol colour use outliers boxplots - integer string.  default blue. See trellis.par.get \"box.symbol\". bwoutcex amount outlier points scaled relative default boxplots. 'NULL' 'NA' equivalent '1.0'. default 0.8. See trellis.par.get \"box.symbol\". bwoutpch plotting character, symbol, use outlier points boxplots.  Specified integer. See R help 'points'. default open circle. See trellis.par.get \"box.symbol\". autocorr autocorrelation plot?  Values can TRUE/FALSE. vline Add vertical line plot values specified. vllwd Width (lwd) vertical line vllty Line type (lty) vertical line vlcol Color (col) vertical line hline Add horizontal line plot values specified. hllwd Width (lwd) horizontal line hllty Line type (lty) horizontal line hlcol Color (col) horizontal line pch.ip.sp panel just one observation specifies type points DV, IPRED PRED respectively. cex.ip.sp panel just one observation specifies size points DV, IPRED PRED respectively. ... arguments may needed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.default.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default panel function for Xpose 4 — xpose.panel.default","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","title":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","text":"histogram panel function Xpose 4. intended ised outside xpose.plot.histogram function. arguments take default values xpose.data object can overridden supplying argument xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","text":"","code":"xpose.panel.histogram(   x,   object,   breaks = NULL,   dens = TRUE,   hidlty = object@Prefs@Graph.prefs$hidlty,   hidcol = object@Prefs@Graph.prefs$hidcol,   hidlwd = object@Prefs@Graph.prefs$hidlwd,   hiborder = object@Prefs@Graph.prefs$hiborder,   hilty = object@Prefs@Graph.prefs$hilty,   hicol = object@Prefs@Graph.prefs$hicol,   hilwd = object@Prefs@Graph.prefs$hilwd,   math.dens = NULL,   vline = NULL,   vllwd = 3,   vllty = 1,   vlcol = \"grey\",   hline = NULL,   hllwd = 3,   hllty = 1,   hlcol = \"grey\",   bins.per.panel.equal = TRUE,   showMean = FALSE,   meanllwd = 3,   meanllty = 1,   meanlcol = \"orange\",   showMedian = FALSE,   medianllwd = 3,   medianllty = 1,   medianlcol = \"black\",   showPCTS = FALSE,   PCTS = c(0.025, 0.975),   PCTSllwd = 2,   PCTSllty = hidlty,   PCTSlcol = \"black\",   vdline = NULL,   vdllwd = 3,   vdllty = 1,   vdlcol = \"red\",   ...,   groups )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","text":"x Name(s) x-variable. object xpose.data object. breaks breakpoints histogram. dens Density plot top histogram? hidlty Density line type. hidcol Color density line. hidlwd Width density line. hiborder Colour bar borders. hilty Line type bar borders. hicol Fill colour bars. hilwd Width bar borders. math.dens density line drawn.  Values NULL TRUE. vline NULL vector locations vertical lines drawn.  example, vline=c(50,60) draw two vertical lines. function panel.abline used. vllwd Line width vertical lines defined vline. Can vector single value, example vllwd=2 vllwd=c(2,3). vllty Line type vertical lines defined vline. Can vector single value, example vllty=1 vllty=c(1,2). vlcol Line color vertical lines defined vline. Can vector single value, example vlcol=\"red\" vllty=c(\"red\",\"blue\"). hline NULL vector locations horizontal lines drawn.  example, hline=c(50,60) draw two horizontal lines. function panel.abline used. hllwd Line width horizontal lines defined hline. Can vector single value, example hllwd=2 hllwd=c(2,3). hllty Line type horizontal lines defined hline. Can vector single value, example hllty=1 hllty=c(1,2). hlcol Line color horizontal lines defined hline. Can vector single value, example hlcol=\"red\" hllty=c(\"red\",\"blue\"). bins.per.panel.equal Allow different bins different panels continuous data? TRUE FALSE. showMean mean data histogram shown? meanllwd Line width mean line. meanllty line type mean meanlcol Color mean line showMedian median data histogram shown vertical line? medianllwd line width median line. medianllty line type median line. medianlcol color median line. showPCTS percentiles data histogram shown? PCTS vector percentiles show.  Can length. PCTSllwd line width percentiles.  Can vector length PCTS. PCTSllty Line type percentiles.  Can vector length PCTS. PCTSlcol Color percentiles.  Can vector length PCTS. vdline vertical line different histogram. Must vector. vdllwd line widths vdllty line types vdlcol line colors ... arguments may needed function. groups used pass conditioning variable function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.histogram.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default histogram panel function for Xpose 4 — xpose.panel.histogram","text":"Andrew Hooker, Mats Karlsson, Justin Wilkins & E. Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"Default QQ panel function for Xpose 4 — xpose.panel.qq","title":"Default QQ panel function for Xpose 4 — xpose.panel.qq","text":"QQ panel function Xpose 4. intended used outside xpose.plot.qq function.  arguments take default values xpose.data object can overridden supplying argument xpose.plot.qq.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default QQ panel function for Xpose 4 — xpose.panel.qq","text":"","code":"xpose.panel.qq(   x,   object,   pch = object@Prefs@Graph.prefs$pch,   col = object@Prefs@Graph.prefs$col,   cex = object@Prefs@Graph.prefs$cex,   abllty = object@Prefs@Graph.prefs$abllty,   abllwd = object@Prefs@Graph.prefs$abllwd,   ablcol = object@Prefs@Graph.prefs$ablcol,   grid = object@Prefs@Graph.prefs$grid,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default QQ panel function for Xpose 4 — xpose.panel.qq","text":"x Name(s) x-variable. object xpose.data object. pch Plot character use. col Colour lines plot symbols. cex Amount scale plotting character . abllty Line type. abllwd Line width. ablcol Line colour. grid logical value indicating whether visual reference grid added graph. (use arguments line type, color etc). ... arguments may needed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default QQ panel function for Xpose 4 — xpose.panel.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.splom.html","id":null,"dir":"Reference","previous_headings":"","what":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","title":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","text":"scatterplot matrix panel function Xpose 4. intended ised outside xpose.plot.splom function. arguments take default values xpose.data object can overridden supplying argument xpose.plot.splom.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.splom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","text":"","code":"xpose.panel.splom(   x,   y,   object,   subscripts,   onlyfirst = TRUE,   inclZeroWRES = FALSE,   type = \"p\",   col = object@Prefs@Graph.prefs$col,   pch = object@Prefs@Graph.prefs$pch,   cex = object@Prefs@Graph.prefs$cex,   lty = object@Prefs@Graph.prefs$lty,   lwd = object@Prefs@Graph.prefs$lwd,   smooth = TRUE,   smlwd = object@Prefs@Graph.prefs$smlwd,   smlty = object@Prefs@Graph.prefs$smlty,   smcol = object@Prefs@Graph.prefs$smcol,   smspan = object@Prefs@Graph.prefs$smspan,   smdegr = object@Prefs@Graph.prefs$smdegr,   lmline = NULL,   lmlwd = object@Prefs@Graph.prefs$lmlwd,   lmlty = object@Prefs@Graph.prefs$lmlty,   lmcol = object@Prefs@Graph.prefs$lmcol,   grid = object@Prefs@Graph.prefs$grid,   groups = NULL,   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.splom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","text":"x Name(s) x-variable. y Name(s) y-variable. object xpose.data object. subscripts standard Trellis subscripts argument (see xyplot) onlyfirst Logical value indicating whether first row per individual included plot. inclZeroWRES Logical value indicating whether rows WRES=0 included plot. type 1-character string giving type plot desired.  