diff --git a/articles/running-monlix.html b/articles/running-monlix.html
index 94ad0124..b9c9828c 100644
--- a/articles/running-monlix.html
+++ b/articles/running-monlix.html
@@ -215,7 +215,7 @@ <h2 id="step-1-run-a-nlmixr2-in-monolix">Step 1: Run a <code>nlmixr2</code> in M
 <span><span class="co">#&gt; <span style="font-weight: bold;">── Time (sec </span><span style="color: #BBBB00; font-weight: bold;">fit</span><span style="color: #0000BB; font-weight: bold;">$time</span><span style="font-weight: bold;">): ──</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt;            setup table compress    other</span></span>
-<span><span class="co">#&gt; elapsed 0.002145 0.099    0.007 4.049855</span></span>
+<span><span class="co">#&gt; elapsed 0.001851 0.093    0.006 3.802149</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="font-weight: bold;">── Population Parameters (</span><span style="color: #BBBB00; font-weight: bold;">fit</span><span style="color: #0000BB; font-weight: bold;">$parFixed</span><span style="font-weight: bold;"> or </span><span style="color: #BBBB00; font-weight: bold;">fit</span><span style="color: #0000BB; font-weight: bold;">$parFixedDf</span><span style="font-weight: bold;">): ──</span></span></span>
 <span><span class="co">#&gt; </span></span>
diff --git a/articles/running-nonmem.html b/articles/running-nonmem.html
index e555495e..ad2701eb 100644
--- a/articles/running-nonmem.html
+++ b/articles/running-nonmem.html
@@ -280,7 +280,7 @@ <h2 id="optional-step-2-recover-a-failed-nonmem-run">Optional Step 2: Recover a
 <span><span class="co">#&gt; <span style="font-weight: bold;">── Time (sec </span><span style="color: #0000BB; font-weight: bold;">$time</span><span style="font-weight: bold;">): ──</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt;            setup table compress NONMEM</span></span>
-<span><span class="co">#&gt; elapsed 0.024675 0.064    0.009 320.27</span></span>
+<span><span class="co">#&gt; elapsed 0.023798 0.055     0.01 320.27</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="font-weight: bold;">── Population Parameters (</span><span style="color: #0000BB; font-weight: bold;">$parFixed</span><span style="font-weight: bold;"> or </span><span style="color: #0000BB; font-weight: bold;">$parFixedDf</span><span style="font-weight: bold;">): ──</span></span></span>
 <span><span class="co">#&gt; </span></span>
@@ -382,7 +382,7 @@ <h2 id="optional-step-3-use-nlmixr2-to-help-understand-why-nonmem-failed">Option
 <span><span class="co">#&gt; using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’</span></span>
 <span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> done</span></span>
 <span><span class="co">#&gt; calculating covariance matrix</span></span>
-<span><span class="co">#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:06</span></span>
+<span><span class="co">#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:05</span></span>
 <span><span class="co">#&gt; Warning in foceiFitCpp_(.ret): using R matrix to calculate covariance, can</span></span>
 <span><span class="co">#&gt; check sandwich or S matrix with $covRS and $covS</span></span>
 <span><span class="co">#&gt; Warning in foceiFitCpp_(.ret): gradient problems with covariance; see</span></span>
@@ -470,8 +470,8 @@ <h2 id="optional-step-4-use-model-piping-to-get-a-successful-nonmem-run">Optiona
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="font-weight: bold;">── Time (sec </span><span style="color: #BBBB00; font-weight: bold;">f2</span><span style="color: #0000BB; font-weight: bold;">$time</span><span style="font-weight: bold;">): ──</span></span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;            setup table compress NONMEM</span></span>
-<span><span class="co">#&gt; elapsed 0.002236  0.06    0.009 505.59</span></span>
+<span><span class="co">#&gt;           setup table compress NONMEM</span></span>
+<span><span class="co">#&gt; elapsed 0.00193 0.053     0.01 505.59</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="font-weight: bold;">── Population Parameters (</span><span style="color: #BBBB00; font-weight: bold;">f2</span><span style="color: #0000BB; font-weight: bold;">$parFixed</span><span style="font-weight: bold;"> or </span><span style="color: #BBBB00; font-weight: bold;">f2</span><span style="color: #0000BB; font-weight: bold;">$parFixedDf</span><span style="font-weight: bold;">): ──</span></span></span>
 <span><span class="co">#&gt; </span></span>
diff --git a/articles/running-pknca.html b/articles/running-pknca.html
index 45ed8ca4..a8716daf 100644
--- a/articles/running-pknca.html
+++ b/articles/running-pknca.html
@@ -291,7 +291,7 @@ <h2 id="initial-example">Initial example<a class="anchor" aria-label="anchor" hr
 <span><span class="co">#&gt; <span style="font-weight: bold;">── Time (sec </span><span style="color: #BBBB00; font-weight: bold;">fit</span><span style="color: #0000BB; font-weight: bold;">$time</span><span style="font-weight: bold;">): ──</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt;            setup optimize covariance table compress    other</span></span>
-<span><span class="co">#&gt; elapsed 0.024855 0.508425   0.508427 0.062    0.013 7.754293</span></span>
+<span><span class="co">#&gt; elapsed 0.025541 0.498633   0.498634 0.058    0.013 7.510192</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="font-weight: bold;">── Population Parameters (</span><span style="color: #BBBB00; font-weight: bold;">fit</span><span style="color: #0000BB; font-weight: bold;">$parFixed</span><span style="font-weight: bold;"> or </span><span style="color: #BBBB00; font-weight: bold;">fit</span><span style="color: #0000BB; font-weight: bold;">$parFixedDf</span><span style="font-weight: bold;">): ──</span></span></span>
 <span><span class="co">#&gt; </span></span>
diff --git a/news/index.html b/news/index.html
index 42ce81d7..2d6714b8 100644
--- a/news/index.html
+++ b/news/index.html
@@ -54,7 +54,7 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="0.1.2" id="babelmixr2-012">babelmixr2 0.1.2<a class="anchor" aria-label="anchor" href="#babelmixr2-012"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.1.2" id="babelmixr2-012">babelmixr2 0.1.2<a class="anchor" aria-label="anchor" href="#babelmixr2-012"></a></h2><p class="text-muted">CRAN release: 2023-12-12</p>
 <ul><li><p>Handle algebraic <code>mu</code> expressions</p></li>
 <li><p>PKNCA controller now contains <code>rxControl</code> since it is used for some translation options</p></li>
 <li><p>This revision will load the pruned ui model to query the compartment properties (i.e. bioavailability, lag time, etc) when writing out the NONMEM model. It should fix issues where the PK block does not define some of the variables and will have a larger calculated variable that can be used in the model instead.</p></li>
diff --git a/pkgdown.yml b/pkgdown.yml
index 391c0059..342b7ac3 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -6,5 +6,5 @@ articles:
   running-nonmem: running-nonmem.html
   new-estimation: new-estimation.html
   running-pknca: running-pknca.html
-last_built: 2023-12-12T19:28Z
+last_built: 2023-12-12T23:55Z
 
diff --git a/reference/as.nlmixr2.html b/reference/as.nlmixr2.html
index ec81db2c..93162daa 100644
--- a/reference/as.nlmixr2.html
+++ b/reference/as.nlmixr2.html
@@ -258,7 +258,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">── Time (sec </span><span style="color: #0000BB; font-weight: bold;">$time</span><span style="font-weight: bold;">): ──</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>            setup table compress NONMEM as.nlmixr2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> elapsed 0.034816 0.096    0.018 100.95       2.75</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> elapsed 0.030514 0.075    0.016 100.95      2.582</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">── Population Parameters (</span><span style="color: #0000BB; font-weight: bold;">$parFixed</span><span style="font-weight: bold;"> or </span><span style="color: #0000BB; font-weight: bold;">$parFixedDf</span><span style="font-weight: bold;">): ──</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
diff --git a/search.json b/search.json
index 053440bd..3980ab57 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"/articles/new-estimation.html","id":"create-a-nlmixr2est-method","dir":"Articles","previous_headings":"","what":"Create a nlmixr2Est() method","title":"Creating a New Estimation Method","text":"method input environment nlmixr2est UI object (see ?nlmixr2Est). output fit object.","code":""},{"path":"/articles/new-estimation.html","id":"create-a-control-method","dir":"Articles","previous_headings":"","what":"Create a control method","title":"Creating a New Estimation Method","text":"control method gives access controls required estimation.","code":""},{"path":"/articles/running-monlix.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-monolix-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run Monolix from a nlmixr2 model","title":"Running Monolix","text":"use Monolix nlmixr2, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run Monolix. setup lixoftConnectors package Monolix, setup needed. Instead run Monolix command line grid processing (example) can figure command run Monolix (often useful use full command path set options, ie options(\"babelmixr2.monolix\"=\"monolix\") use monolixControl(runCommand=\"monolix\"). needed, prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-monlix.html","id":"step-1-run-a-nlmixr2-in-monolix","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in Monolix","title":"Running Monolix","text":"Lets take classic warfarin example. model use nlmixr2 vignettes : monolix run, can run nlmixr2 model using Monolix new estimation method: fit issues informational tidbit - monolix parameter history needs exported charts, please export charts automatically generated well lixoftConnectors package generated recent version Monolix. don’t information important parameter history plots imported see plots. Just like NONMEM translation, monolixControl() modelName helps control output directory Monolix (specified babelmixr2 tries guess based model name based input). Printing nlmixr2 fit see: particular interest comparison Monolix predictions nlmixr predictions. case, believe also imply models predicting thing. Note model predictions close NONMEM Monolix use lsoda ODE solver. Hence small deviation expected, still gives validated Monolix model.","code":"pk.turnover.emax3 <- function() {   ini({     tktr <- log(1)     tka <- log(1)     tcl <- log(0.1)     tv <- log(10)     ##     eta.ktr ~ 1     eta.ka ~ 1     eta.cl ~ 2     eta.v ~ 1     prop.err <- 0.1     pkadd.err <- 0.1     ##     temax <- logit(0.8)     tec50 <- log(0.5)     tkout <- log(0.05)     te0 <- log(100)     ##     eta.emax ~ .5     eta.ec50  ~ .5     eta.kout ~ .5     eta.e0 ~ .5     ##     pdadd.err <- 10   })   model({     ktr <- exp(tktr + eta.ktr)     ka <- exp(tka + eta.ka)     cl <- exp(tcl + eta.cl)     v <- exp(tv + eta.v)     emax = expit(temax+eta.emax)     ec50 =  exp(tec50 + eta.ec50)     kout = exp(tkout + eta.kout)     e0 = exp(te0 + eta.e0)     ##     DCP = center/v     PD=1-emax*DCP/(ec50+DCP)     ##     effect(0) = e0     kin = e0*kout     ##     d/dt(depot) = -ktr * depot     d/dt(gut) =  ktr * depot -ka * gut     d/dt(center) =  ka * gut - cl / v * center     d/dt(effect) = kin*PD -kout*effect     ##     cp = center / v     cp ~ prop(prop.err) + add(pkadd.err)     effect ~ add(pdadd.err) | pca   }) } fit <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"monolix\",                               monolixControl(modelName=\"pk.turnover.emax3\")) #> ℹ assuming monolix is running because 'pk.turnover.emax3-monolix.txt' is present #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> ℹ monolix parameter history needs exported charts, please export charts fit #> ── nlmixr² monolix ver 2021R1 ── #>  #>              OBJF      AIC      BIC Log-likelihood Condition#(Cov) #> monolix  1522.704 2448.398 2527.819      -1205.199        2203.836 #>          Condition#(Cor) #> monolix         2.697324 #>  #> ── Time (sec fit$time): ── #>  #>            setup table compress    other #> elapsed 0.002145 0.099    0.007 4.049855 #>  #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #>  #>              Est.     