following values possible, details, see 'plot': '\"p\"' points, '\"l\"' lines, '\"o\"' -plotted points lines, '\"b\"', '\"c\"') (empty '\"c\"') points joined lines, '\"s\"' '\"S\"' stair steps '\"h\"' histogram-like vertical lines.  Finally, '\"n\"' produce points lines. col color lines points. Specified integer text string. full list obtained R command colours(). default blue (col=4). pch plotting character, symbol, use. Specified integer. See R help points. default open circle. cex amount plotting text symbols scaled relative default. 'NULL' 'NA' equivalent '1.0'. lty line type. Line types can either specified integer (0=blank, 1=solid, 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash) one character strings '\"blank\"', '\"solid\"', '\"dashed\"', '\"dotted\"', '\"dotdash\"', '\"longdash\"', '\"twodash\"', '\"blank\"' uses 'invisible lines' (.e., draw ). lwd width lines. Specified integer. default 1. smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. smlwd Line width x-y smooth. smlty Line type x-y smooth. smcol Line color x-y smooth. smspan smoothness parameter x-y smooth. default 0.667. argument panel.loess. smdegr degree polynomials used x-y smooth, 2. default 1. argument panel.loess. lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) lmlwd Line width lmline. lmlty Line type lmline. lmcol Line colour lmline. grid logical value indicating whether visual reference grid added graph. (use arguments line type, color etc). groups Name variable used superpose plots. ... arguments may needed function.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.panel.splom.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scatterplot matrix panel function for Xpose 4 — xpose.panel.splom","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":null,"dir":"Reference","previous_headings":"","what":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"wrapper function lattice bwplot function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"","code":"xpose.plot.bw(   x,   y,   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   panel = xpose.panel.bw,   groups = NULL,   ids = FALSE,   logy = FALSE,   logx = FALSE,   aspect = object@Prefs@Graph.prefs$aspect,   funy = NULL,   funx = NULL,   PI = FALSE,   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   ordby = object@Prefs@Graph.prefs$ordby,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   subset = xsubset(object),   main = xpose.create.title(x, y, object, subset, funx, funy, ...),   xlb = xpose.create.label(x, object, funx, logx, ...),   ylb = xpose.create.label(y, object, funy, logy, ...),   scales = list(),   suline = object@Prefs@Graph.prefs$suline,   binvar = NULL,   bins = 10,   mirror = FALSE,   max.plots.per.page = 4,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"x Name(s) x-variable. y Name(s) y-variable. object xpose.data object. inclZeroWRES logical value indicating whether rows WRES=0 plotted. onlyfirst logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. panel name panel function use. cases left xpose.panel.bw. groups string name grouping variable (used groups argument panel.xyplot. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab Xpose data variable). logy Logical value indicating whether y-axis logarithmic. logx Logical value indicating whether x-axis logarithmic. aspect aspect ratio display (see bwplot). funy String name function apply y-variable plotting, e.g. \"abs\". funx String name function apply x-variable plotting, e.g. \"abs\". PI Either \"lines\", \"area\" \"\" specifying whether prediction intervals (lines, shaded area ) computed data SData added display. NULL means prediction interval. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. ordby string name variable used reorder factor conditioning variables (). variable used call reorder function. byordfun name function used reordering factor conditioning variable (see argument ordby). shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum) strip name function used strip argument bwplot. subset string giving subset expression applied data plotting. See xsubset. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. scales list used scales argument bwplot. suline string giving variable used construct smooth superpose display. NULL none. argument used want add superpose line variable present y list variables. binvar Variable used binning. bins number bins used. default 10. mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. max.plots.per.page maximum number plots per page can created mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument bwplot used. ... arguments passed xpose.panel.bw.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.bw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The generic Xpose functions for box-and-whisker plots — xpose.plot.bw","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## Box & whisker plot of WRES vs PRED xpose.plot.bw(\"WRES\", \"PRED\", xpdb5, binvar=\"PRED\") }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":null,"dir":"Reference","previous_headings":"","what":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"function wrapper lattice xyplot function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"","code":"xpose.plot.default(   x,   y,   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   panel = xpose.panel.default,   groups = object@Prefs@Xvardef$id,   ids = object@Prefs@Graph.prefs$ids,   logy = FALSE,   logx = FALSE,   yscale.components = \"default\",   xscale.components = \"default\",   aspect = object@Prefs@Graph.prefs$aspect,   funx = NULL,   funy = NULL,   iplot = NULL,   PI = NULL,   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   ordby = object@Prefs@Graph.prefs$ordby,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   by.interval = NULL,   strip = function(...) {      strip.default(..., strip.names = c(TRUE, TRUE))  },   use.xpose.factor.strip.names = TRUE,   subset = xsubset(object),   autocorr = FALSE,   main = xpose.create.title(x, y, object, subset, funx, funy, ...),   xlb = xpose.create.label(x, object, funx, logx, autocorr.x = autocorr, ...),   ylb = xpose.create.label(y, object, funy, logy, autocorr.y = autocorr, ...),   scales = list(),   suline = object@Prefs@Graph.prefs$suline,   bwhoriz = object@Prefs@Graph.prefs$bwhoriz,   dilution = FALSE,   dilfrac = object@Prefs@Graph.prefs$dilfrac,   diltype = object@Prefs@Graph.prefs$diltype,   dilci = object@Prefs@Graph.prefs$dilci,   seed = NULL,   mirror = FALSE,   max.plots.per.page = 4,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"x string vector strings name(s) x-variable(s). y string vector strings name(s) y-variable(s). object \"xpose.data\" object. inclZeroWRES logical value indicating whether rows WRES=0 plotted. onlyfirst logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. panel name panel function use. groups string name grouping variable (used groups argument panel.xyplot. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). logy Logical value indicating whether y-axis logarithmic. logx Logical value indicating whether x-axis logarithmic. yscale.components Used change way axis look logy used. Can user defined function link{xpose.yscale.components.log10}.  axes log transformed yscale.components.default used. xscale.components Used change way axis look logx used. Can user defined function link{xpose.xscale.components.log10}.  axes log transformed xscale.components.default used. aspect aspect ratio display (see xyplot). funx String name function apply x-variable plotting, e.g. \"abs\". funy String name function apply y-variable plotting, e.g. \"abs\". iplot individual plots matrix? Internal use . PI Either \"lines\", \"area\" \"\" specifying whether prediction intervals (lines, shaded area ) computed data SData added display. NULL means prediction interval. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. ordby string name variable used reorder factor conditioning variables (). variable used call reorder.factor function. byordfun name function used reordering factor conditioning variable (see argument ordby) shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum) .interval intervals use conditioning continuous variable . strip name function used strip argument xyplot. easy way change strip appearance use strip.custom.  example, want change text strips can use strip=strip.custom(factor.levels=c(\"Hi\",\"\")) variable factor strip=strip.custom(var.name=c(\"New Name\")) variable continuous. use.xpose.factor.strip.names Use factor names strips conditioning plots.. subset string giving subset expression applied data plotting. See xsubset. autocorr autocorrelation plot?  Values can TRUE/FALSE. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. scales list used scales argument xyplot. suline string giving variable used construct smooth superpose display. NULL none. argument used want add superpose line variable present y list variables. bwhoriz logical value indicating box whiskers bars plotted horizontally . Used x-variable(s) categorical. dilution Logical value indicating whether data dilution used. dilfrac Dilution fraction indicating expected fraction individuals display plots. exact meaning depends type dilution (see ). diltype Indicating type dilution apply. NULL means random dilution without stratification. nonNULL value means stratified dilution. dilci number 0 1 giving range eligible dilution stratified dilution (see ). seed Seed number used random dilution. NULL means seed. mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. max.plots.per.page maximum number plots per page can created mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument xyplot used. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"Returns xyplot graph object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"y must numeric (continuous) x can either numeric factor. x numeric regular xy-plot drawn. x factor, hand, box whiskers plot constructed. x y can either single valued strings vector strings. x y can vectors call function. ids TRUE, text labels added plotting symbols. labels taken idlab xpose data variable. way text labels plotted governed idsmode argument (passed panel function). idsmode=NULL (default) means extreme data points labelled non-NULL value adds labels data points (default Xpose 3). xpose.panel.default identifies extreme data points fitting loess smooth (y~x) looking residuals fit. Points associated highest/lowest residuals labelled. \"High\" \"low\" judged panel function parameter idsext, gives fraction total number data points judged extreme \"\" \"\" direction. default value idsext 0.05 (see xpose.prefs-class). also possibility label high low extreme points. done idsdir argument xpose.panel.default. value \"\" (default) means high low extreme points labelled \"\" \"\" labels high low extreme points respectively. Data dilution useful situations excessive amount data. xpose.plot.default can dilute data two different ways. first completely random dilution individuals eligible exclusion plot. case argument dilfrac determines fraction individuals excluded plot. second type dilution uses stratification make sure none extreme individuals omitted plot. Extreme individuals identified similar manner extreme data points identified text labelling. smooth fitted data extreme residuals fit used inform extremeness. judged extreme determined argument dilci, defaults 0.95 (Note meaning opposite idsext). dilci give confidence level interval around fitted curve outside points deemed extreme. Extreme individuals least one point \"extremeness\" interval. Individuals extreme points eligible dilution dilfrac give number omitted graph. means dilfrac usually grater stratified dilution completely random dilution. smooths added diluted plot based undiluted data. graphical parameters may passed xpose.panel.default.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.default.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.default","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## A spaghetti plot of DV vs TIME xpose.plot.default(\"TIME\", \"DV\", xpdb5)   ## A conditioning plot xpose.plot.default(\"TIME\", \"DV\", xpdb5, by = \"SEX\")   ## Multiple x-variables xpose.plot.default(c(\"WT\", \"SEX\"), \"CL\", xpdb5)  ## Multiple y-variables xpose.plot.default(\"WT\", c(\"CL\", \"V\"), xpdb5) xpose.plot.default(\"WT\", c(\"CL\", \"V\"), xpdb5, by=c(\"SEX\", \"HCTZ\"))  ## determining the interval for the conditioning variable wt.ints <- matrix(c(50,60,60,70,70,80,80,90,90,100,100,150),nrow=6,ncol=2,byrow=T) xpose.plot.default(\"TIME\",\"DV\",xpdb5,by=\"WT\", by.interval=wt.ints) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"function wrapper lattice xyplot function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"","code":"xpose.plot.histogram(   x,   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   type = \"density\",   aspect = object@Prefs@Graph.prefs$aspect,   scales = list(),   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   ordby = object@Prefs@Graph.prefs$ordby,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   subset = xsubset(object),   main = xpose.create.title.hist(x, object, subset, ...),   xlb = NULL,   ylb = \"Density\",   hicol = object@Prefs@Graph.prefs$hicol,   hilty = object@Prefs@Graph.prefs$hilty,   hilwd = object@Prefs@Graph.prefs$hilwd,   hidcol = object@Prefs@Graph.prefs$hidcol,   hidlty = object@Prefs@Graph.prefs$hidlty,   hidlwd = object@Prefs@Graph.prefs$hidlwd,   hiborder = object@Prefs@Graph.prefs$hiborder,   mirror = FALSE,   max.plots.per.page = 4,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"x string vector strings name(s) x-variable(s). object \"xpose.data\" object. inclZeroWRES logical value indicating whether rows WRES=0 plotted. onlyfirst logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. type type histogram make.  