SE     %RSE Back-transformed(95%CI) BSV(CV% or SD) #> tktr        0.218  0.179       82      1.24 (0.876, 1.77)           84.0 #> tka       0.00533  0.117 2.19e+03        1.01 (0.8, 1.26)           48.6 #> tcl         -2.01 0.0518     2.58    0.135 (0.122, 0.149)           28.5 #> tv           2.04 0.0438     2.14       7.73 (7.09, 8.42)           22.6 #> prop.err   0.0986                                  0.0986                #> pkadd.err   0.533                                   0.533                #> temax        4.46  0.527     11.8    0.989 (0.969, 0.996)          0.380 #> tec50      0.0786 0.0889      113      1.08 (0.909, 1.29)           47.8 #> tkout       -2.94 0.0261    0.888  0.053 (0.0503, 0.0558)           7.87 #> te0          4.57 0.0114    0.249       96.7 (94.5, 98.9)           5.08 #> pdadd.err    3.79                                    3.79                #>           Shrink(SD)% #> tktr           47.9%  #> tka            48.9%  #> tcl            1.25%  #> tv             6.09%  #> prop.err              #> pkadd.err             #> temax          91.9%  #> tec50          6.29%  #> tkout          36.6%  #> te0            19.9%  #> pdadd.err             #>   #>   Covariance Type (fit$covMethod): MonolixLin #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink  #>   Censoring (fit$censInformation): No censoring #>   Minimization message (fit$message):   #>     IPRED relative difference compared to Monolix IPRED: 0.09%; 95% percentile: (0.01%,0.49%); rtol=0.000941 #>     PRED relative difference compared to Monolix PRED: 0.04%; 95% percentile: (0%,0.2%); rtol=0.000428 #>     IPRED absolute difference compared to Monolix IPRED: atol=0.00911; 95% percentile: (0.000493, 0.0928) #>     PRED absolute difference compared to Monolix PRED: atol=0.000428; 95% percentile: (3.14e-07, 0.203) #>     monolix model: 'pk.turnover.emax3-monolix.mlxtran'  #>  #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 483 × 35 #>   ID     TIME CMT      DV  PRED   RES IPRED   IRES  IWRES eta.ktr eta.ka eta.cl #>   <fct> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl> #> 1 1       0.5 cp      0    1.40 -1.40 0.500 -0.500 -0.934  -0.638 -0.447  0.689 #> 2 1       1   cp      1.9  3.94 -2.04 1.62   0.284  0.511  -0.638 -0.447  0.689 #> 3 1       2   cp      3.3  8.30 -5.00 4.29  -0.987 -1.45   -0.638 -0.447  0.689 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v <dbl>, eta.emax <dbl>, eta.ec50 <dbl>, #> #   eta.kout <dbl>, eta.e0 <dbl>, cp <dbl>, depot <dbl>, gut <dbl>, #> #   center <dbl>, effect <dbl>, ktr <dbl>, ka <dbl>, cl <dbl>, v <dbl>, #> #   emax <dbl>, ec50 <dbl>, kout <dbl>, e0 <dbl>, DCP <dbl>, PD <dbl>, #> #   kin <dbl>, tad <dbl>, dosenum <dbl>"},{"path":"/articles/running-monlix.html","id":"optional-step-2-add-conditional-weighted-residualsfocei-objf-to-monolix","dir":"Articles","previous_headings":"","what":"Optional Step 2: Add conditional weighted residuals/focei objf to Monolix","title":"Running Monolix","text":"case NONMEM, gives things available Monolix, like adding conditional weighted residuals: add nlmixr’s CWRES well adding nlmixr2 FOCEi objective function now objective function compared based assumptions, compare performance Monolix NONMEM based objective function. fair, objective function values must always used caution. model performs predicts data far valuable.","code":"fit <- addCwres(fit) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done"},{"path":"/articles/running-monlix.html","id":"optional-step-3-use-nlmixr2-for-vpc-reporting-etc-","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 for vpc, reporting, etc.","title":"Running Monolix","text":"Also since nlmixr2 object easy perform VPC :","code":"v1s <- vpcPlot(fit, show=list(obs_dv=TRUE), scales=\"free_y\") +   ylab(\"Warfarin Cp [mg/L] or PCA\") +   xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’  v2s <- vpcPlot(fit, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") +   ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") +   xlab(\"Time [h]\")  v1s v2s"},{"path":"/articles/running-monlix.html","id":"notes-about-monolix-data-translation","dir":"Articles","previous_headings":"","what":"Notes about Monolix data translation","title":"Running Monolix","text":"input dataset expected compatible rxode2 nlmixr2. dataset converted Monolix format: combination CMT Dose type creates unique ADM variable. ADM definition saved monolix model file babelmixr2 creates macro describing compartment, ie compartment(cmt=#, amount=stateName) babelmixr2 also creates macro type dosing: Bolus/infusion uses depot() adds modeled lag time (Tlag) bioavailability (p) specified Modeled rate uses depot() Tk0=amtDose/rate. babelmixr2 also adds modeled lag time (Tlag) bioavailability (p) specified Modeled duration uses depot() Tk0=dur, also add adds modeled lag time (Tlag) bioavailability (p) specified Turning compartment uses empty macro","code":""},{"path":"/articles/running-nonmem.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-nonmem-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run NONMEM from a nlmixr2 model","title":"Running NONMEM with nlmixr2","text":"use NONMEM nlmixr, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run NONMEM. many cases easy; simply figure command run NONMEM (often useful use full command path). can set options(\"babelmixr2.nonmem\"=\"nmfe743\") use nonmemControl(runCommand=\"nmfe743\"). prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-nonmem.html","id":"step-1-run-a-nlmixr2-in-nonmem","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in NONMEM","title":"Running NONMEM with nlmixr2","text":"Lets take classic warfarin example start comparison. model use nlmixr2 vignettes : Now can run nlmixr2 model using NONMEM simply can run directly: way run ordinary nlmixr2 model, simply new estimation method \"nonmem\" new controller (nonmemControl()) setup options estimation. options nonmemControl() modelName helps control output directory NONMEM (specified babelmixr2 tries guess based model name based input). try , see NONMEM fails rounding errors. standard approach changing sigdig, sigl, tol etc, get successful NONMEM model convergence, course supported. babelmixr2 can .","code":"library(babelmixr2) pk.turnover.emax3 <- function() {   ini({     tktr <- log(1)     tka <- log(1)     tcl <- log(0.1)     tv <- log(10)     ##     eta.ktr ~ 1     eta.ka ~ 1     eta.cl ~ 2     eta.v ~ 1     prop.err <- 0.1     pkadd.err <- 0.1     ##     temax <- logit(0.8)     tec50 <- log(0.5)     tkout <- log(0.05)     te0 <- log(100)     ##     eta.emax ~ .5     eta.ec50  ~ .5     eta.kout ~ .5     eta.e0 ~ .5     ##     pdadd.err <- 10   })   model({     ktr <- exp(tktr + eta.ktr)     ka <- exp(tka + eta.ka)     cl <- exp(tcl + eta.cl)     v <- exp(tv + eta.v)     emax = expit(temax+eta.emax)     ec50 =  exp(tec50 + eta.ec50)     kout = exp(tkout + eta.kout)     e0 = exp(te0 + eta.e0)     ##     DCP = center/v     PD=1-emax*DCP/(ec50+DCP)     ##     effect(0) = e0     kin = e0*kout     ##     d/dt(depot) = -ktr * depot     d/dt(gut) =  ktr * depot -ka * gut     d/dt(center) =  ka * gut - cl / v * center     d/dt(effect) = kin*PD -kout*effect     ##     cp = center / v     cp ~ prop(prop.err) + add(pkadd.err)     effect ~ add(pdadd.err) | pca   }) } try(nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\",            nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax3\")),     silent=TRUE) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #>  #>  #>  WARNINGS AND ERRORS (IF ANY) FOR PROBLEM    1 #>  #>  (WARNING  2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #>  #>  #> 0MINIMIZATION TERMINATED #>  DUE TO ROUNDING ERRORS (ERROR=134) #>  NO. OF FUNCTION EVALUATIONS USED:     1088 #>  NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #>  #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> → terminated with rounding errors, can force nlmixr2/rxode2 to read with nonmemControl(readRounding=TRUE) #> Error : nonmem minimization not successful"},{"path":"/articles/running-nonmem.html","id":"optional-step-2-recover-a-failed-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 2: Recover a failed NONMEM run","title":"Running NONMEM with nlmixr2","text":"One approaches ignore rounding errors occurred read nlmixr2 anyway: may see work happening expected need already completed model. reading NONMEM model, babelmixr2 grabs: NONMEM’s objective function value NONMEM’s covariance (available) NONMEM’s optimization history NONMEM’s final parameter estimates (including ETAs) NONMEM’s PRED IPRED values (validation purposes) used solve ODEs came nlmixr2 optimization procedure. means can compare IPRED PRED values nlmixr2/rxode2 know immediately model validates. similar procedure Kyle Baron advocates validating NONMEM model mrgsolve model (see https://mrgsolve.org/blog/posts/2022-05-validate-translation/ https://mrgsolve.org/blog/posts/2023-update-validation.html), advantage method need simply write one model get validated roxde2/nlmixr2 model. case can see validation print fit object: shows preds ipreds match NONMEM nlmixr2 quite well.","code":"# Can still load the model to get information (possibly pipe) and create a new model f <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\",             nonmemControl(readRounding=TRUE, modelName=\"pk.turnover.emax3\")) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.1.0 using 2 threads (see ?getRxThreads) #>   no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 5536 print(f) #> ── nlmixr² nonmem ver 7.4.3 ── #>  #>                 OBJF      AIC      BIC Log-likelihood Condition#(Cov) #> nonmem focei 1326.91 2252.605 2332.025      -1107.302              NA #>              Condition#(Cor) #> nonmem focei              NA #>  #> ── Time (sec $time): ── #>  #>            setup table compress NONMEM #> elapsed 0.024675 0.064    0.009 320.27 #>  #> ── Population Parameters ($parFixed or $parFixedDf): ── #>  #>                Est. Back-transformed BSV(CV% or SD) Shrink(SD)% #> tktr       6.24e-07                1           86.5      59.8%  #> tka       -3.01e-06                1           86.5      59.8%  #> tcl              -2            0.135           28.6      1.34%  #> tv             2.05             7.78           22.8      6.44%  #> prop.err     0.0986           0.0986                            #> pkadd.err     0.512            0.512                            #> temax          6.42            0.998        0.00707      100.%  #> tec50         0.141             1.15           45.0      6.06%  #> tkout         -2.95           0.0522           9.16      32.4%  #> te0            4.57             96.6           5.24      18.1%  #> pdadd.err      3.72             3.72                            #>   #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink  #>   Information about run found ($runInfo): #>    • NONMEM terminated due to rounding errors, but reading into nlmixr2/rxode2 anyway  #>   Censoring ($censInformation): No censoring #>   Minimization message ($message):   #>      #>  #>  WARNINGS AND ERRORS (IF ANY) FOR PROBLEM    1 #>  #>  (WARNING  2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #>  #>      #> 0MINIMIZATION TERMINATED #>  DUE TO ROUNDING ERRORS (ERROR=134) #>  NO. OF FUNCTION EVALUATIONS USED:     1088 #>  NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #>  #>     IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.36e-06 #>     PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.08e-06 #>     IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.53e-06, 0.000502); atol=7.15e-05 #>     PRED absolute difference compared to Nonmem PRED: 95% percentile: (3.79e-07,0.00321); atol=6.08e-06 #>     there are solving errors during optimization (see '$prderr') #>     nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl'  #>  #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 483 × 35 #>   ID     TIME CMT      DV  PRED   RES IPRED   IRES  IWRES eta.ktr eta.ka eta.cl #>   <fct> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl> #> 1 1       0.5 cp      0    1.16 -1.16 0.444 -0.444 -0.864  -0.506 -0.506  0.699 #> 2 1       1   cp      1.9  3.37 -1.47 1.45   0.446  0.840  -0.506 -0.506  0.699 #> 3 1       2   cp      3.3  7.51 -4.21 3.96  -0.660 -1.03   -0.506 -0.506  0.699 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v <dbl>, eta.emax <dbl>, eta.ec50 <dbl>, #> #   eta.kout <dbl>, eta.e0 <dbl>, cp <dbl>, depot <dbl>, gut <dbl>, #> #   center <dbl>, effect <dbl>, ktr <dbl>, ka <dbl>, cl <dbl>, v <dbl>, #> #   emax <dbl>, ec50 <dbl>, kout <dbl>, e0 <dbl>, DCP <dbl>, PD <dbl>, #> #   kin <dbl>, tad <dbl>, dosenum <dbl>"},{"path":"/articles/running-nonmem.html","id":"optional-step-3-use-nlmixr2-to-help-understand-why-nonmem-failed","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 to help understand why NONMEM failed","title":"Running NONMEM with nlmixr2","text":"Since nlmixr2 fit, can interesting things fit couldn’t NONMEM even another translator. example, wanted add covariance step can getVarCov(): nlmixr2 generous constitutes covariance step. r,s covariance matrix “” successful covariance step focei, system fall back methods necessary. covariance matrix r,s, regarded caution, can still give us clues things working NONMEM. examining fit, can see shrinkage high temax, tktr tka, dropped, making things likely converge NONMEM.","code":"getVarCov(f) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:06 #> Warning in foceiFitCpp_(.ret): using R matrix to calculate covariance, can #> check sandwich or S matrix with $covRS and $covS #> Warning in foceiFitCpp_(.ret): gradient problems with covariance; see #> $scaleInfo #> → compress origData in nlmixr2 object, save 27560 #> Updated original fit object f #>                tktr           tka           tcl           tv         temax #> tktr   1.821078e-02 -1.512272e-02 -2.550343e-05 3.216116e-04  0.0015410335 #> tka   -1.512272e-02  1.815814e-02 -1.992622e-05 3.175474e-04  0.0010345827 #> tcl   -2.550343e-05 -1.992622e-05  2.477225e-04 1.181659e-05 -0.0008009162 #> tv     3.216116e-04  3.175474e-04  1.181659e-05 3.184497e-04  0.0010914727 #> temax  1.541033e-03  1.034583e-03 -8.009162e-04 1.091473e-03  7.5815740647 #> tec50  1.410716e-04  1.273505e-04 -3.578298e-04 1.229707e-04  0.0483191718 #> tkout  1.023011e-04  1.011022e-04 -9.757882e-05 1.188260e-04 -0.0189641465 #> te0    1.310259e-05  1.399880e-05 -9.833068e-06 1.232683e-05 -0.0004365713 #>               tec50         tkout           te0 #> tktr   0.0001410716  1.023011e-04  1.310259e-05 #> tka    0.0001273505  1.011022e-04  1.399880e-05 #> tcl   -0.0003578298 -9.757882e-05 -9.833068e-06 #> tv     0.0001229707  1.188260e-04  1.232683e-05 #> temax  0.0483191718 -1.896415e-02 -4.365713e-04 #> tec50  0.0018345990  1.544065e-04 -1.357629e-04 #> tkout  0.0001544065  6.320302e-04  5.220487e-05 #> te0   -0.0001357629  5.220487e-05  8.843897e-05"},{"path":"/articles/running-nonmem.html","id":"optional-step-4-use-model-piping-to-get-a-successful-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 4: Use model piping to get a successful NONMEM run","title":"Running NONMEM with nlmixr2","text":"use model piping remove parameters, new run start last model’s best estimates (saving bunch model development time). case, specify output directory pk.turnover.emax4 control get following: can see NONMEM run now successful validates rxode2 model : One thing emphasize: unlike translators, know immediately translation model validate. Hence can start process confidence - know immediately something wrong. related converting NONMEM nlmixr2 fit. Since nlmixr2 object easy perform VPC (true NONMEM models):","code":"f2 <- f %>% model(ktr <- exp(tktr)) %>%   model(ka <- exp(tka)) %>%   model(emax = expit(temax)) %>%   nlmixr(data=nlmixr2data::warfarin, est=\"nonmem\",          control=nonmemControl(readRounding=FALSE,                                modelName=\"pk.turnover.emax4\")) #> ! remove between subject variability `eta.ktr` #> ! remove between subject variability `eta.ka` #> ! remove between subject variability `eta.emax` #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|==== #> ====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 8800 f2 #> ── nlmixr² nonmem ver 7.4.3 ── #>  #>                  OBJF      AIC      BIC Log-likelihood Condition#(Cov) #> nonmem focei 1418.923 2338.618 2405.498      -1153.309    1.852796e+16 #>              Condition#(Cor) #> nonmem focei        18934770 #>  #> ── Time (sec f2$time): ── #>  #>            setup table compress NONMEM #> elapsed 0.002236  0.06    0.009 505.59 #>  #> ── Population Parameters (f2$parFixed or f2$parFixedDf): ── #>  #>                Est.       SE     %RSE     Back-transformed(95%CI) BSV(CV%) #> tktr       6.24e-07 9.05e-05 1.45e+04                    1 (1, 1)          #> tka       -3.57e-06 0.000153 4.29e+03                    1 (1, 1)          #> tcl           -1.99   0.0639      3.2         0.136 (0.12, 0.154)     27.6 #> tv             2.05     2.66      130      7.76 (0.042, 1.44e+03)     23.6 #> prop.err      0.161                                         0.161          #> pkadd.err     0.571                                         0.571          #> temax          9.98     4.96     49.7                1 (0.565, 1)          #> tec50         0.131     1.61 1.23e+03         1.14 (0.0489, 26.6)     43.6 #> tkout         -2.96     28.3      954 0.0517 (4.63e-26, 5.77e+22)     8.63 #> te0            4.57    0.411        9            96.7 (43.2, 217)     5.19 #> pdadd.err      3.59                                          3.59          #>           Shrink(SD)% #> tktr                  #> tka                   #> tcl            3.19%  #> tv             10.7%  #> prop.err              #> pkadd.err             #> temax                 #> tec50          7.12%  #> tkout          33.8%  #> te0            17.2%  #> pdadd.err             #>   #>   Covariance Type (f2$covMethod): nonmem.r,s #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance (f2$omega) or correlation (f2$omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in f2$shrink  #>   Censoring (f2$censInformation): No censoring #>   Minimization message (f2$message):   #>      #>  #>  WARNINGS AND ERRORS (IF ANY) FOR PROBLEM    1 #>  #>  (WARNING  2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #>  #>      #> 0MINIMIZATION SUCCESSFUL #>  HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. #>  REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY #>  AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. #>  NO. OF FUNCTION EVALUATIONS USED:     2391 #>  NO. OF SIG. DIGITS IN FINAL EST.:  4.1 #>  #>     IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.85e-06 #>     PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.45e-06 #>     IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.89e-06, 0.000506); atol=7.19e-05 #>     PRED absolute difference compared to Nonmem PRED: 95% percentile: (5.14e-07,0.00318); atol=6.45e-06 #>     nonmem model: 'pk.turnover.emax4-nonmem/pk.turnover.emax4.nmctl'  #>  #> ── Fit Data (object f2 is a modified tibble): ── #> # A tibble: 483 × 32 #>   ID     TIME CMT      DV  PRED   RES IPRED   IRES IWRES eta.cl eta.v eta.ec50 #>   <fct> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl>    <dbl> #> 1 1       0.5 cp      0    1.16 -1.16 0.920 -0.920 -1.56  0.689 0.228    0.160 #> 2 1       1   cp      1.9  3.38 -1.48 2.68  -0.780 -1.09  0.689 0.228    0.160 #> 3 1       2   cp      3.3  7.53 -4.23 5.94  -2.64  -2.36  0.689 0.228    0.160 #> # ℹ 480 more rows #> # ℹ 20 more variables: eta.kout <dbl>, eta.e0 <dbl>, cp <dbl>, depot <dbl>, #> #   gut <dbl>, center <dbl>, effect <dbl>, ktr <dbl>, ka <dbl>, cl <dbl>, #> #   v <dbl>, emax <dbl>, ec50 <dbl>, kout <dbl>, e0 <dbl>, DCP <dbl>, PD <dbl>, #> #   kin <dbl>, tad <dbl>, dosenum <dbl> v1s <- vpcPlot(f2, show=list(obs_dv=TRUE), scales=\"free_y\") +   ylab(\"Warfarin Cp [mg/L] or PCA\") +   xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’  v2s <- vpcPlot(f2, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") +   ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") +   xlab(\"Time [h]\")   library()  v1s v2s"},{"path":"/articles/running-pknca.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Obtain initial estimates and unit conversions with PKNCA","text":"Initial estimates compartmental population PK model can obtained using babelmixr2 \"pknca\" estimation method. Also, central compartment scaling factor can auto-generated based units dosing, concentration measurement, desired volume distribution units, time. need perform NCA analysis hand; \"pknca\" estimation method perform NCA analysis using PKNCA package automatically. methods used converting NCA calculations parameter estimates described help nlmixr2Est.pknca().","code":""},{"path":"/articles/running-pknca.html","id":"initial-example","dir":"Articles","previous_headings":"","what":"Initial example","title":"Obtain initial estimates and unit conversions with PKNCA","text":"Initial model setup nlmixr2 model. must load babelmixr2 library nlmixr() function recognizes est = \"pknca\". use PKNCA get initial estimates, use est = \"pknca\" instead one nlmixr2 estimation methods. unit conversions, provide units control = pkncaControl() argument. Unit conversions supported units can automatically converted; mass/volume can converted mass/volume ratio, mass molar molar mass single mass--molar conversion factor. Now, prepared model updated initial estimates NCA results embedded. can see new model PKNCA estimates looking prepared$ui (model interpreted rxode2) prepared$nca (PKNCAresults object). Note new model, fixed effect initial estimates changed original values. residual error -subject variability unchanged. updated model, can perform estimation new model object, model created nlmixr2:","code":"library(babelmixr2) #> Loading required package: nlmixr2 #> Loading required package: nlmixr2data one.compartment <- function() {   ini({     tka <- log(1.57); label(\"Ka (1/hr)\")     tcl <- log(2.72); label(\"Cl (L/hr)\")     tv <- log(31.5); label(\"V (L)\")     eta.ka ~ 0.6     eta.cl ~ 0.3     eta.v ~ 0.1     add.sd <- 0.7; label(\"additive residual error (mg/L)\")   })   # and a model block with the error specification and model specification   model({     ka <- exp(tka + eta.ka)     cl <- exp(tcl + eta.cl)     vc <- exp(tv + eta.v)     d/dt(depot) <- -ka * depot     d/dt(center) <- ka * depot - cl / vc * center     cp <- center / vc     cp ~ add(add.sd)   }) } prepared <-   nlmixr2(     one.compartment,     data = theo_sd,     est = \"pknca\",     control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\")   ) #> Loading required namespace: testthat #> ℹ change initial estimate (0.89314878960486) and upper/lower bound (-3.50655789731998 to 3.72508541597241) of `tka` #> → significant model change detected #> → removed from model: '$getSplitModel' #> ℹ change initial estimate (8.41044546236311) and upper/lower bound (5.51439905878865 to 10.899462850803) of `tcl` #> ℹ change initial estimate (10.5377244826318) and upper/lower bound (7.94567233496473 to 13.1050053785005) of `tv` prepared$ui #>  ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>        tka        tcl         tv     add.sd  #>  0.8931488  8.4104455 10.5377245  0.7000000  #>  #> Omega ($omega):  #>        eta.ka eta.cl eta.v #> eta.ka    0.6    0.0   0.0 #> eta.cl    0.0    0.3   0.0 #> eta.v     0.0    0.0   0.1 #>  #> States ($state or $stateDf):  #>   Compartment Number Compartment Name #> 1                  1            depot #> 2                  2           center #>  ── μ-referencing ($muRefTable): ──   #>   theta    eta level #> 1   tka eta.ka    id #> 2   tcl eta.cl    id #> 3    tv  eta.v    id #>  #>  ── Model (Normalized Syntax): ──  #> function() { #>     ini({ #>         tka <- c(-3.50655789731998, 0.89314878960486, 3.72508541597241) #>         label(\"Ka (1/hr)\") #>         tcl <- c(5.51439905878865, 8.41044546236311, 10.899462850803) #>         label(\"Cl (L/hr)\") #>         tv <- c(7.94567233496473, 10.5377244826318, 13.1050053785005) #>         label(\"V (L)\") #>         add.sd <- c(0, 0.7) #>         label(\"additive residual error (mg/L)\") #>         eta.ka ~ 0.6 #>         eta.cl ~ 0.3 #>         eta.v ~ 0.1 #>     }) #>     model({ #>         ka <- exp(tka + eta.ka) #>         cl <- exp(tcl + eta.cl) #>         vc <- exp(tv + eta.v) #>         d/dt(depot) <- -ka * depot #>         d/dt(center) <- ka * depot - cl/vc * center #>         cp <- 1000 * center/vc #>         cp ~ add(add.sd) #>     }) #> }  knitr::knit_print(   summary(prepared$nca) ) #>  Interval Start Interval End  N AUClast (hr*ng/mL) Cmax (ng/mL) #>               0           24 12        74.6 [24.3]            . #>               0          Inf 12                  .  8.65 [17.0] #>           Tmax (hr) CL (based on AUClast) (mg/(hr*ng/mL)) #>                   .                           4.22 [23.0] #>  1.14 [0.630, 3.55]                                     . #>  Vss (based on AUClast) (mg/(ng/mL)) Half-life (hr) AUCinf,obs (hr*ng/mL) #>                          25.0 [18.5]              .                     . #>                                    .    8.18 [2.12]            115 [28.4] #>  Cmax (dose-normalized) ((ng/mL)/mg) #>                                    . #>                        0.0274 [18.1] #>  #> Caption: AUClast, Cmax, CL (based on AUClast), Vss (based on AUClast), AUCinf,obs, Cmax (dose-normalized): geometric mean and geometric coefficient of variation; Tmax: median and range; Half-life: arithmetic mean and standard deviation fit <- nlmixr(prepared, data = theo_sd, est = \"focei\", control = list(print = 0)) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.1.0 using 2 threads (see ?getRxThreads) #>   no cache: create with `rxCreateCache()` #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:00  #> done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 5952 #> → compress parHistData in nlmixr2 object, save 10632  fit #> ── nlmixr² FOCEi (outer: nlminb) ── #>  #>           OBJF      AIC      BIC Log-likelihood Condition#(Cov) Condition#(Cor) #> FOCEi 116.9548 373.5546 393.7342      -179.7773         66.5244        12.97786 #>  #> ── Time (sec fit$time): ── #>  #>            setup optimize covariance table compress    other #> elapsed 0.024855 0.508425   0.508427 0.062    0.013 7.754293 #>  #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #>  #>                             Parameter  Est.     SE  %RSE #> tka                         Ka (1/hr) 0.469  0.224  47.6 #> tcl                         Cl (L/hr)  7.92 0.0929  1.17 #> tv                              V (L)  10.4 0.0602 0.581 #> add.sd additive residual error (mg/L) 0.697              #>              Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> tka                 1.6 (1.03, 2.48)     68.0    -0.666%  #> tcl     2.75e+03 (2.29e+03, 3.3e+03)     26.1      3.95%  #> tv     3.19e+04 (2.83e+04, 3.59e+04)     15.4      14.5%  #> add.sd                         0.697                      #>   #>   Covariance Type (fit$covMethod): r,s #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink  #>   Information about run found (fit$runInfo): #>    • gradient problems with initial estimate and covariance; see $scaleInfo  #>    • last objective function was not at minimum, possible problems in optimization  #>    • ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))  #>    • initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))  #>   Censoring (fit$censInformation): No censoring #>   Minimization message (fit$message):   #>     false convergence (8)  #>   In an ODE system, false convergence may mean \"useless\" evaluations were performed. #>   See https://tinyurl.com/yyrrwkce #>   It could also mean the convergence is poor, check results before accepting fit #>   You may also try a good derivative free optimization: #>     nlmixr2(...,control=list(outerOpt=\"bobyqa\")) #>  #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 132 × 22 #>   ID     TIME    DV  PRED    RES   WRES IPRED   IRES  IWRES CPRED   CRES  CWRES #>   <fct> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl> #> 1 1      0     0.74  0     0.74   1.06   0     0.74   1.06   0     0.74   1.06  #> 2 1      0.25  2.84  3.27 -0.432 -0.234  3.84 -1.00  -1.44   3.23 -0.389 -0.185 #> 3 1      0.57  6.57  5.84  0.730  0.297  6.78 -0.215 -0.308  5.78  0.786  0.287 #> # ℹ 129 more rows #> # ℹ 10 more variables: eta.ka <dbl>, eta.cl <dbl>, eta.v <dbl>, depot <dbl>, #> #   center <dbl>, ka <dbl>, cl <dbl>, vc <dbl>, tad <dbl>, dosenum <dbl>"},{"path":"/articles/running-pknca.html","id":"give-pknca-a-different-dataset-or-a-completed-nca-analysis","dir":"Articles","previous_headings":"","what":"Give PKNCA a different dataset or a completed NCA analysis","title":"Obtain initial estimates and unit conversions with PKNCA","text":"get initial estimate, babelmixr2 automatically converts modeling dataset format needed PKNCA, NCA automatically performed using data. cases (e.g. studies sparse data), NCA may feasible. cases, can provide different dataset PKNCA compared full modeling dataset. Usually, simplest method provide single-dose, dense-sampling, dose-ranging data (.e. single-ascending dose portion first--human study) estimated. , give data PKNCA using ncaData argument pkncaControl() follows: initial estimates now based NCA calculated dNCA dataset rather full theo_sd dataset. already NCA results calculated PKNCA required parameters (“tmax”, “cmax.dn”, “cllast”), can provide instead using pkncaControl(ncaResults) argument.","code":"# Choose a subset of the full dataset for NCA dNCA <- theo_sd[theo_sd$ID <= 6, ]  preparedNcaData <-   nlmixr2(     one.compartment,     data = theo_sd,     est = \"pknca\",     control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\", ncaData = dNCA)   ) #> ℹ change initial estimate (0.929027077269762) and upper/lower bound (-3.50655789731998 to 3.32136703319919) of `tka` #> → significant model change detected #> → removed from model: '$getSplitModel' #> ℹ change initial estimate (8.3955404628088) and upper/lower bound (5.85241523541802 to 10.7637056987378) of `tcl` #> ℹ change initial estimate (10.5377244826318) and upper/lower bound (7.94370069836702 to 13.1024358787022) of `tv`  preparedNcaData$ui #>  ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>        tka        tcl         tv     add.sd  #>  0.9290271  8.3955405 10.5377245  0.7000000  #>  #> Omega ($omega):  #>        eta.ka eta.cl eta.v #> eta.ka    0.6    0.0   0.0 #> eta.cl    0.0    0.3   0.0 #> eta.v     0.0    0.0   0.1 #>  #> States ($state or $stateDf):  #>   Compartment Number Compartment Name #> 1                  1            depot #> 2                  2           center #>  ── μ-referencing ($muRefTable): ──   #>   theta    eta level #> 1   tka eta.ka    id #> 2   tcl eta.cl    id #> 3    tv  eta.v    id #>  #>  ── Model (Normalized Syntax): ──  #> function() { #>     ini({ #>         tka <- c(-3.50655789731998, 0.929027077269762, 3.32136703319919) #>         label(\"Ka (1/hr)\") #>         tcl <- c(5.85241523541802, 8.3955404628088, 10.7637056987378) #>         label(\"Cl (L/hr)\") #>         tv <- c(7.94370069836702, 10.5377244826318, 13.1024358787022) #>         label(\"V (L)\") #>         add.sd <- c(0, 0.7) #>         label(\"additive residual error (mg/L)\") #>         eta.ka ~ 0.6 #>         eta.cl ~ 0.3 #>         eta.v ~ 0.1 #>     }) #>     model({ #>         ka <- exp(tka + eta.ka) #>         cl <- exp(tcl + eta.cl) #>         vc <- exp(tv + eta.v) #>         d/dt(depot) <- -ka * depot #>         d/dt(center) <- ka * depot - cl/vc * center #>         cp <- 1000 * center/vc #>         cp ~ add(add.sd) #>     }) #> }"},{"path":"/articles/running-pknca.html","id":"model-requirements","dir":"Articles","previous_headings":"","what":"Model requirements","title":"Obtain initial estimates and unit conversions with PKNCA","text":"update initial estimates, model must parameters model() block names expected est = \"pknca\". expected names : ka vc cl vp vp2 q q2 parameter names found model() block automatically traced back initial conditions (ini() block), parameter values updated. parameter estimated log scale, updated parameter value automatically converted log scale.