See histogram. aspect aspect ratio display (see histogram). scales list used scales argument histogram. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. ordby string name variable used reorder factor conditioning variables (). variable used call reorder.factor function. byordfun name function used reordering factor conditioning variable (see argument ordby) shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum) strip name function used strip argument xyplot. subset string giving subset expression applied data plotting. See xsubset. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. hicol fill colour histogram - integer string.  default blue (see histogram). hilty border line type histogram - integer.  default 1 (see histogram). hilwd border line width histogram - integer.  default 1 (see histogram). hidcol fill colour density line - integer string. default black (see histogram). hidlty border line type density line - integer.  default 1 (see histogram). hidlwd border line width density line - integer.  default 1 (see histogram). hiborder border colour histogram - integer string. default black (see histogram). mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. max.plots.per.page maximum number plots per page can created mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument xyplot used. ... arguments passed xpose.plot.histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"Returns histogram.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"x can either numeric factor, can either single valued strings vectors strings.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.histogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Xpose 4 generic functions for continuous y-variables. — xpose.plot.histogram","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  xpose.plot.histogram(\"AGE\", xpdb5, onlyfirst = TRUE) xpose.plot.histogram(c(\"SEX\", \"AGE\"), xpdb5, onlyfirst = TRUE)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":null,"dir":"Reference","previous_headings":"","what":"The generic Xpose functions for QQ plots — xpose.plot.qq","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"wrapper function lattice qqmath function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"","code":"xpose.plot.qq(   x,   object,   inclZeroWRES = FALSE,   onlyfirst = FALSE,   samp = NULL,   aspect = object@Prefs@Graph.prefs$aspect,   scales = list(),   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   ordby = object@Prefs@Graph.prefs$ordby,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   subset = xsubset(object),   main = xpose.create.title.hist(x, object, subset, ...),   xlb = \"Quantiles of Normal\",   ylb = paste(\"Quantiles of \", xlabel(x, object), sep = \"\"),   pch = object@Prefs@Graph.prefs$pch,   col = object@Prefs@Graph.prefs$col,   cex = object@Prefs@Graph.prefs$cex,   abllty = object@Prefs@Graph.prefs$abllty,   abllwd = object@Prefs@Graph.prefs$abllwd,   ablcol = object@Prefs@Graph.prefs$ablcol,   mirror = FALSE,   max.plots.per.page = 4,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"x string vector strings name(s) x-variable(s). object \"xpose.data\" object. inclZeroWRES logical value indicating whether rows WRES=0 plotted. onlyfirst logical value indicating whether first row per individual included plot. samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. aspect aspect ratio display (see qqmath). scales list used scales argument qqmath. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. ordby string name variable used reorder factor conditioning variables (). variable used call reorder function. byordfun name function used reordering factor conditioning variable (see argument ordby). shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum). strip name function used strip argument xyplot. subset string giving subset expression applied data plotting. See xsubset. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. pch Plotting symbol. col Color plotting symbol. cex Amount scale plotting character . abllty Line type qqline. abllwd Line width qqline. ablcol Color qqline. mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. max.plots.per.page maximum number plots per page can created mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument qqmath used. ... arguments passed xpose.plot.qq.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.qq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The generic Xpose functions for QQ plots — xpose.plot.qq","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## A QQ plot of WRES xpose.plot.qq(\"WRES\", xpdb5)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":null,"dir":"Reference","previous_headings":"","what":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"function wrapper lattice splom function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"","code":"xpose.plot.splom(   plist,   object,   varnames = NULL,   main = \"Scatterplot Matrix\",   xlb = NULL,   ylb = NULL,   scales = list(),   onlyfirst = TRUE,   inclZeroWRES = FALSE,   subset = xsubset(object),   by = object@Prefs@Graph.prefs$condvar,   force.by.factor = FALSE,   include.cat.vars = FALSE,   ordby = NULL,   byordfun = object@Prefs@Graph.prefs$byordfun,   shingnum = object@Prefs@Graph.prefs$shingnum,   shingol = object@Prefs@Graph.prefs$shingol,   strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),   groups = NULL,   ids = object@Prefs@Graph.prefs$ids,   smooth = TRUE,   lmline = NULL,   panel = xpose.panel.splom,   aspect = object@Prefs@Graph.prefs$aspect,   samp = NULL,   max.plots.per.page = 4,   mirror = FALSE,   mirror.aspect = \"fill\",   pass.plot.list = FALSE,   x.cex = NULL,   y.cex = NULL,   main.cex = NULL,   mirror.internal = list(strip.missing = missing(strip)),   ... )"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"plist vector strings containing variable names scatterplot matrix. object \"xpose.data\" object. varnames vector strings containing labels variables scatterplot matrix. main string giving plot title NULL none. xlb string giving label x-axis. NULL none. ylb string giving label y-axis. NULL none. scales list used scales argument xyplot. onlyfirst logical value indicating whether first row per individual included plot. inclZeroWRES logical value indicating whether rows WRES=0 plotted. subset string giving subset expression applied data plotting. See xsubset. string vector strings name(s) conditioning variables. force..factor Logical value. TRUE, NULL, variable specified taken categorical. include.cat.vars Logical value. ordby string name variable used reorder factor conditioning variables (). variable used call reorder.factor function. byordfun name function used reordering factor conditioning variable (see argument ordby) shingnum number shingles (\"parts\") continuous conditioning variable divided . shingol amount overlap adjacent shingles (see argument shingnum) strip name function used strip argument xyplot. groups string name grouping variable (used groups argument panel.xyplot. ids logical value indicating whether text labels used plotting symbols (variable used symbols indicated idlab xpose data variable). smooth NULL value indicates superposed line added graph. TRUE smooth data superimposed. lmline logical variable specifying whether linear regression line superimposed xyplot. NULL ~ FALSE. (y~x) panel name panel function use. aspect aspect ratio display (see xyplot). samp integer 1 object@Nsim (seexpose.data-class) specifying simulated data sets extract SData. max.plots.per.page maximum number plots per page can created mirror plots. mirror create mirror plots simulation data?  