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthew Fidler. Author, maintainer. Bill Denney. Author. Nook Fulloption. Contributor.            goldfish art","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Fidler M, Xiong Y, Schoemaker R, Wilkins J, Trame M, Hooijmaijers R, Post T, Wang W (2023). nlmixr: Nonlinear Mixed Effects Models Population Pharmacokinetics Pharmacodynamics. R package version 0.1.2, https://CRAN.R-project.org/package=nlmixr. Fidler M, Wilkins J, Hooijmaijers R, Post T, Schoemaker R, Trame M, Xiong Y, Wang W (2019). “Nonlinear Mixed-Effects Model Development Simulation Using nlmixr Related R Open-Source Packages.” CPT: Pharmacometrics & Systems Pharmacology, 8(9), 621–633. https://doi.org/10.1002/psp4.12445. Schoemaker R, Fidler M, Laveille C, Wilkins J, Hooijmaijers R, Post T, Trame M, Xiong Y, Wang W (2019). “Performance SAEM FOCEI Algorithms Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.” CPT: Pharmacometrics & Systems Pharmacology, 8(12), 923–930. https://doi.org/10.1002/psp4.12471.","code":"@Manual{,   title = {{nlmixr}: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics},   author = {Matthew Fidler and Yuan Xiong and Rik Schoemaker and Justin Wilkins and Mirjam Trame and Richard Hooijmaijers and Teun Post and Wenping Wang},   year = {2023},   note = {R package version 0.1.2},   url = {https://CRAN.R-project.org/package=nlmixr}, } @Article{,   title = {Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages},   author = {Matthew Fidler and Justin Wilkins and Richard Hooijmaijers and Teun Post and Rik Schoemaker and Mirjam Trame and Yuan Xiong and Wenping Wang},   journal = {CPT: Pharmacometrics \\& Systems Pharmacology},   year = {2019},   volume = {8},   pages = {621--633},   number = {9},   month = {sep},   abstract = {nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.},   address = {Hoboken},   publisher = {John Wiley and Sons Inc.},   url = {https://doi.org/10.1002/psp4.12445}, } @Article{,   title = {Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr},   author = {Rik Schoemaker and Matthew Fidler and Christian Laveille and Justin Wilkins and Richard Hooijmaijers and Teun Post and Mirjam Trame and Yuan Xiong and Wenping Wang},   journal = {CPT: Pharmacometrics \\& Systems Pharmacology},   year = {2019},   volume = {8},   pages = {923--930},   number = {12},   month = {dec},   abstract = {The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.},   url = {https://doi.org/10.1002/psp4.12471}, }"},{"path":"/index.html","id":"babelmixr2","dir":"","previous_headings":"","what":"Use nlmixr2 to Interact with Open Source and Commercial Software","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"goal babelmixr2 convert nlmixr2 syntax commonly used tools.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"can install released version babelmixr2 CRAN : can install r-universe : Otherwise can always install GitHub:","code":"install.packages(\"babelmixr2\") # Download and install babelmixr2 in R install.packages('babelmixr2',                  repos = c(                    nlmixr2 = 'https://nlmixr2.r-universe.dev',                    CRAN = 'https://cloud.r-project.org'))"},{"path":"/index.html","id":"what-you-can-do-with-babelmixr2","dir":"","previous_headings":"","what":"What you can do with babelmixr2","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"Babelmixr2 can help : Running nlmixr2 model commercial nonlinear mixed effects modeling tool like NONMEM Monolix Convert NONMEM model nlmixr2 model (conjunction nonmem2rx) Calculate scaling factors automatically add initial conditions based non-compartmental analysis (using PKNCA)","code":""},{"path":"/index.html","id":"monolix-setup","dir":"","previous_headings":"","what":"Monolix Setup","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"required, can get/install R ‘lixoftConnectors’ package ‘Monolix’ installation, described following url https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/. ‘lixoftConnectors’ available, R can run ‘Monolix’ directly instead using command line.","code":""},{"path":"/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"installed, use standard interface, can obtain new initial estimates PKNCA: , can convert Monolix , can convert NONMEM ","code":"mod <-   nlmixr2(     nlmixrFun, nlmmixrData, est = \"pknca\",     control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\")   ) mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"monolix\") mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"nonmem\")"},{"path":"/reference/as.nlmixr2.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an object to a nlmixr2 fit object — as.nlmixr2","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Convert object nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"as.nlmixr2(   x,   ...,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl() )  as.nlmixr(   x,   ...,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl() )"},{"path":"/reference/as.nlmixr2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"x Object convert ... arguments table nlmixr2est::tableControl() options rxControl rxode2::rxControl() options, generally needed addl doses handled translation","code":""},{"path":"/reference/as.nlmixr2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Matthew L. Fidler","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"# \\donttest{  # First read in the model (but without residuals) mod <- nonmem2rx(system.file(\"mods/cpt/runODE032.ctl\", package=\"nonmem2rx\"),                  determineError=FALSE, lst=\".res\", save=FALSE) #> ℹ getting information from  '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> ℹ reading in xml file #> ℹ done #> ℹ reading in phi file #> ℹ done #> ℹ reading in lst file #> ℹ abbreviated list parsing #> ℹ done #> ℹ done #> ℹ splitting control stream by records #> ℹ done #> ℹ Processing record $INPUT #> ℹ Processing record $MODEL #> ℹ Processing record $THETA #> ℹ Processing record $OMEGA #> ℹ Processing record $SIGMA #> ℹ Processing record $PROBLEM #> ℹ Processing record $DATA #> ℹ Processing record $SUBROUTINES #> ℹ Processing record $PK #> ℹ Processing record $DES #> ℹ Processing record $ERROR #> ℹ Processing record $ESTIMATION #> ℹ Ignore record $ESTIMATION #> ℹ Processing record $COVARIANCE #> ℹ Ignore record $COVARIANCE #> ℹ Processing record $TABLE #> ℹ change initial estimate of `theta1` to `1.37034036528946` #> ℹ change initial estimate of `theta2` to `4.19814911033061` #> ℹ change initial estimate of `theta3` to `1.38003493562413` #> ℹ change initial estimate of `theta4` to `3.87657341967489` #> ℹ change initial estimate of `theta5` to `0.196446108190896` #> ℹ change initial estimate of `eta1` to `0.101251418415006` #> ℹ change initial estimate of `eta2` to `0.0993872449483344` #> ℹ change initial estimate of `eta3` to `0.101302674763154` #> ℹ change initial estimate of `eta4` to `0.0730497519364148` #> ℹ read in nonmem input data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/Bolus_2CPT.csv #> ℹ ignoring lines that begin with a letter (IGNORE=@)' #> ℹ applying names specified by $INPUT #> ℹ subsetting accept/ignore filters code: .data[-which((.data$SD == 0)),] #> ℹ done #>   #>   #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ read in nonmem IPRED data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.csv #> ℹ done #> ℹ changing most variables to lower case #> ℹ done #> ℹ replace theta names #> ℹ done #> ℹ replace eta names #> ℹ done (no labels) #> ℹ renaming compartments #> ℹ done #>   #>   #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done  # define the model with residuals (and change the name of the # parameters) In this step you need to be careful to not change the # estimates and make sure the residual estimates are correct (could # have to change var to sd).   mod2 <-function() {    ini({      lcl <- 1.37034036528946      lvc <- 4.19814911033061      lq <- 1.38003493562413      lvp <- 3.87657341967489      RSV <- c(0, 0.196446108190896, 1)      eta.cl ~ 0.101251418415006      eta.v ~ 0.0993872449483344      eta.q ~ 0.101302674763154      eta.v2 ~ 0.0730497519364148    })    model({      cmt(CENTRAL)      cmt(PERI)      cl <- exp(lcl + eta.cl)      v <- exp(lvc + eta.v)      q <- exp(lq + eta.q)      v2 <- exp(lvp + eta.v2)      v1 <- v      scale1 <- v      k21 <- q/v2      k12 <- q/v      d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1      d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL      f <- CENTRAL/scale1      f ~ prop(RSV)    })  }  # now we create another nonmem2rx object that validates the model above:  new <- as.nonmem2rx(mod2, mod) #>   #>   #> ℹ copy 'dfSub' to nonmem2rx model #> ℹ copy 'thetaMat' to nonmem2rx model #> ℹ copy 'dfObs' to nonmem2rx model #>   #>   #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done  # once that is done, you can translate to a full nlmixr2 fit (if you wish)  fit <- as.nlmixr2(new) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> → optimizing duplicate expressions in EBE model... #> → compiling EBE model... #>   #>   #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.1.0 using 2 threads (see ?getRxThreads) #>   no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 204016 #> → compress parHistData in nlmixr2 object, save 2176  print(fit) #> ── nlmixr² nonmem2rx reading NONMEM ver 7.4.3 ── #>  #>               OBJF      AIC      BIC Log-likelihood Condition#(Cov) #> nonmem2rx 15977.28 20185.64 20237.23      -10083.82        335.4129 #>           Condition#(Cor) #> nonmem2rx        2.096559 #>  #> ── Time (sec $time): ── #>  #>            setup table compress NONMEM as.nlmixr2 #> elapsed 0.034816 0.096    0.018 100.95       2.75 #>  #> ── Population Parameters ($parFixed or $parFixedDf): ── #>  #>      Est.     SE  %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> lcl  1.37 0.0298  2.17       3.94 (3.71, 4.17)     32.6      1.94%  #> lvc   4.2 0.0295 0.703       66.6 (62.8, 70.5)     32.3      2.46%  #> lq   1.38 0.0547  3.96       3.98 (3.57, 4.42)     32.7      40.5%  #> lvp  3.88 0.0348 0.899       48.3 (45.1, 51.7)     27.5      28.4%  #> RSV 0.196                                0.196                      #>   #>   Covariance Type ($covMethod): nonmem2rx #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink  #>   Censoring ($censInformation): No censoring #>   Minimization message ($message):   #>      #>  #>  WARNINGS AND ERRORS (IF ANY) FOR PROBLEM    1 #>  #>  (WARNING  2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #>  #>      #> 0MINIMIZATION SUCCESSFUL #>  NO. OF FUNCTION EVALUATIONS USED:      320 #>  NO. OF SIG. DIGITS IN FINAL EST.:  2.5 #>  #>     IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.43e-06 #>     PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.41e-06 #>     IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.25e-05, 0.0418); atol=0.00167 #>     PRED absolute difference compared to Nonmem PRED: 95% percentile: (1.41e-07,0.00382); atol=6.41e-06 #>     nonmem2rx model file: '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl'  #>  #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 2,280 × 25 #>   ID     TIME    DV  PRED    RES IPRED  IRES  IWRES eta.cl eta.v  eta.q eta.v2 #>   <fct> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl> #> 1 1      0.25 1041. 1750. -710.  1215. -175. -0.732 -0.144 0.375 0.0650  0.241 #> 2 1      0.5  1629  1700.  -70.8 1192.  437.  1.87  -0.144 0.375 0.0650  0.241 #> 3 1      0.75  878. 1651. -774.  1169. -291. -1.27  -0.144 0.375 0.0650  0.241 #> # ℹ 2,277 more rows #> # ℹ 13 more variables: f <dbl>, CENTRAL <dbl>, PERI <dbl>, cl <dbl>, v <dbl>, #> #   q <dbl>, v2 <dbl>, v1 <dbl>, scale1 <dbl>, k21 <dbl>, k12 <dbl>, tad <dbl>, #> #   dosenum <dbl>  # }"},{"path":"/reference/bblDatToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Convert nlmixr2-compatible data formats (possible)","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"bblDatToMonolix(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )  bblDatToNonmem(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )  bblDatToRxode(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )  bblDatToMrgsolve(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )  bblDatToPknca(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )"},{"path":"/reference/bblDatToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"model rxode2 model conversion data Input dataset. table table control; mostly figure additional columns keep. rxControl rxode2 control options; figure handle addl dosing information. env NULL (default) nothing done.  environment, function nlmixr2est::.foceiPreProcessData(data, env, model, rxControl) called provided environment.","code":""},{"path":"/reference/bblDatToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"function bblDatToMonolix() return list : Monolix compatible dataset ($monolix) Monolix ADM information ($adm) function nlmixrDataToNonmem() return dataset compatible NONMEM. function nlmixrDataToMrgsolve() return dataset compatible mrgsolve.  Unlike NONMEM, supports replacement events evid=8 (note rxode2 replacement evid 5). function nlmixrDataToRxode() normalize dataset use newer evid definitions closer NONMEM instead classic definitions used lower level","code":""},{"path":"/reference/bblDatToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Matthew L. Fidler","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"pk.turnover.emax3 <- function() {   ini({     tktr <- log(1)     tka <- log(1)     tcl <- log(0.1)     tv <- log(10)     ##     eta.ktr ~ 1     eta.ka ~ 1     eta.cl ~ 2     eta.v ~ 1     prop.err <- 0.1     pkadd.err <- 0.1     ##     temax <- logit(0.8)     tec50 <- log(0.5)     tkout <- log(0.05)     te0 <- log(100)     ##     eta.emax ~ .5     eta.ec50  ~ .5     eta.kout ~ .5     eta.e0 ~ .5     ##     pdadd.err <- 10   })   model({     ktr <- exp(tktr + eta.ktr)     ka <- exp(tka + eta.ka)     cl <- exp(tcl + eta.cl)     v <- exp(tv + eta.v)     emax = expit(temax+eta.emax)     ec50 =  exp(tec50 + eta.ec50)     kout = exp(tkout + eta.kout)     e0 = exp(te0 + eta.e0)     ##     DCP = center/v     PD=1-emax*DCP/(ec50+DCP)     ##     effect(0) = e0     kin = e0*kout     ##     d/dt(depot) = -ktr * depot     d/dt(gut) =  ktr * depot -ka * gut     d/dt(center) =  ka * gut - cl / v * center     d/dt(effect) = kin*PD -kout*effect     ##     cp = center / v     cp ~ prop(prop.err) + add(pkadd.err)     effect ~ add(pdadd.err) | pca   }) }  bblDatToMonolix(pk.turnover.emax3, nlmixr2data::warfarin) #>   #>   #> $monolix #>     ID  TIME EVID   AMT II    DV ADM YTYPE SS nlmixrRowNums #> 1    1   0.0    1 100.0  0    NA   1     0  0             1 #> 2    1   0.5    0    NA  0   0.0   0     1  0             2 #> 3    1   1.0    0    NA  0   1.9   0     1  0             3 #> 4    1   2.0    0    NA  0   3.3   0     1  0             4 #> 5    1   3.0    0    NA  0   6.6   0     1  0             5 #> 6    1   6.0    0    NA  0   9.1   0     1  0             6 #> 7    1   9.0    0    NA  0  10.8   0     1  0             7 #> 8    1  12.0    0    NA  0   8.6   0     1  0             8 #> 9    1  24.0    0    NA  0   5.6   0     1  0             9 #> 10   1  24.0    0    NA  0  44.0   0     2  0            10 #> 11   1  36.0    0    NA  0   4.0   0     1  0            11 #> 12   1  36.0    0    NA  0  27.0   0     2  0            12 #> 13   1  48.0    0    NA  0   2.7   0     1  0            13 #> 14   1  48.0    0    NA  0  28.0   0     2  0            14 #> 15   1  72.0    0    NA  0   0.8   0     1  0            15 #> 16   1  72.0    0    NA  0  31.0   0     2  0            16 #> 17   1  96.0    0    NA  0  60.0   0     2  0            17 #> 18   1 120.0    0    NA  0  65.0   0     2  0            18 #> 19   1 144.0    0    NA  0  71.0   0     2  0            19 #> 20   2   0.0    1 100.0  0    NA   1     0  0            20 #> 21   2   0.0    0    NA  0 100.0   0     2  0            21 #> 22   2  24.0    0    NA  0   9.2   0     1  0            22 #> 23   2  24.0    0    NA  0  49.0   0     2  0            23 #> 24   2  36.0    0    NA  0   8.5   0     1  0            24 #> 25   2  36.0    0    NA  0  32.0   0     2  0            25 #> 26   2  48.0    0    NA  0   6.4   0     1  0            26 #> 27   2  48.0    0    NA  0  26.0   0     2  0            27 #> 28   2  72.0    0    NA  0   4.8   0     1  0            28 #> 29   2  72.0    0    NA  0  22.0   0     2  0            29 #> 30   2  96.0    0    NA  0   3.1   0     1  0            30 #> 31   2  96.0    0    NA  0  28.0   0     2  0            31 #> 32   2 120.0    0    NA  0   2.5   0     1  0            32 #> 33   2 120.0    0    NA  0  33.0   0     2  0            33 #> 34   3   0.0    1 100.0  0    NA   1     0  0            34 #> 35   3   0.0    0    NA  0 100.0   0     2  0            35 #> 36   3   0.5    0    NA  0   0.0   0     1  0            36 #> 37   3   2.0    0    NA  0   8.4   0     1  0            37 #> 38   3   3.0    0    NA  0   9.7   0     1  0            38 #> 39   3   6.0    0    NA  0   9.8   0     1  0            39 #> 40   3  12.0    0    NA  0  11.0   0     1  0            40 #> 41   3  24.0    0    NA  0   8.3   0     1  0            41 #> 42   3  24.0    0    NA  0  46.0   0     2  0            42 #> 43   3  36.0    0    NA  0   7.7   0     1  0            43 #> 44   3  36.0    0    NA  0  22.0   0     2  0            44 #> 45   3  48.0    0    NA  0   6.3   0     1  0            45 #> 46   3  48.0    0    NA  0  19.0   0     2  0            46 #> 47   3  72.0    0    NA  0   4.1   0     1  0            47 #> 48   3  72.0    0    NA  0  20.0   0     2  0            48 #> 49   3  96.0    0    NA  0   3.0   0     1  0            49 #> 50   3  96.0    0    NA  0  42.0   0     2  0            50 #> 51   3 120.0    0    NA  0   1.4   0     1  0            51 #> 52   3 120.0    0    NA  0  49.0   0     2  0            52 #> 53   3 144.0    0    NA  0  54.0   0     2  0            53 #> 54   4   0.0    1 120.0  0    NA   1     0  0            54 #> 55   4   0.0    0    NA  0 100.0   0     2  0            55 #> 56   4   3.0    0    NA  0  12.0   0     1  0            56 #> 57   4   6.0    0    NA  0  13.2   0     1  0            57 #> 58   4   9.0    0    NA  0  14.4   0     1  0            58 #> 59   4  24.0    0    NA  0   9.6   0     1  0            59 #> 60   4  24.0    0    NA  0  30.0   0     2  0            60 #> 61   4  36.0    0    NA  0   8.2   0     1  0            61 #> 62   4  36.0    0    NA  0  24.0   0     2  0            62 #> 63   4  48.0    0    NA  0   7.8   0     1  0            63 #> 64   4  48.0    0    NA  0  13.0   0     2  0            64 #> 65   4  72.0    0    NA  0   5.8   0     1  0            65 #> 66   4  72.0    0    NA  0   9.0   0     2  0            66 #> 67   4  96.0    0    NA  0   4.3   0     1  0            67 #> 68   4  96.0    0    NA  0   9.0   0     2  0            68 #> 69   4 120.0    0    NA  0   3.0   0     1  0            69 #> 70   4 120.0    0    NA  0  11.0   0     2  0            70 #> 71   4 144.0    0    NA  0  12.0   0     2  0            71 #> 72   5   0.0    1  60.0  0    NA   1     0  0            72 #> 73   5   0.0    0    NA  0  82.0   0     2  0            73 #> 74   5   3.0    0    NA  0  11.1   0     1  0            74 #> 75   5   6.0    0    NA  0  11.9   0     1  0            75 #> 76   5   9.0    0    NA  0   9.8   0     1  0            76 #> 77   5  12.0    0    NA  0  11.0   0     1  0            77 #> 78   5  24.0    0    NA  0   8.5   0     1  0            78 #> 79   5  24.0    0    NA  0  43.0   0     2  0            79 #> 80   5  36.0    0    NA  0   7.6   0     1  0            80 #> 81   5  36.0    0    NA  0  25.0   0     2  0            81 #> 82   5  48.0    0    NA  0   5.4   0     1  0            82 #> 83   5  48.0    0    NA  0  18.0   0     2  0            83 #> 84   5  72.0    0    NA  0   4.5   0     1  0            84 #> 85   5  72.0    0    NA  0  17.0   0     2  0            85 #> 86   5  96.0    0    NA  0   3.3   0     1  0            86 #> 87   5  96.0    0    NA  0  23.0   0     2  0            87 #> 88   5 120.0    0    NA  0   2.3   0     1  0            88 #> 89   5 120.0    0    NA  0  29.0   0     2  0            89 #> 90   5 144.0    0    NA  0  41.0   0     2  0            90 #> 91   6   0.0    1 113.0  0    NA   1     0  0            91 #> 92   6   0.0    0    NA  0 100.0   0     2  0            92 #> 93   6   6.0    0    NA  0   8.6   0     1  0            93 #> 94   6  12.0    0    NA  0   8.6   0     1  0            94 #> 95   6  24.0    0    NA  0   7.0   0     1  0            95 #> 96   6  24.0    0    NA  0  34.0   0     2  0            96 #> 97   6  36.0    0    NA  0   5.7   0     1  0            97 #> 98   6  36.0    0    NA  0  23.0   0     2  0            98 #> 99   6  48.0    0    NA  0   4.7   0     1  0            99 #> 100  6  48.0    0    NA  0  20.0   0     2  0           100 #> 101  6  72.0    0    NA  0   3.3   0     1  0           101 #> 102  6  72.0    0    NA  0  16.0   0     2  0           102 #> 103  6  96.0    0    NA  0   2.3   0     1  0           103 #> 104  6  96.0    0    NA  0  17.0   0     2  0           104 #> 105  6 120.0    0    NA  0   1.7   0     1  0           105 #> 106  6 120.0    0    NA  0  18.0   0     2  0           106 #> 107  6 144.0    0    NA  0  25.0   0     2  0           107 #> 108  7   0.0    1  90.0  0    NA   1     0  0           108 #> 109  7   3.0    0    NA  0  13.4   0     1  0           109 #> 110  7   6.0    0    NA  0  12.4   0     1  0           110 #> 111  7   9.0    0    NA  0  12.7   0     1  0           111 #> 112  7  12.0    0    NA  0   8.8   0     1  0           112 #> 113  7  24.0    0    NA  0   6.1   0     1  0           113 #> 114  7  24.0    0    NA  0  36.0   0     2  0           114 #> 115  7  36.0    0    NA  0   3.5   0     1  0           115 #> 116  7  36.0    0    NA  0  33.0   0     2  0           116 #> 117  7  48.0    0    NA  0   1.8   0     1  0           117 #> 118  7  48.0    0    NA  0  28.0   0     2  0           118 #> 119  7  72.0    0    NA  0   1.5   0     1  0           119 #> 120  7  72.0    0    NA  0  52.0   0     2  0           120 #> 121  7  96.0    0    NA  0   1.0   0     1  0           121 #> 122  7  96.0    0    NA  0  80.0   0     2  0           122 #> 123  7 120.0    0    NA  0  90.0   0     2  0           123 #> 124  7 144.0    0    NA  0 100.0   0     2  0           124 #> 125  8   0.0    1 135.0  0    NA   1     0  0           125 #> 126  8   0.0    0    NA  0  88.0   0     2  0           126 #> 127  8   2.0    0    NA  0  17.6   0     1  0           127 #> 128  8   3.0    0    NA  0  17.3   0     1  0           128 #> 129  8   6.0    0    NA  0  15.0   0     1  0           129 #> 130  8   9.0    0    NA  0  15.0   0     1  0           130 #> 131  8  12.0    0    NA  0  12.4   0     1  0           131 #> 132  8  24.0    0    NA  0   7.9   0     1  0           132 #> 133  8  24.0    0    NA  0  35.0   0     2  0           133 #> 134  8  36.0    0    NA  0   7.9   0     1  0           134 #> 135  8  36.0    0    NA  0  20.0   0     2  0           135 #> 136  8  48.0    0    NA  0   5.1   0     1  0           136 #> 137  8  48.0    0    NA  0  12.0   0     2  0           137 #> 138  8  72.0    0    NA  0   3.6   0     1  0           138 #> 139  8  72.0    0    NA  0  16.0   0     2  0           139 #> 140  8  96.0    0    NA  0   2.4   0     1  0           140 #> 141  8  96.0    0    NA  0  23.0   0     2  0           141 #> 142  8 120.0    0    NA  0   2.0   0     1  0           142 #> 143  8 120.0    0    NA  0  36.0   0     2  0           143 #> 144  8 144.0    0    NA  0  48.0   0     2  0           144 #> 145  9   0.0    1  75.0  0    NA   1     0  0           145 #> 146  9   0.0    0    NA  0  92.0   0     2  0           146 #> 147  9   0.5    0    NA  0   0.0   0     1  0           147 #> 148  9   1.0    0    NA  0   1.0   0     1  0           148 #> 149  9   2.0    0    NA  0   4.6   0     1  0           149 #> 150  9   3.0    0    NA  0  12.7   0     1  0           150 #> 151  9   3.0    0    NA  0   8.0   0     1  0           151 #> 152  9   6.0    0    NA  0  12.7   0     1  0           152 #> 153  9   6.0    0    NA  0  11.5   0     1  0           153 #> 154  9   9.0    0    NA  0  12.9   0     1  0           154 #> 155  9   9.0    0    NA  0  11.4   0     1  0           155 #> 156  9  12.0    0    NA  0  11.4   0     1  0           156 #> 157  9  12.0    0    NA  0  11.0   0     1  0           157 #> 158  9  24.0    0    NA  0   9.1   0     1  0           158 #> 159  9  24.0    0    NA  0  33.0   0     2  0           159 #> 160  9  36.0    0    NA  0   8.2   0     1  0           160 #> 161  9  36.0    0    NA  0  22.0   0     2  0           161 #> 162  9  48.0    0    NA  0   5.9   0     1  0           162 #> 163  9  48.0    0    NA  0  16.0   0     2  0           163 #> 164  9  72.0    0    NA  0   3.6   0     1  0           164 #> 165  9  72.0    0    NA  0  18.0   0     2  0           165 #> 166  9  96.0    0    NA  0   1.7   0     1  0           166 #> 167  9  96.0    0    NA  0  32.0   0     2  0           167 #> 168  9 120.0    0    NA  0   1.1   0     1  0           168 #> 169  9 120.0    0    NA  0  30.0   0     2  0           169 #> 170  9 144.0    0    NA  0  45.0   0     2  0           170 #> 171 10   0.0    1 105.0  0    NA   1     0  0           171 #> 172 10   0.0    0    NA  0  90.0   0     2  0           172 #> 173 10  24.0    0    NA  0   8.6   0     1  0           173 #> 174 10  24.0    0    NA  0  39.0   0     2  0           174 #> 175 10  36.0    0    NA  0   8.0   0     1  0           175 #> 176 10  36.0    0    NA  0  22.0   0     2  0           176 #> 177 10  48.0    0    NA  0   6.0   0     1  0           177 #> 178 10  48.0    0    NA  0  17.0   0     2  0           178 #> 179 10  72.0    0    NA  0   4.4   0     1  0           179 #> 180 10  72.0    0    NA  0  17.0   0     2  0           180 #> 181 10  96.0    0    NA  0   3.6   0     1  0           181 #> 182 10  96.0    0    NA  0  22.0   0     2  0           182 #> 183 10 120.0    0    NA  0   2.8   0     1  0           183 #> 184 10 120.0    0    NA  0  25.0   0     2  0           184 #> 185 10 144.0    0    NA  0  33.0   0     2  0           185 #> 186 11   0.0    1 123.0  0    NA   1     0  0           186 #> 187 11   0.0    0    NA  0 100.0   0     2  0           187 #> 188 11   1.5    0    NA  0  11.4   0     1  0           188 #> 189 11   3.0    0    NA  0  15.4   0     1  0           189 #> 190 11   6.0    0    NA  0  17.5   0     1  0           190 #> 191 11  12.0    0    NA  0  14.0   0     1  0           191 #> 192 11  24.0    0    NA  0   