Value can FALSE, TRUE 1 one mirror plot, 3 three mirror plots. mirror.aspect aspect ratio plots used mirror functionality. pass.plot.list pass list plots created mirror print directly.  Values can TRUE/FALSE. x.cex size x-axis label. y.cex size y-axis label. main.cex size title. mirror.internal internal mirror argument used create.mirror.  Checks strip argument qqmath used. ... arguments passed xpose.panel.default.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"Returns scatterplot matrix graph object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"ids TRUE, text labels added plotting symbols. labels taken idlab xpose data variable. way text labels plotted governed idsmode argument (passed panel function). idsmode=NULL (default) means extreme data points labelled non-NULL value adds labels data points (default Xpose 3). xpose.panel.default identifies extreme data points fitting loess smooth (y~x) looking residuals fit. Points associated highest/lowest residuals labelled. \"High\" \"low\" judged panel function parameter idsext, gives fraction total number data points judged extreme \"\" \"\" direction. default value idsext 0.05 (see link{xpose.prefs-class}). also possibility label high low extreme points. done idsdir argument xpose.panel.default. value \"\" (default) means high low extreme points labelled \"\" \"\" labels high low extreme points respectively. graphical parameters may passed xpose.panel.splom. example, want adjust size varnames axis tick labels can use parameters varname.cex=0.5 axis.text.cex=0.5.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.plot.splom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Xpose 4 generic functions for scatterplot matrices. — xpose.plot.splom","text":"","code":"if (FALSE) { ## xpdb5 is an Xpose data object ## We expect to find the required NONMEM run and table files for run ## 5 in the current working directory xpdb5 <- xpose.data(5)  ## CL, WT, HT, SEX with a regression line xpose.plot.splom(c(\"CL\", \"WT\", \"HT\", \"SEX\"), xpdb5, lmline = TRUE)  }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.prefs-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"object \"xpose.prefs\" class holds information variable graphical preferences particular \"xpose.data\" object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.prefs-class.html","id":"objects-from-the-class","dir":"Reference","previous_headings":"","what":"Objects from the Class","title":"Class ","text":"Objects can created calls form new(\"xpose.prefs\",...) usually necessary since \"xpose.prefs\" object created time \"xpose.data\" object.","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.prefs-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Class ","text":"Niclas Jonsson & Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize an xpose database — xpose.print","title":"Summarize an xpose database — xpose.print","text":"Summarize xpose database","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize an xpose database — xpose.print","text":"","code":"xpose.print(object, long = TRUE)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize an xpose database — xpose.print","text":"object xpose data object long long format .","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize an xpose database — xpose.print","text":"\"\"","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.print.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize an xpose database — xpose.print","text":"","code":"xpose.print(simpraz.xpdb) #> The database contains the following observed items: #> ID TIME IPRED IWRES CWRES CL V KA ETA1 ETA2 ETA3 AGE HT WT  #> SECR SEX RACE SMOK HCTZ PROP CON OCC DV PRED RES WRES #>  #> The following variables are defined: #>  #> ID variable: ID  #> Label variable: ID  #> Independent variable: TIME  #> Occasion variable: OCC  #> Dependent variable: DV  #> Population prediction variable: PRED  #> Individual prediction variable: IPRED  #> Weighted population residual variable: WRES  #> Weighted individual residual variable: IWRES  #> Population residual variable: RES  #> Parameters: ETA3 ETA2 ETA1 KA V CL #> Covariates: SEX RACE SMOK HCTZ PROP CON OCC AGE HT WT SECR #>   ( Continuous: AGE HT WT SECR ) #>   ( Categorical: SEX RACE SMOK HCTZ PROP CON OCC ) #> Variability parameters: ETA1 ETA2 ETA3 #> Missing value label: -99"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.string.print.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a pretty string. — xpose.string.print","title":"Print a pretty string. — xpose.string.print","text":"Print string certain number characters per row.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.string.print.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a pretty string. — xpose.string.print","text":"","code":"xpose.string.print(value, fill = 60, file = \"\")"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.string.print.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a pretty string. — xpose.string.print","text":"value text print. fill wide text per row. file print. \"\" means screen.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.string.print.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a pretty string. — xpose.string.print","text":"Niclas Jonsson Andrew C. Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"functions used create standard tic marks axis labels axes log scale.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"","code":"xpose.logTicks(lim, loc = c(1, 5))  xpose.yscale.components.log10(lim, ...)  xpose.xscale.components.log10(lim, ...)"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"lim Limits loc Locations ... Additional arguments passed function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"functions create log scales look like (default R scales). functions used input xscale.components argument lattice plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"xpose.logTicks(): Make log tic marks xpose.xscale.components.log10(): Make log scale x-axis","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose.yscale.components.log10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions to create nice looking axes when using Log scales. — xpose.logTicks","text":"","code":"if (FALSE) { xpdb5 <- xpose.data(5) xpose.plot.default(\"PRED\",\"DV\",xpdb,logy=T,logx=T) xpose.plot.default(\"PRED\",\"DV\",xpdb,logy=T,logx=T,                    yscale.components = xpose.yscale.components.log10,                    xscale.components = xpose.xscale.components.log10)  ## both give the same result }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The Xpose Package — xpose4-package","title":"The Xpose Package — xpose4-package","text":"Xpose R-based model building aid population analysis using NONMEM.  facilitates data set checkout, exploration visualization, model  diagnostics, candidate covariate identification model comparison.