9.0   0     1  0           192 #> 193 11  24.0    0    NA  0  37.0   0     2  0           193 #> 194 11  36.0    0    NA  0   8.9   0     1  0           194 #> 195 11  36.0    0    NA  0  24.0   0     2  0           195 #> 196 11  48.0    0    NA  0   6.6   0     1  0           196 #> 197 11  48.0    0    NA  0  14.0   0     2  0           197 #> 198 11  72.0    0    NA  0   4.2   0     1  0           198 #> 199 11  72.0    0    NA  0  11.0   0     2  0           199 #> 200 11  96.0    0    NA  0   3.6   0     1  0           200 #> 201 11  96.0    0    NA  0  14.0   0     2  0           201 #> 202 11 120.0    0    NA  0   2.6   0     1  0           202 #> 203 11 120.0    0    NA  0  23.0   0     2  0           203 #> 204 11 144.0    0    NA  0  33.0   0     2  0           204 #> 205 12   0.0    1 113.0  0    NA   1     0  0           205 #> 206 12   0.0    0    NA  0  85.0   0     2  0           206 #> 207 12   1.5    0    NA  0   0.6   0     1  0           207 #> 208 12   3.0    0    NA  0   2.8   0     1  0           208 #> 209 12   6.0    0    NA  0  13.8   0     1  0           209 #> 210 12   9.0    0    NA  0  15.0   0     1  0           210 #> 211 12  24.0    0    NA  0  10.5   0     1  0           211 #> 212 12  24.0    0    NA  0  25.0   0     2  0           212 #> 213 12  36.0    0    NA  0   9.1   0     1  0           213 #> 214 12  36.0    0    NA  0  15.0   0     2  0           214 #> 215 12  48.0    0    NA  0   6.6   0     1  0           215 #> 216 12  48.0    0    NA  0  11.0   0     2  0           216 #> 217 12  72.0    0    NA  0   4.9   0     1  0           217 #> 218 12  96.0    0    NA  0   2.4   0     1  0           218 #> 219 12 120.0    0    NA  0   1.9   0     1  0           219 #> 220 13   0.0    1 113.0  0    NA   1     0  0           220 #> 221 13   0.0    0    NA  0  88.0   0     2  0           221 #> 222 13   1.5    0    NA  0   3.6   0     1  0           222 #> 223 13   3.0    0    NA  0  12.9   0     1  0           223 #> 224 13   6.0    0    NA  0  12.9   0     1  0           224 #> 225 13   9.0    0    NA  0  10.2   0     1  0           225 #> 226 13  24.0    0    NA  0   6.4   0     1  0           226 #> 227 13  24.0    0    NA  0  41.0   0     2  0           227 #> 228 13  36.0    0    NA  0   6.9   0     1  0           228 #> 229 13  36.0    0    NA  0  23.0   0     2  0           229 #> 230 13  48.0    0    NA  0   4.5   0     1  0           230 #> 231 13  48.0    0    NA  0  16.0   0     2  0           231 #> 232 13  72.0    0    NA  0   3.2   0     1  0           232 #> 233 13  72.0    0    NA  0  14.0   0     2  0           233 #> 234 13  96.0    0    NA  0   2.4   0     1  0           234 #> 235 13  96.0    0    NA  0  18.0   0     2  0           235 #> 236 13 120.0    0    NA  0   1.3   0     1  0           236 #> 237 13 120.0    0    NA  0  22.0   0     2  0           237 #> 238 13 144.0    0    NA  0  35.0   0     2  0           238 #> 239 14   0.0    1  75.0  0    NA   1     0  0           239 #> 240 14   0.0    0    NA  0  85.0   0     2  0           240 #> 241 14   0.5    0    NA  0   0.0   0     1  0           241 #> 242 14   1.0    0    NA  0   2.7   0     1  0           242 #> 243 14   2.0    0    NA  0  11.6   0     1  0           243 #> 244 14   3.0    0    NA  0  11.6   0     1  0           244 #> 245 14   6.0    0    NA  0  11.3   0     1  0           245 #> 246 14   9.0    0    NA  0   9.7   0     1  0           246 #> 247 14  24.0    0    NA  0   6.5   0     1  0           247 #> 248 14  24.0    0    NA  0  32.0   0     2  0           248 #> 249 14  36.0    0    NA  0   5.2   0     1  0           249 #> 250 14  36.0    0    NA  0  22.0   0     2  0           250 #> 251 14  48.0    0    NA  0   3.6   0     1  0           251 #> 252 14  48.0    0    NA  0  21.0   0     2  0           252 #> 253 14  72.0    0    NA  0   2.4   0     1  0           253 #> 254 14  72.0    0    NA  0  28.0   0     2  0           254 #> 255 14  96.0    0    NA  0   0.9   0     1  0           255 #> 256 14  96.0    0    NA  0  38.0   0     2  0           256 #> 257 14 120.0    0    NA  0  46.0   0     2  0           257 #> 258 14 144.0    0    NA  0  65.0   0     2  0           258 #> 259 15   0.0    1  85.0  0    NA   1     0  0           259 #> 260 15   0.0    0    NA  0 100.0   0     2  0           260 #> 261 15   1.0    0    NA  0   6.6   0     1  0           261 #> 262 15   3.0    0    NA  0  11.9   0     1  0           262 #> 263 15   6.0    0    NA  0  11.7   0     1  0           263 #> 264 15   9.0    0    NA  0  12.2   0     1  0           264 #> 265 15  24.0    0    NA  0   8.1   0     1  0           265 #> 266 15  24.0    0    NA  0  43.0   0     2  0           266 #> 267 15  36.0    0    NA  0   7.4   0     1  0           267 #> 268 15  36.0    0    NA  0  26.0   0     2  0           268 #> 269 15  48.0    0    NA  0   6.8   0     1  0           269 #> 270 15  48.0    0    NA  0  15.0   0     2  0           270 #> 271 15  72.0    0    NA  0   5.3   0     1  0           271 #> 272 15  72.0    0    NA  0  13.0   0     2  0           272 #> 273 15  96.0    0    NA  0   3.0   0     1  0           273 #> 274 15  96.0    0    NA  0  21.0   0     2  0           274 #> 275 15 120.0    0    NA  0   2.0   0     1  0           275 #> 276 15 120.0    0    NA  0  28.0   0     2  0           276 #> 277 15 144.0    0    NA  0  39.0   0     2  0           277 #> 278 16   0.0    1  87.0  0    NA   1     0  0           278 #> 279 16   0.0    0    NA  0 100.0   0     2  0           279 #> 280 16  24.0    0    NA  0  10.4   0     1  0           280 #> 281 16  24.0    0    NA  0  42.0   0     2  0           281 #> 282 16  36.0    0    NA  0   8.9   0     1  0           282 #> 283 16  36.0    0    NA  0  32.0   0     2  0           283 #> 284 16  48.0    0    NA  0   7.0   0     1  0           284 #> 285 16  48.0    0    NA  0  26.0   0     2  0           285 #> 286 16  72.0    0    NA  0   4.4   0     1  0           286 #> 287 16  72.0    0    NA  0  31.0   0     2  0           287 #> 288 16  96.0    0    NA  0   3.2   0     1  0           288 #> 289 16  96.0    0    NA  0  33.0   0     2  0           289 #> 290 16 120.0    0    NA  0   2.4   0     1  0           290 #> 291 16 120.0    0    NA  0  54.0   0     2  0           291 #> 292 17   0.0    1 117.0  0    NA   1     0  0           292 #> 293 17   0.0    0    NA  0 100.0   0     2  0           293 #> 294 17  24.0    0    NA  0   7.6   0     1  0           294 #> 295 17  24.0    0    NA  0  35.0   0     2  0           295 #> 296 17  36.0    0    NA  0   6.4   0     1  0           296 #> 297 17  36.0    0    NA  0  23.0   0     2  0           297 #> 298 17  48.0    0    NA  0   6.0   0     1  0           298 #> 299 17  48.0    0    NA  0  17.0   0     2  0           299 #> 300 17  72.0    0    NA  0   4.0   0     1  0           300 #> 301 17  72.0    0    NA  0  18.0   0     2  0           301 #> 302 17  96.0    0    NA  0   3.1   0     1  0           302 #> 303 17  96.0    0    NA  0  18.0   0     2  0           303 #> 304 17 120.0    0    NA  0   2.0   0     1  0           304 #> 305 17 120.0    0    NA  0  21.0   0     2  0           305 #> 306 18   0.0    1 112.0  0    NA   1     0  0           306 #> 307 18   0.0    0    NA  0 100.0   0     2  0           307 #> 308 18  24.0    0    NA  0   7.6   0     1  0           308 #> 309 18  24.0    0    NA  0  32.0   0     2  0           309 #> 310 18  36.0    0    NA  0   6.6   0     1  0           310 #> 311 18  36.0    0    NA  0  20.0   0     2  0           311 #> 312 18  48.0    0    NA  0   5.4   0     1  0           312 #> 313 18  48.0    0    NA  0  18.0   0     2  0           313 #> 314 18  72.0    0    NA  0   3.4   0     1  0           314 #> 315 18  72.0    0    NA  0  18.0   0     2  0           315 #> 316 18  96.0    0    NA  0   1.2   0     1  0           316 #> 317 18  96.0    0    NA  0  19.0   0     2  0           317 #> 318 18 120.0    0    NA  0   0.9   0     1  0           318 #> 319 18 120.0    0    NA  0  29.0   0     2  0           319 #> 320 19   0.0    1  95.5  0    NA   1     0  0           320 #> 321 19   0.0    0    NA  0 100.0   0     2  0           321 #> 322 19  24.0    0    NA  0   6.6   0     1  0           322 #> 323 19  24.0    0    NA  0  33.0   0     2  0           323 #> 324 19  36.0    0    NA  0   5.3   0     1  0           324 #> 325 19  36.0    0    NA  0  28.0   0     2  0           325 #> 326 19  48.0    0    NA  0   3.6   0     1  0           326 #> 327 19  48.0    0    NA  0  18.0   0     2  0           327 #> 328 19  72.0    0    NA  0   2.7   0     1  0           328 #> 329 19  72.0    0    NA  0  18.0   0     2  0           329 #> 330 19  96.0    0    NA  0   1.4   0     1  0           330 #> 331 19  96.0    0    NA  0  17.0   0     2  0           331 #> 332 19 120.0    0    NA  0   1.1   0     1  0           332 #> 333 19 120.0    0    NA  0  26.0   0     2  0           333 #> 334 20   0.0    1  88.5  0    NA   1     0  0           334 #> 335 20   0.0    0    NA  0 100.0   0     2  0           335 #> 336 20  24.0    0    NA  0   9.6   0     1  0           336 #> 337 20  24.0    0    NA  0  41.0   0     2  0           337 #> 338 20  36.0    0    NA  0   8.0   0     1  0           338 #> 339 20  36.0    0    NA  0  30.0   0     2  0           339 #> 340 20  48.0    0    NA  0   6.6   0     1  0           340 #> 341 20  48.0    0    NA  0  22.0   0     2  0           341 #> 342 20  72.0    0    NA  0   5.6   0     1  0           342 #> 343 20  72.0    0    NA  0  23.0   0     2  0           343 #> 344 20  96.0    0    NA  0   3.5   0     1  0           344 #> 345 20  96.0    0    NA  0  23.0   0     2  0           345 #> 346 20 120.0    0    NA  0   2.3   0     1  0           346 #> 347 20 120.0    0    NA  0  35.0   0     2  0           347 #> 348 21   0.0    1  93.0  0    NA   1     0  0           348 #> 349 21   0.0    0    NA  0 100.0   0     2  0           349 #> 350 21  24.0    0    NA  0   7.3   0     1  0           350 #> 351 21  24.0    0    NA  0  46.0   0     2  0           351 #> 352 21  36.0    0    NA  0   6.1   0     1  0           352 #> 353 21  36.0    0    NA  0  27.0   0     2  0           353 #> 354 21  48.0    0    NA  0   4.3   0     1  0           354 #> 355 21  48.0    0    NA  0  22.0   0     2  0           355 #> 356 21  72.0    0    NA  0   3.2   0     1  0           356 #> 357 21  72.0    0    NA  0  36.0   0     2  0           357 #> 358 21  96.0    0    NA  0   2.3   0     1  0           358 #> 359 21  96.0    0    NA  0  40.0   0     2  0           359 #> 360 21 120.0    0    NA  0   1.9   0     1  0           360 #> 361 21 120.0    0    NA  0  44.0   0     2  0           361 #> 362 22   0.0    1  87.0  0    NA   1     0  0           362 #> 363 22   0.0    0    NA  0 100.0   0     2  0           363 #> 364 22  24.0    0    NA  0   8.9   0     1  0           364 #> 365 22  24.0    0    NA  0  35.0   0     2  0           365 #> 366 22  36.0    0    NA  0   8.4   0     1  0           366 #> 367 22  36.0    0    NA  0  27.0   0     2  0           367 #> 368 22  48.0    0    NA  0   8.0   0     1  0           368 #> 369 22  48.0    0    NA  0  23.0   0     2  0           369 #> 370 22  72.0    0    NA  0   4.4   0     1  0           370 #> 371 22  72.0    0    NA  0  27.0   0     2  0           371 #> 372 22  96.0    0    NA  0   3.2   0     1  0           372 #> 373 22  96.0    0    NA  0  43.0   0     2  0           373 #> 374 22 120.0    0    NA  0   1.7   0     1  0           374 #> 375 22 120.0    0    NA  0  43.0   0     2  0           375 #> 376 23   0.0    1 110.0  0    NA   1     0  0           376 #> 377 23   0.0    0    NA  0 100.0   0     2  0           377 #> 378 23  24.0    0    NA  0   9.8   0     1  0           378 #> 379 23  24.0    0    NA  0  34.0   0     2  0           379 #> 380 23  36.0    0    NA  0   8.4   0     1  0           380 #> 381 23  36.0    0    NA  0  24.0   0     2  0           381 #> 382 23  48.0    0    NA  0   6.6   0     1  0           382 #> 383 23  48.0    0    NA  0  15.0   0     2  0           383 #> 384 23  72.0    0    NA  0   4.8   0     1  0           384 #> 385 23  72.0    0    NA  0  15.0   0     2  0           385 #> 386 23  96.0    0    NA  0   3.2   0     1  0           386 #> 387 23  96.0    0    NA  0  19.0   0     2  0           387 #> 388 23 120.0    0    NA  0   2.4   0     1  0           388 #> 389 23 120.0    0    NA  0  19.0   0     2  0           389 #> 390 24   0.0    1 115.0  0    NA   1     0  0           390 #> 391 24   0.0    0    NA  0  88.0   0     2  0           391 #> 392 24  24.0    0    NA  0   8.2   0     1  0           392 #> 393 24  24.0    0    NA  0  37.0   0     2  0           393 #> 394 24  36.0    0    NA  0   7.5   0     1  0           394 #> 395 24  36.0    0    NA  0  20.0   0     2  0           395 #> 396 24  48.0    0    NA  0   6.8   0     1  0           396 #> 397 24  48.0    0    NA  0  20.0   0     2  0           397 #> 398 24  72.0    0    NA  0   5.5   0     1  0           398 #> 399 24  72.0    0    NA  0  26.0   0     2  0           399 #> 400 24  96.0    0    NA  0   4.5   0     1  0           400 #> 401 24  96.0    0    NA  0  28.0   0     2  0           401 #> 402 24 120.0    0    NA  0   3.7   0     1  0           402 #> 403 24 120.0    0    NA  0  50.0   0     2  0           403 #> 404 25   0.0    1 112.0  0    NA   1     0  0           404 #> 405 25   0.0    0    NA  0 100.0   0     2  0           405 #> 406 25  24.0    0    NA  0  11.0   0     1  0           406 #> 407 25  24.0    0    NA  0  32.0   0     2  0           407 #> 408 25  36.0    0    NA  0  10.0   0     1  0           408 #> 409 25  36.0    0    NA  0  20.0   0     2  0           409 #> 410 25  48.0    0    NA  0   8.2   0     1  0           410 #> 411 25  48.0    0    NA  0  17.0   0     2  0           411 #> 412 25  72.0    0    NA  0   6.0   0     1  0           412 #> 413 25  72.0    0    NA  0  19.0   0     2  0           413 #> 414 25  96.0    0    NA  0   3.7   0     1  0           414 #> 415 25  96.0    0    NA  0  21.0   0     2  0           415 #> 416 25 120.0    0    NA  0   2.6   0     1  0           416 #> 417 25 120.0    0    NA  0  30.0   0     2  0           417 #> 418 26   0.0    1 120.0  0    NA   1     0  0           418 #> 419 26   0.0    0    NA  0 