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Xpose Package — xpose4-package","text":"Xpose takes output NONMEM output /PsN output generates graphs analyses. assumed NONMEM run can uniquely identified run number (see section generate appropriate input Xpose). Xpose implemented using lattice graphics library. Xpose package can divided six subsections (functions associated different subsections linked \"See Also\" section): Data Functions Functions managing          input data manipulating Xpose database. Generic Functions Generic wrapper functions around lattice          functions. functions can invoked user require quite          detailed instructions generate desired output. Specific Functions functions single purpose functions        generate specific output given Xpose database input.        behavior can, extent, influenced user. Classic Functions Xpose text based menu interface make       simple user invoke Xpose specific functions. interface       called Xpose Classic. Given limitations text based interface imposes,       Xpose Classic flexible may useful quick assessment       model learning use Xpose. PsN Functions functions interface Xpose        PsN, .e. post-process NONMEM output rather PsN output. GAM Functions Functions take Xpose object performs generalized additive model         (GAM) stepwise search influential covariates single model parameter.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"how-to-make-nonmem-generate-input-to-xpose","dir":"Reference","previous_headings":"","what":"How to make NONMEM generate input to Xpose","title":"The Xpose Package — xpose4-package","text":"Xpose recognizes NONMEM    runs, files associated particular run, though run number.    number used name NONMEM model files, output   files table files.  fundamental input Xpose one   NONMEM table files.  table files named followed   run number, example xptab1 run number 1.  Xpose looks files   according following pattern, * run number: sdtab* Standard table file, containing ID, IDV, DV, PRED, IPRED,    WRES, IWRES, RES, IRES, etc. patab* Parameter table, containing model parameters - THETAs, ETAs    EPSes catab* Categorical covariates, e.g. SEX, RACE cotab* Continuous covariates, e.g. WT, AGE extra*, mutab*, mytab*, xptab*, cwtab* variables might   need available Xpose run*.mod Model specification file run*.lst NONMEM output Strictly, one table file needed xpose (example sdtab*    xptab*).  However, using patab*, cotab*, catab* influence way   Xpose interprets data recommended get full benefit    Xpose. can use code NONMEM similar following generate tables    need.  NONMEM automatically appends DV, PRED, WRES RES unless    NOAPPEND specified.  forget leave least one blank line   end NONMEM model specification file. $TABLE ID TIME IPRED IWRES EVID MDV NOPRINT ONEHEADER FILE=sdtab1    $TABLE ID CL V2 KA K SLP KENZ NOPRINT ONEHEADER FILE=patab1    $TABLE ID WT HT AGE BMI PKG NOPRINT ONEHEADER FILE=cotab1    $TABLE ID SEX SMOK ALC NOPRINT ONEHEADER FILE=catab1","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Xpose Package — xpose4-package","text":"PsN","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The Xpose Package — xpose4-package","text":"E. Niclas Jonsson, Mats Karlsson, Justin Wilkins Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4-package.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Xpose Package — xpose4-package","text":"","code":"if (FALSE) { # run the classic interface library(xpose4) xpose4()    # command line interface   library(xpose4) xpdb <- xpose.data(5) basic.gof(xpdb) }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4.html","id":null,"dir":"Reference","previous_headings":"","what":"Classic menu system for Xpose 4 — xpose4","title":"Classic menu system for Xpose 4 — xpose4","text":"Classic menu system Xpose 4","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Classic menu system for Xpose 4 — xpose4","text":"","code":"xpose4()"},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Classic menu system for Xpose 4 — xpose4","text":"Andrew Hooker","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xpose4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Classic menu system for Xpose 4 — xpose4","text":"","code":"if (FALSE) { xpose4() }"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or set the value of the Subset slot. — xsubset","title":"Extract or set the value of the Subset slot. — xsubset","text":"Extract set value Subset slot \"xpose.data\" object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or set the value of the Subset slot. — xsubset","text":"","code":"xsubset(object)  xsubset(object) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or set the value of the Subset slot. — xsubset","text":"object \"xpose.data\" object. value string subset expression.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or set the value of the Subset slot. — xsubset","text":"string representing subset expression.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract or set the value of the Subset slot. — xsubset","text":"subset string syntax subset argument , e.g. panel.xyplot. Note, however, \"xpose.data\" subset used argument panel.xyplot. intended subset argument Data SData functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract or set the value of the Subset slot. — xsubset","text":"xsubset(object) <- value: assign value string representing subset expression","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or set the value of the Subset slot. — xsubset","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xsubset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or set the value of the Subset slot. — xsubset","text":"","code":"xpdb <- simpraz.xpdb xsubset(xpdb) <- \"DV > 0\" xsubset(xpdb) #> [1] \"DV > 0\""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract and set Xpose variable definitions. — xvardef","title":"Extract and set Xpose variable definitions. — xvardef","text":"function extracts set Xpose variable definitions \"xpose.data\" objects.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract and set Xpose variable definitions. — xvardef","text":"","code":"xvardef(x, object)  xvardef(object) <- value"},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract and set Xpose variable definitions. — xvardef","text":"x name xpose variable (see ). object xpose.data object. value two element vector first element name variable second column name Data slot object.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract and set Xpose variable definitions. — xvardef","text":"Returns string name data variable defined Xpose data variable.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract and set Xpose variable definitions. — xvardef","text":"Xpose variable definitions used map particular variable types column names data.frame Data slot \"xpose.data\" object. single-valued Xpose variable definitions : id, idlab, idv, occ, dv, pred, ipred, iwres, res. (potentially) vector-valued Xpose variable definitions : parms, covariates, ranpar, tvparms (parameters, covariates, random effects parameters=etas, typical value parameters). default values can found createXposeClasses function.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Extract and set Xpose variable definitions. — xvardef","text":"xvardef(object) <- value: reset column label dv points Data slot xpose database object","code":""},{"path":[]},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract and set Xpose variable definitions. — xvardef","text":"Niclas Jonsson","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/reference/xvardef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract and set Xpose variable definitions. — xvardef","text":"","code":"xpdb <- simpraz.xpdb  ## get the column name in the Data slot of object xpdb ## corresponding to the label dv xvardef(\"dv\", xpdb) #> [1] \"DV\"  ## reset the which column the label dv points to in the Data slot of ## object xpdb xvardef(xpdb) <- c(\"dv\", \"DVA\")"},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-471","dir":"Changelog","previous_headings":"","what":"xpose4 4.7.1","title":"xpose4 4.7.1","text":"CRAN release: 2020-12-18 Fix bug filtering “-99” rows table files filter plotting variables. Fix bug items catab cotab files added list covariates xpose database (#16). Fix bug csv files improperly read database situations (#16). Updated code using depreciated dplyr tibble functions.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-470","dir":"Changelog","previous_headings":"","what":"xpose4 4.7.0","title":"xpose4 4.7.0","text":"CRAN release: 2020-02-27 allow changes relative length censored lines kaplan.plot(). Handle directory without trailing slash xpose.data() (#14, @rikardn). fix bug classic menu system allowing change variable definitions. Various small spelling bug fixes (#13, @vrognas).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-461","dir":"Changelog","previous_headings":"","what":"xpose4 4.6.1","title":"xpose4 4.6.1","text":"CRAN release: 2018-03-08 Updates comply changes readr gam packages.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-460","dir":"Changelog","previous_headings":"","what":"xpose4 4.6.0","title":"xpose4 4.6.0","text":"CRAN release: 2017-06-17 Update xpose.VPC() outliers can identified plotted. Update xpose.VPC() lines VPC plotted median observed values X axis bins default. Update xpose.VPC() allow rug bottom plot showing bins located. Update namespace lattice loaded loading xpose. documentation now written roxygen Updates boot GAM boot SCM plots documentation. Various small bug fixes.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-453","dir":"Changelog","previous_headings":"","what":"xpose4 4.5.3","title":"xpose4 4.5.3","text":"CRAN release: 2014-11-24 Update ind.plots() allow subsets per-y-variable basis. Useful show IPRED PRED finer grid DV. See option “y.vals.subset”. Update axes limits computed xpose.plot.default. Fix using expression() ylb argument xpose.VPC. Various small bug fixes.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-452","dir":"Changelog","previous_headings":"","what":"xpose4 4.5.2","title":"xpose4 4.5.2","text":"Internal release Updates read.bootscm.par.est()","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-451","dir":"Changelog","previous_headings":"","what":"xpose4 4.5.1","title":"xpose4 4.5.1","text":"Internal release Updated xpose.gam work latest version gam package Updated kaplan.plot allow ylim specification “cov” argument used. Updated compute.cwres associated functions work NONMEM 7. Fixed warnings created xpose.VPC.categorical creating personalized x y axis labels.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-450","dir":"Changelog","previous_headings":"","what":"xpose4 4.5.0","title":"xpose4 4.5.0","text":"CRAN release: 2014-05-20 External release just one package instead five. Added ind.plots one data point individual, PRED IPRED show plot.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-443","dir":"Changelog","previous_headings":"","what":"xpose4 4.4.3","title":"xpose4 4.4.3","text":"Internal release Added functionality plotting delta mean output vpc tool PsN. Option xpose.VPC() can turned using PI.delta.mean=T. See ?xpose.panel.default information.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-442","dir":"Changelog","previous_headings":"","what":"xpose4 4.4.2","title":"xpose4 4.4.2","text":"Internal release Removed default messages print screen running xpose.VPC(). can change back previous behavior option verbose=TRUE. Combined five packages xpose one package. Updated Histogram functionality. New plots randtest.hist() boot.hist() creating histograms results PsN’s ‘randtest’ ‘bootstrap’ tools. Updated xpose.VPC() function handle plotting mean values simulations.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-441","dir":"Changelog","previous_headings":"","what":"xpose4 4.4.1","title":"xpose4 4.4.1","text":"CRAN release: 2013-08-13 Updates kaplan.plot.R (thanks Leonid Gibiansky reporting problems) kaplan.plot.R: Removed debugging command mistakenly left function kaplan.plot.R: “ylab” argument now passed plot cov option used. kaplan.plot.R: Using cov option repeated censoring observations break chain mean value calculation wrong (used surviving IDs last censored ID). Fixed now. Changed “aspect” argument plots default “fill”. Previously “1”.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-440","dir":"Changelog","previous_headings":"","what":"xpose4 4.4.0","title":"xpose4 4.4.0","text":"CRAN release: 2012-10-17 Added bootstrap GAM diagnostics boostrap PsN function boot_scm.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-436","dir":"Changelog","previous_headings":"","what":"xpose4 4.3.6","title":"xpose4 4.3.6","text":"fixed plot classic menu system “Weighted residuals vs covariates”.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-435","dir":"Changelog","previous_headings":"","what":"xpose4 4.3.5","title":"xpose4 4.3.5","text":"CRAN release: 2012-04-19 Updated help files workable examples, example dataset. Look data(simpraz.xpdb), simprazExample() example(xpose.data) dataset examples example(basic.gof) example(cwres.vs.idv) plot examples. xpose4specific functions now examples can run example(). Updated kaplan.plot() kaplan-Meier mean covariate (KMMC) plot can created. Also added options adjusting plot properties. New gofSetup() command create customized series GOF plots. fixed RSE values reported runsum() parameter fixed. Fixed argument xpose.VPC.categorical(max.plots.per.page=1), one plot per page possible. Fixed xpose.VPC() psn option vpc “confidence_interval=X” works. Fixed compute.cwres() function wasn’t computing anything (returning error).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-432","dir":"Changelog","previous_headings":"","what":"xpose4 4.3.2","title":"xpose4 4.3.2","text":"CRAN release: 2010-11-30 Fixed bug xpose.VPC asking logx=T (didn’t work previously). Fixed dOFV.vs.id ties individual dOFV drops.