100.0   0     2  0           419 #> 420 26  24.0    0    NA  0  10.0   0     1  0           420 #> 421 26  24.0    0    NA  0  41.0   0     2  0           421 #> 422 26  36.0    0    NA  0   9.0   0     1  0           422 #> 423 26  36.0    0    NA  0  28.0   0     2  0           423 #> 424 26  48.0    0    NA  0   7.3   0     1  0           424 #> 425 26  48.0    0    NA  0  19.0   0     2  0           425 #> 426 26  72.0    0    NA  0   5.2   0     1  0           426 #> 427 26  72.0    0    NA  0  17.0   0     2  0           427 #> 428 26  96.0    0    NA  0   3.7   0     1  0           428 #> 429 26  96.0    0    NA  0  17.0   0     2  0           429 #> 430 26 120.0    0    NA  0   2.7   0     1  0           430 #> 431 26 120.0    0    NA  0  24.0   0     2  0           431 #> 432 27   0.0    1 120.0  0    NA   1     0  0           432 #> 433 27   0.0    0    NA  0 100.0   0     2  0           433 #> 434 27  24.0    0    NA  0  11.8   0     1  0           434 #> 435 27  24.0    0    NA  0  32.0   0     2  0           435 #> 436 27  36.0    0    NA  0   9.2   0     1  0           436 #> 437 27  36.0    0    NA  0  21.0   0     2  0           437 #> 438 27  48.0    0    NA  0   7.7   0     1  0           438 #> 439 27  48.0    0    NA  0  19.0   0     2  0           439 #> 440 27  72.0    0    NA  0   4.9   0     1  0           440 #> 441 27  72.0    0    NA  0  22.0   0     2  0           441 #> 442 27  96.0    0    NA  0   3.4   0     1  0           442 #> 443 27  96.0    0    NA  0  33.0   0     2  0           443 #> 444 27 120.0    0    NA  0   2.7   0     1  0           444 #> 445 27 120.0    0    NA  0  46.0   0     2  0           445 #> 446 28   0.0    1 120.0  0    NA   1     0  0           446 #> 447 28   0.0    0    NA  0 100.0   0     2  0           447 #> 448 28  24.0    0    NA  0  10.1   0     1  0           448 #> 449 28  24.0    0    NA  0  39.0   0     2  0           449 #> 450 28  36.0    0    NA  0   8.0   0     1  0           450 #> 451 28  36.0    0    NA  0  25.0   0     2  0           451 #> 452 28  48.0    0    NA  0   6.0   0     1  0           452 #> 453 28  48.0    0    NA  0  16.0   0     2  0           453 #> 454 28  72.0    0    NA  0   4.9   0     1  0           454 #> 455 28  72.0    0    NA  0  14.0   0     2  0           455 #> 456 28  96.0    0    NA  0   3.4   0     1  0           456 #> 457 28  96.0    0    NA  0  15.0   0     2  0           457 #> 458 28 120.0    0    NA  0   2.0   0     1  0           458 #> 459 28 120.0    0    NA  0  20.0   0     2  0           459 #> 460 29   0.0    1 153.0  0    NA   1     0  0           460 #> 461 29   0.0    0    NA  0  86.0   0     2  0           461 #> 462 29  24.0    0    NA  0   8.3   0     1  0           462 #> 463 29  24.0    0    NA  0  35.0   0     2  0           463 #> 464 29  36.0    0    NA  0   7.0   0     1  0           464 #> 465 29  36.0    0    NA  0  21.0   0     2  0           465 #> 466 29  48.0    0    NA  0   5.6   0     1  0           466 #> 467 29  48.0    0    NA  0  18.0   0     2  0           467 #> 468 29  72.0    0    NA  0   4.1   0     1  0           468 #> 469 29  72.0    0    NA  0  20.0   0     2  0           469 #> 470 29  96.0    0    NA  0   3.1   0     1  0           470 #> 471 29  96.0    0    NA  0  29.0   0     2  0           471 #> 472 29 120.0    0    NA  0   2.2   0     1  0           472 #> 473 29 120.0    0    NA  0  41.0   0     2  0           473 #> 474 30   0.0    1 105.0  0    NA   1     0  0           474 #> 475 30   0.0    0    NA  0 100.0   0     2  0           475 #> 476 30  24.0    0    NA  0   9.9   0     1  0           476 #> 477 30  24.0    0    NA  0  45.0   0     2  0           477 #> 478 30  36.0    0    NA  0   7.5   0     1  0           478 #> 479 30  36.0    0    NA  0  24.0   0     2  0           479 #> 480 30  48.0    0    NA  0   6.5   0     1  0           480 #> 481 30  48.0    0    NA  0  23.0   0     2  0           481 #> 482 30  72.0    0    NA  0   4.1   0     1  0           482 #> 483 30  72.0    0    NA  0  26.0   0     2  0           483 #> 484 30  96.0    0    NA  0   2.9   0     1  0           484 #> 485 30  96.0    0    NA  0  28.0   0     2  0           485 #> 486 30 120.0    0    NA  0   2.3   0     1  0           486 #> 487 30 120.0    0    NA  0  39.0   0     2  0           487 #> 488 31   0.0    1 125.0  0    NA   1     0  0           488 #> 489 31   0.0    0    NA  0 100.0   0     2  0           489 #> 490 31  24.0    0    NA  0   9.5   0     1  0           490 #> 491 31  24.0    0    NA  0  45.0   0     2  0           491 #> 492 31  36.0    0    NA  0   7.8   0     1  0           492 #> 493 31  36.0    0    NA  0  30.0   0     2  0           493 #> 494 31  48.0    0    NA  0   6.4   0     1  0           494 #> 495 31  48.0    0    NA  0  24.0   0     2  0           495 #> 496 31  72.0    0    NA  0   4.5   0     1  0           496 #> 497 31  72.0    0    NA  0  22.0   0     2  0           497 #> 498 31  96.0    0    NA  0   3.4   0     1  0           498 #> 499 31  96.0    0    NA  0  28.0   0     2  0           499 #> 500 31 120.0    0    NA  0   2.5   0     1  0           500 #> 501 31 120.0    0    NA  0  42.0   0     2  0           501 #> 502 32   0.0    1  93.0  0    NA   1     0  0           502 #> 503 32   0.0    0    NA  0 100.0   0     2  0           503 #> 504 32  24.0    0    NA  0   8.9   0     1  0           504 #> 505 32  24.0    0    NA  0  36.0   0     2  0           505 #> 506 32  36.0    0    NA  0   7.7   0     1  0           506 #> 507 32  36.0    0    NA  0  27.0   0     2  0           507 #> 508 32  48.0    0    NA  0   6.9   0     1  0           508 #> 509 32  48.0    0    NA  0  24.0   0     2  0           509 #> 510 32  72.0    0    NA  0   4.4   0     1  0           510 #> 511 32  72.0    0    NA  0  23.0   0     2  0           511 #> 512 32  96.0    0    NA  0   3.5   0     1  0           512 #> 513 32  96.0    0    NA  0  20.0   0     2  0           513 #> 514 32 120.0    0    NA  0   2.5   0     1  0           514 #> 515 32 120.0    0    NA  0  22.0   0     2  0           515 #>  #> $adm #>   adm cmt  type #> 1   1   1 bolus #>   bblDatToNonmem(pk.turnover.emax3, nlmixr2data::warfarin) #>   #>   #>     ID  TIME EVID   AMT    DV CMT DVID nlmixrRowNums #> 1    1   0.0    1 100.0    NA   1   NA             1 #> 2    1   0.5    0    NA   0.0  NA    1             2 #> 3    1   1.0    0    NA   1.9  NA    1             3 #> 4    1   2.0    0    NA   3.3  NA    1             4 #> 5    1   3.0    0    NA   6.6  NA    1             5 #> 6    1   6.0    0    NA   9.1  NA    1             6 #> 7    1   9.0    0    NA  10.8  NA    1             7 #> 8    1  12.0    0    NA   8.6  NA    1             8 #> 9    1  24.0    0    NA   5.6  NA    1             9 #> 10   1  24.0    0    NA  44.0  NA    2            10 #> 11   1  36.0    0    NA   4.0  NA    1            11 #> 12   1  36.0    0    NA  27.0  NA    2            12 #> 13   1  48.0    0    NA   2.7  NA    1            13 #> 14   1  48.0    0    NA  28.0  NA    2            14 #> 15   1  72.0    0    NA   0.8  NA    1            15 #> 16   1  72.0    0    NA  31.0  NA    2            16 #> 17   1  96.0    0    NA  60.0  NA    2            17 #> 18   1 120.0    0    NA  65.0  NA    2            18 #> 19   1 144.0    0    NA  71.0  NA    2            19 #> 20   2   0.0    1 100.0    NA   1   NA            20 #> 21   2   0.0    0    NA 100.0  NA    2            21 #> 22   2  24.0    0    NA   9.2  NA    1            22 #> 23   2  24.0    0    NA  49.0  NA    2            23 #> 24   2  36.0    0    NA   8.5  NA    1            24 #> 25   2  36.0    0    NA  32.0  NA    2            25 #> 26   2  48.0    0    NA   6.4  NA    1            26 #> 27   2  48.0    0    NA  26.0  NA    2            27 #> 28   2  72.0    0    NA   4.8  NA    1            28 #> 29   2  72.0    0    NA  22.0  NA    2            29 #> 30   2  96.0    0    NA   3.1  NA    1            30 #> 31   2  96.0    0    NA  28.0  NA    2            31 #> 32   2 120.0    0    NA   2.5  NA    1            32 #> 33   2 120.0    0    NA  33.0  NA    2            33 #> 34   3   0.0    1 100.0    NA   1   NA            34 #> 35   3   0.0    0    NA 100.0  NA    2            35 #> 36   3   0.5    0    NA   0.0  NA    1            36 #> 37   3   2.0    0    NA   8.4  NA    1            37 #> 38   3   3.0    0    NA   9.7  NA    1            38 #> 39   3   6.0    0    NA   9.8  NA    1            39 #> 40   3  12.0    0    NA  11.0  NA    1            40 #> 41   3  24.0    0    NA   8.3  NA    1            41 #> 42   3  24.0    0    NA  46.0  NA    2            42 #> 43   3  36.0    0    NA   7.7  NA    1            43 #> 44   3  36.0    0    NA  22.0  NA    2            44 #> 45   3  48.0    0    NA   6.3  NA    1            45 #> 46   3  48.0    0    NA  19.0  NA    2            46 #> 47   3  72.0    0    NA   4.1  NA    1            47 #> 48   3  72.0    0    NA  20.0  NA    2            48 #> 49   3  96.0    0    NA   3.0  NA    1            49 #> 50   3  96.0    0    NA  42.0  NA    2            50 #> 51   3 120.0    0    NA   1.4  NA    1            51 #> 52   3 120.0    0    NA  49.0  NA    2            52 #> 53   3 144.0    0    NA  54.0  NA    2            53 #> 54   4   0.0    1 120.0    NA   1   NA            54 #> 55   4   0.0    0    NA 100.0  NA    2            55 #> 56   4   3.0    0    NA  12.0  NA    1            56 #> 57   4   6.0    0    NA  13.2  NA    1            57 #> 58   4   9.0    0    NA  14.4  NA    1            58 #> 59   4  24.0    0    NA   9.6  NA    1            59 #> 60   4  24.0    0    NA  30.0  NA    2            60 #> 61   4  36.0    0    NA   8.2  NA    1            61 #> 62   4  36.0    0    NA  24.0  NA    2            62 #> 63   4  48.0    0    NA   7.8  NA    1            63 #> 64   4  48.0    0    NA  13.0  NA    2            64 #> 65   4  72.0    0    NA   5.8  NA    1            65 #> 66   4  72.0    0    NA   9.0  NA    2            66 #> 67   4  96.0    0    NA   4.3  NA    1            67 #> 68   4  96.0    0    NA   9.0  NA    2            68 #> 69   4 120.0    0    NA   3.0  NA    1            69 #> 70   4 120.0    0    NA  11.0  NA    2            70 #> 71   4 144.0    0    NA  12.0  NA    2            71 #> 72   5   0.0    1  60.0    NA   1   NA            72 #> 73   5   0.0    0    NA  82.0  NA    2            73 #> 74   5   3.0    0    NA  11.1  NA    1            74 #> 75   5   6.0    0    NA  11.9  NA    1            75 #> 76   5   9.0    0    NA   9.8  NA    1            76 #> 77   5  12.0    0    NA  11.0  NA    1            77 #> 78   5  24.0    0    NA   8.5  NA    1            78 #> 79   5  24.0    0    NA  43.0  NA    2            79 #> 80   5  36.0    0    NA   7.6  NA    1            80 #> 81   5  36.0    0    NA  25.0  NA    2            81 #> 82   5  48.0    0    NA   5.4  NA    1            82 #> 83   5  48.0    0    NA  18.0  NA    2            83 #> 84   5  72.0    0    NA   4.5  NA    1            84 #> 85   5  72.0    0    NA  17.0  NA    2            85 #> 86   5  96.0    0    NA   3.3  NA    1            86 #> 87   5  96.0    0    NA  23.0  NA    2            87 #> 88   5 120.0    0    NA   2.3  NA    1            88 #> 89   5 120.0    0    NA  29.0  NA    2            89 #> 90   5 144.0    0    NA  41.0  NA    2            90 #> 91   6   0.0    1 113.0    NA   1   NA            91 #> 92   6   0.0    0    NA 100.0  NA    2            92 #> 93   6   6.0    0    NA   8.6  NA    1            93 #> 94   6  12.0    0    NA   8.6  NA    1            94 #> 95   6  24.0    0    NA   7.0  NA    1            95 #> 96   6  24.0    0    NA  34.0  NA    2            96 #> 97   6  36.0    0    NA   5.7  NA    1            97 #> 98   6  36.0    0    NA  23.0  NA    2            98 #> 99   6  48.0    0    NA   4.7  NA    1            99 #> 100  6  48.0    0    NA  20.0  NA    2           100 #> 101  6  72.0    0    NA   3.3  NA    1           101 #> 102  6  72.0    0    NA  16.0  NA    2           102 #> 103  6  96.0    0    NA   2.3  NA    1           103 #> 104  6  96.0    0    NA  17.0  NA    2           104 #> 105  6 120.0    0    NA   1.7  NA    1           105 #> 106  6 120.0    0    NA  18.0  NA    2           106 #> 107  6 144.0    0    NA  25.0  NA    2           107 #> 108  7   0.0    1  90.0    NA   1   NA           108 #> 109  7   3.0    0    NA  13.4  NA    1           109 #> 110  7   6.0    0    NA  12.4  NA    1           110 #> 111  7   9.0    0    NA  12.7  NA    1           111 #> 112  7  12.0    0    NA   8.8  NA    1           112 #> 113  7  24.0    0    NA   6.1  NA    1           113 #> 114  7  24.0    0    NA  36.0  NA    2           114 #> 115  7  36.0    0    NA   3.5  NA    1           115 #> 116  7  36.0    0    NA  33.0  NA    2           116 #> 117  7  48.0    0    NA   1.8  NA    1           117 #> 118  7  48.0    0    NA  28.0  NA    2           118 #> 119  7  72.0    0    NA   1.5  NA    1           119 #> 120  7  72.0    0    NA  52.0  NA    2           120 #> 121  7  96.0    0    NA   1.0  NA    1           121 #> 122  7  96.0    0    NA  80.0  NA    2           122 #> 123  7 120.0    0    NA  90.0  NA    2           123 #> 124  7 144.0    0    NA 100.0  NA    2           124 #> 125  8   0.0    1 135.0    NA   1   NA           125 #> 126  8   0.0    0    NA  88.0  NA    2           126 #> 127  8   2.0    0    NA  17.6  NA    1           127 #> 128  8   3.0    0    NA  17.3  NA    1           128 #> 129  8   6.0    0    NA  15.0  NA    1           129 #> 130  8   9.0    0    NA  15.0  NA    1           130 #> 131  8  12.0    0    NA  12.4  NA    1           131 #> 132  8  24.0    0    NA   7.9  NA    1           132 #> 133  8  24.0    0    NA  35.0  NA    2           133 #> 134  8  36.0    0    NA   7.9  NA    1           134 #> 135  8  36.0    0    NA  20.0  NA    2           135 #> 136  8  48.0    0    NA   5.1  NA    1           136 #> 137  8  48.0    0    NA  12.0  NA    2           137 #> 138  8  72.0    0    NA   3.6  NA    1           138 #> 139  8  72.0    0    NA  16.0  NA    2           139 #> 140  8  96.0    0    NA   2.4  NA    1           140 #> 141  8  96.0    0    NA  23.0  NA    2           141 #> 142  8 120.0    0    NA   2.0  NA    1           142 #> 143  8 120.0    0    NA  36.0  NA    2           143 #> 144  8 144.0    0    NA  48.0  