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-430","dir":"Changelog","previous_headings":"","what":"xpose4 4.3.0","title":"xpose4 4.3.0","text":"CRAN release: 2010-10-23 Updated read.nm.tables comma separated NONMEM 7 files can read Xpose. Changing behavior xpose.multiple.plot.default. Now multiple plots returned objects just like single plots (automatic printing function created plot list). accomplished defining new class - xpose.multiple.plots - corresponding print show methods class. Updated xpose.VPC, xpose.VPC.categorical xpose.VPC.handle new format PsN vpc_results.csv files. xpose.VPC.categorical now new option: censored (T F) create BLOQ VPC plots TRUE. xpose.VPC.tries combine continuous categorical BLOQ plots. page numbers can turned multiple page plots using page.numbers option (T F).","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-410","dir":"Changelog","previous_headings":"","what":"xpose4 4.1.0","title":"xpose4 4.1.0","text":"Updated ind.plots(), function much flexible now. Added graphical options xpose.VPC.categorical() Fixed logy=T option xpose.VPC(Pi.ci=T,logy=T). Fixed logy=T logx=T option (bug resulting error). VPC changed require y-axis continuous default. Fixed classic version parm.vs.parm() plot. Fixed runsum(). Previous version line line model file. Added new function change.xvardef(), replaces much previous change functions. Thanks Sebastien Bihorel input helped create function. Added ability apply functions x-axis plots. function options now called funx funy. Added support reading NONMEM 7 table output files. Added functions odd type (categorical, TTE, count) plots including VPCs. Updated handling PsN vpc output file Updated interpretation categories xpose.VPC.categorical()","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-404","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.4","title":"xpose4 4.0.4","text":"cwres.vs.pred.bw() fixed. Previously cwres.vs.pred.bw() gave result cwres.vs.idv.bw(). Fixed xpose.VPC() bug causing plots created situations. Added functionality xpose.VPC() users can define titles subplot stratification used VPC. see ?xpose.VPC info. Updated method opening graphical devices windows consistent new methods used R version 2.8.0. Added functionality allow user plot vertical horizontal lines histograms. See ?xpose.panel.histogram information. Fixed small bug xpose.panel.splom().","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-403","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.3","title":"xpose4 4.0.3","text":"compute.cwres() debugging flag left file resulting R going debugging mode function called. fixed.","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-402","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.2","title":"xpose4 4.0.2","text":"Added ability smooth PI.ci “area” plots match “line” plots. See ‘PI.ci.area.smooth’ xpose.panel.default() Added ‘logx’ ‘logy’ functionality PI plots. Changed par.summary cov.summary routines removed functions almost thing (adding functionality current functions). fixed GAM plot problems xp.plot() added support GAM command line. Fixed problem ind.plots() ID variable called ID. Changed functions xpose4specific began “abs.” begin “absval.” consistent rules generic function definitions R. Changed name add.abs() add.absval(). Changed name par.summary() parm.summary(). Changed name param.vs.cov() parm.vs.cov(). Changed name param.vs.param() parm.vs.parm().","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-401","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.1","title":"xpose4 4.0.1","text":"Added functionality visual predictive checks Added functionality numerical predictive checks","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40037","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.7","title":"xpose4 4.0.0.3.7","text":"Added generic functions xpose.draw.table, xpose.draw.cell, xpose.get.c xpose.get.r drawing tables using graphics device (JW) Added specific function param.table display parameter estimates using graphics device (e.g. PDF file) (JW) Added additional specific functions : Added additional specific functions: IWRES distribution (histogram) (iwres.dist.hist) Added additional specific functions: IWRES distribution (QQ) (iwres.dist.qq) Added additional specific functions: ETA distribution (histogram) (ranpar.dist.hist) Added additional specific functions: ETA scatter-plot matrices (ranpar.splom) Added additional specific functions: ETAs vs covariates (ranpar.vs.cov) Added additional specific functions: Parameter tables graphics device (param.table) Updated compute.cwres function work without xpose 4 Just ‘source’ file (compute.cwres.R) work (AH) fixed problems run summary function (AH) added new general class printing multiple plot objects page (AH) Fixed bug plotting results GAM (AH)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40035","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.5","title":"xpose4 4.0.0.3.5","text":"Bugs ‘groups’ argument fixed xpose.plot.default, dv.vs.pred.ipred, dv.preds.vs.idv (multiple values x y properly handled) (JW) File devices (e.g. pdf, postscript, etc) now work correctly functions (JW) Bug multiple-page covariate plots fixed (first page display) (JW) Bug reading table files sometimes leave file debris, interfere reading subsequent data - fixed (JW) Bug covariate checking sometimes cause plot functions fail (e.g. abs.wres.vs.pred..cov) - fixed (JW) Bug classic menu system prevented display plots - fixed (JW) Bug classic menu system prevented display plots - fixed (JW) Bug CWRES calculation fixed (AH) Bug parameter histogram display fixed (JW) Missing values (defaults -99) now handled correctly (JW) QQ plots longer display categorical variables (JW)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40033","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.3","title":"xpose4 4.0.0.3.3","text":"Bug ‘subset’ argument individual plots corrected (JW)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40032","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.2","title":"xpose4 4.0.0.3.2","text":"Online documentation cleaned (JW) Numerous small bugs fixed (JW) *nix support added (JW) Multipage plots now create stacks display windows, rather stacks plots single window (JW) Scatter-plot matrices added (JW) QQ plots parameters covariates added (JW) Generic functions renamed consistency (JW)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40031","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3.1","title":"xpose4 4.0.0.3.1","text":"Bugs CWRES application documentation fixed (AH) Bugs histogram functions fixed - lack defined covariates longer causes crash - customization options now work","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-4003","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.3","title":"xpose4 4.0.0.3","text":"GAM added (AH) CWRES plots functions added (AH) gam package now required Known bugs corrected","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-4002","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.2","title":"xpose4 4.0.0.2","text":"SUBSET functionality fixed procedures Preferences, summaries data checkout implemented Box whisker plots now preferences tell ‘label’ function renamed ‘xlabel’ compatibility Hmisc package now required Many small additions tweaks R package functionality fixed","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-40021","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.2.1","title":"xpose4 4.0.0.2.1","text":"Ind.plots.R updated (AH)","code":""},{"path":"http://uupharmacometrics.github.io/xpose4/news/index.html","id":"xpose4-4001","dir":"Changelog","previous_headings":"","what":"xpose4 4.0.0.1","title":"xpose4 4.0.0.1","text":"Xpose 4 completely rewritten version Xpose 3.1, everything changed.","code":""}]