NA    2           144 #> 145  9   0.0    1  75.0    NA   1   NA           145 #> 146  9   0.0    0    NA  92.0  NA    2           146 #> 147  9   0.5    0    NA   0.0  NA    1           147 #> 148  9   1.0    0    NA   1.0  NA    1           148 #> 149  9   2.0    0    NA   4.6  NA    1           149 #> 150  9   3.0    0    NA  12.7  NA    1           150 #> 151  9   3.0    0    NA   8.0  NA    1           151 #> 152  9   6.0    0    NA  12.7  NA    1           152 #> 153  9   6.0    0    NA  11.5  NA    1           153 #> 154  9   9.0    0    NA  12.9  NA    1           154 #> 155  9   9.0    0    NA  11.4  NA    1           155 #> 156  9  12.0    0    NA  11.4  NA    1           156 #> 157  9  12.0    0    NA  11.0  NA    1           157 #> 158  9  24.0    0    NA   9.1  NA    1           158 #> 159  9  24.0    0    NA  33.0  NA    2           159 #> 160  9  36.0    0    NA   8.2  NA    1           160 #> 161  9  36.0    0    NA  22.0  NA    2           161 #> 162  9  48.0    0    NA   5.9  NA    1           162 #> 163  9  48.0    0    NA  16.0  NA    2           163 #> 164  9  72.0    0    NA   3.6  NA    1           164 #> 165  9  72.0    0    NA  18.0  NA    2           165 #> 166  9  96.0    0    NA   1.7  NA    1           166 #> 167  9  96.0    0    NA  32.0  NA    2           167 #> 168  9 120.0    0    NA   1.1  NA    1           168 #> 169  9 120.0    0    NA  30.0  NA    2           169 #> 170  9 144.0    0    NA  45.0  NA    2           170 #> 171 10   0.0    1 105.0    NA   1   NA           171 #> 172 10   0.0    0    NA  90.0  NA    2           172 #> 173 10  24.0    0    NA   8.6  NA    1           173 #> 174 10  24.0    0    NA  39.0  NA    2           174 #> 175 10  36.0    0    NA   8.0  NA    1           175 #> 176 10  36.0    0    NA  22.0  NA    2           176 #> 177 10  48.0    0    NA   6.0  NA    1           177 #> 178 10  48.0    0    NA  17.0  NA    2           178 #> 179 10  72.0    0    NA   4.4  NA    1           179 #> 180 10  72.0    0    NA  17.0  NA    2           180 #> 181 10  96.0    0    NA   3.6  NA    1           181 #> 182 10  96.0    0    NA  22.0  NA    2           182 #> 183 10 120.0    0    NA   2.8  NA    1           183 #> 184 10 120.0    0    NA  25.0  NA    2           184 #> 185 10 144.0    0    NA  33.0  NA    2           185 #> 186 11   0.0    1 123.0    NA   1   NA           186 #> 187 11   0.0    0    NA 100.0  NA    2           187 #> 188 11   1.5    0    NA  11.4  NA    1           188 #> 189 11   3.0    0    NA  15.4  NA    1           189 #> 190 11   6.0    0    NA  17.5  NA    1           190 #> 191 11  12.0    0    NA  14.0  NA    1           191 #> 192 11  24.0    0    NA   9.0  NA    1           192 #> 193 11  24.0    0    NA  37.0  NA    2           193 #> 194 11  36.0    0    NA   8.9  NA    1           194 #> 195 11  36.0    0    NA  24.0  NA    2           195 #> 196 11  48.0    0    NA   6.6  NA    1           196 #> 197 11  48.0    0    NA  14.0  NA    2           197 #> 198 11  72.0    0    NA   4.2  NA    1           198 #> 199 11  72.0    0    NA  11.0  NA    2           199 #> 200 11  96.0    0    NA   3.6  NA    1           200 #> 201 11  96.0    0    NA  14.0  NA    2           201 #> 202 11 120.0    0    NA   2.6  NA    1           202 #> 203 11 120.0    0    NA  23.0  NA    2           203 #> 204 11 144.0    0    NA  33.0  NA    2           204 #> 205 12   0.0    1 113.0    NA   1   NA           205 #> 206 12   0.0    0    NA  85.0  NA    2           206 #> 207 12   1.5    0    NA   0.6  NA    1           207 #> 208 12   3.0    0    NA   2.8  NA    1           208 #> 209 12   6.0    0    NA  13.8  NA    1           209 #> 210 12   9.0    0    NA  15.0  NA    1           210 #> 211 12  24.0    0    NA  10.5  NA    1           211 #> 212 12  24.0    0    NA  25.0  NA    2           212 #> 213 12  36.0    0    NA   9.1  NA    1           213 #> 214 12  36.0    0    NA  15.0  NA    2           214 #> 215 12  48.0    0    NA   6.6  NA    1           215 #> 216 12  48.0    0    NA  11.0  NA    2           216 #> 217 12  72.0    0    NA   4.9  NA    1           217 #> 218 12  96.0    0    NA   2.4  NA    1           218 #> 219 12 120.0    0    NA   1.9  NA    1           219 #> 220 13   0.0    1 113.0    NA   1   NA           220 #> 221 13   0.0    0    NA  88.0  NA    2           221 #> 222 13   1.5    0    NA   3.6  NA    1           222 #> 223 13   3.0    0    NA  12.9  NA    1           223 #> 224 13   6.0    0    NA  12.9  NA    1           224 #> 225 13   9.0    0    NA  10.2  NA    1           225 #> 226 13  24.0    0    NA   6.4  NA    1           226 #> 227 13  24.0    0    NA  41.0  NA    2           227 #> 228 13  36.0    0    NA   6.9  NA    1           228 #> 229 13  36.0    0    NA  23.0  NA    2           229 #> 230 13  48.0    0    NA   4.5  NA    1           230 #> 231 13  48.0    0    NA  16.0  NA    2           231 #> 232 13  72.0    0    NA   3.2  NA    1           232 #> 233 13  72.0    0    NA  14.0  NA    2           233 #> 234 13  96.0    0    NA   2.4  NA    1           234 #> 235 13  96.0    0    NA  18.0  NA    2           235 #> 236 13 120.0    0    NA   1.3  NA    1           236 #> 237 13 120.0    0    NA  22.0  NA    2           237 #> 238 13 144.0    0    NA  35.0  NA    2           238 #> 239 14   0.0    1  75.0    NA   1   NA           239 #> 240 14   0.0    0    NA  85.0  NA    2           240 #> 241 14   0.5    0    NA   0.0  NA    1           241 #> 242 14   1.0    0    NA   2.7  NA    1           242 #> 243 14   2.0    0    NA  11.6  NA    1           243 #> 244 14   3.0    0    NA  11.6  NA    1           244 #> 245 14   6.0    0    NA  11.3  NA    1           245 #> 246 14   9.0    0    NA   9.7  NA    1           246 #> 247 14  24.0    0    NA   6.5  NA    1           247 #> 248 14  24.0    0    NA  32.0  NA    2           248 #> 249 14  36.0    0    NA   5.2  NA    1           249 #> 250 14  36.0    0    NA  22.0  NA    2           250 #> 251 14  48.0    0    NA   3.6  NA    1           251 #> 252 14  48.0    0    NA  21.0  NA    2           252 #> 253 14  72.0    0    NA   2.4  NA    1           253 #> 254 14  72.0    0    NA  28.0  NA    2           254 #> 255 14  96.0    0    NA   0.9  NA    1           255 #> 256 14  96.0    0    NA  38.0  NA    2           256 #> 257 14 120.0    0    NA  46.0  NA    2           257 #> 258 14 144.0    0    NA  65.0  NA    2           258 #> 259 15   0.0    1  85.0    NA   1   NA           259 #> 260 15   0.0    0    NA 100.0  NA    2           260 #> 261 15   1.0    0    NA   6.6  NA    1           261 #> 262 15   3.0    0    NA  11.9  NA    1           262 #> 263 15   6.0    0    NA  11.7  NA    1           263 #> 264 15   9.0    0    NA  12.2  NA    1           264 #> 265 15  24.0    0    NA   8.1  NA    1           265 #> 266 15  24.0    0    NA  43.0  NA    2           266 #> 267 15  36.0    0    NA   7.4  NA    1           267 #> 268 15  36.0    0    NA  26.0  NA    2           268 #> 269 15  48.0    0    NA   6.8  NA    1           269 #> 270 15  48.0    0    NA  15.0  NA    2           270 #> 271 15  72.0    0    NA   5.3  NA    1           271 #> 272 15  72.0    0    NA  13.0  NA    2           272 #> 273 15  96.0    0    NA   3.0  NA    1           273 #> 274 15  96.0    0    NA  21.0  NA    2           274 #> 275 15 120.0    0    NA   2.0  NA    1           275 #> 276 15 120.0    0    NA  28.0  NA    2           276 #> 277 15 144.0    0    NA  39.0  NA    2           277 #> 278 16   0.0    1  87.0    NA   1   NA           278 #> 279 16   0.0    0    NA 100.0  NA    2           279 #> 280 16  24.0    0    NA  10.4  NA    1           280 #> 281 16  24.0    0    NA  42.0  NA    2           281 #> 282 16  36.0    0    NA   8.9  NA    1           282 #> 283 16  36.0    0    NA  32.0  NA    2           283 #> 284 16  48.0    0    NA   7.0  NA    1           284 #> 285 16  48.0    0    NA  26.0  NA    2           285 #> 286 16  72.0    0    NA   4.4  NA    1           286 #> 287 16  72.0    0    NA  31.0  NA    2           287 #> 288 16  96.0    0    NA   3.2  NA    1           288 #> 289 16  96.0    0    NA  33.0  NA    2           289 #> 290 16 120.0    0    NA   2.4  NA    1           290 #> 291 16 120.0    0    NA  54.0  NA    2           291 #> 292 17   0.0    1 117.0    NA   1   NA           292 #> 293 17   0.0    0    NA 100.0  NA    2           293 #> 294 17  24.0    0    NA   7.6  NA    1           294 #> 295 17  24.0    0    NA  35.0  NA    2           295 #> 296 17  36.0    0    NA   6.4  NA    1           296 #> 297 17  36.0    0    NA  23.0  NA    2           297 #> 298 17  48.0    0    NA   6.0  NA    1           298 #> 299 17  48.0    0    NA  17.0  NA    2           299 #> 300 17  72.0    0    NA   4.0  NA    1           300 #> 301 17  72.0    0    NA  18.0  NA    2           301 #> 302 17  96.0    0    NA   3.1  NA    1           302 #> 303 17  96.0    0    NA  18.0  NA    2           303 #> 304 17 120.0    0    NA   2.0  NA    1           304 #> 305 17 120.0    0    NA  21.0  NA    2           305 #> 306 18   0.0    1 112.0    NA   1   NA           306 #> 307 18   0.0    0    NA 100.0  NA    2           307 #> 308 18  24.0    0    NA   7.6  NA    1           308 #> 309 18  24.0    0    NA  32.0  NA    2           309 #> 310 18  36.0    0    NA   6.6  NA    1           310 #> 311 18  36.0    0    NA  20.0  NA    2           311 #> 312 18  48.0    0    NA   5.4  NA    1           312 #> 313 18  48.0    0    NA  18.0  NA    2           313 #> 314 18  72.0    0    NA   3.4  NA    1           314 #> 315 18  72.0    0    NA  18.0  NA    2           315 #> 316 18  96.0    0    NA   1.2  NA    1           316 #> 317 18  96.0    0    NA  19.0  NA    2           317 #> 318 18 120.0    0    NA   0.9  NA    1           318 #> 319 18 120.0    0    NA  29.0  NA    2           319 #> 320 19   0.0    1  95.5    NA   1   NA           320 #> 321 19   0.0    0    NA 100.0  NA    2           321 #> 322 19  24.0    0    NA   6.6  NA    1           322 #> 323 19  24.0    0    NA  33.0  NA    2           323 #> 324 19  36.0    0    NA   5.3  NA    1           324 #> 325 19  36.0    0    NA  28.0  NA    2           325 #> 326 19  48.0    0    NA   3.6  NA    1           326 #> 327 19  48.0    0    NA  18.0  NA    2           327 #> 328 19  72.0    0    NA   2.7  NA    1           328 #> 329 19  72.0    0    NA  18.0  NA    2           329 #> 330 19  96.0    0    NA   1.4  NA    1           330 #> 331 19  96.0    0    NA  17.0  NA    2           331 #> 332 19 120.0    0    NA   1.1  NA    1           332 #> 333 19 120.0    0    NA  26.0  NA    2           333 #> 334 20   0.0    1  88.5    NA   1   NA           334 #> 335 20   0.0    0    NA 100.0  NA    2           335 #> 336 20  24.0    0    NA   9.6  NA    1           336 #> 337 20  24.0    0    NA  41.0  NA    2           337 #> 338 20  36.0    0    NA   8.0  NA    1           338 #> 339 20  36.0    0    NA  30.0  NA    2           339 #> 340 20  48.0    0    NA   6.6  NA    1           340 #> 341 20  48.0    0    NA  22.0  NA    2           341 #> 342 20  72.0    0    NA   5.6  NA    1           342 #> 343 20  72.0    0    NA  23.0  NA    2           343 #> 344 20  96.0    0    NA   3.5  NA    1           344 #> 345 20  96.0    0    NA  23.0  NA    2           345 #> 346 20 120.0    0    NA   2.3  NA    1           346 #> 347 20 120.0    0    NA  35.0  NA    2           347 #> 348 21   0.0    1  93.0    NA   1   NA           348 #> 349 21   0.0    0    NA 100.0  NA    2           349 #> 350 21  24.0    0    NA   7.3  NA    1           350 #> 351 21  24.0    0    NA  46.0  NA    2           351 #> 352 21  36.0    0    NA   6.1  NA    1           352 #> 353 21  36.0    0    NA  27.0  NA    2           353 #> 354 21  48.0    0    NA   4.3  NA    1           354 #> 355 21  48.0    0    NA  22.0  NA    2           355 #> 356 21  72.0    0    NA   3.2  NA    1           356 #> 357 21  72.0    0    NA  36.0  NA    2           357 #> 358 21  96.0    0    NA   2.3  NA    1           358 #> 359 21  96.0    0    NA  40.0  NA    2           359 #> 360 21 120.0    0    NA   1.9  NA    1           360 #> 361 21 120.0    0    NA  44.0  NA    2           361 #> 362 22   0.0    1  87.0    NA   1   NA           362 #> 363 22   0.0    0    NA 100.0  NA    2           363 #> 364 22  24.0    0    NA   8.9  NA    1           364 #> 365 22  24.0    0    NA  35.0  NA    2           365 #> 366 22  36.0    0    NA   8.4  NA    1           366 #> 367 22  36.0    0    NA  27.0  NA    2           367 #> 368 22  48.0    0    NA   8.0  NA    1           368 #> 369 22  48.0    0    NA  23.0  NA    2           369 #> 370 22  72.0    0    NA   4.4  NA    1           370 #> 371 22  72.0    0    NA  27.0  NA    2           371 #> 372 22  96.0    0    NA   3.2  NA    1           372 #> 373 22  96.0    0    NA  43.0  NA    2           373 #> 374 22 120.0    0    NA   1.7  NA    1           374 #> 375 22 120.0    0    NA  43.0  NA    2           375 #> 376 23   0.0    1 110.0    NA   1   NA           376 #> 377 23   0.0    0    NA 100.0  NA    2           377 #> 378 23  24.0    0    NA   9.8  NA    1           378 #> 379 23  24.0    0    NA  34.0  NA    2           379 #> 380 23  36.0    0    NA   8.4  NA    1           380 #> 381 23  36.0    0    NA  24.0  NA    2           381 #> 382 23  48.0    0    NA   6.6  NA    1           382 #> 383 23  48.0    0    NA  15.0  NA    2           383 #> 384 23  72.0    0    NA   4.8  NA    1           384 #> 385 23  72.0    0    NA  15.0  NA    2           385 #> 386 23  96.0    0    NA   3.2  NA    1           386 #> 387 23  96.0    0    NA  19.0  NA    2           387 #> 388 23 120.0    0    NA   2.4  NA    1           388 #> 389 23 120.0    0    NA  19.0  NA    2           389 #> 390 24   0.0    1 115.0    NA   1   NA           390 #> 391 24   0.0    0    NA  88.0  NA    2           391 #> 392 24  24.0    0    NA   8.2  NA    1           392 #> 393 24  24.0    0    NA  37.0  NA    2           393 #> 394 24  36.0    0    NA   7.5  NA    1           394 #> 395 24  36.0    0    NA  20.0  NA    2           395 #> 396 24  48.0    0    NA   6.8  NA    1           396 #> 397 24  48.0    0    NA  20.0  NA    2           397 #> 398 24  72.0    0    NA   5.5  NA    1           398 #> 399 24  72.0    0    NA  26.0  NA    2           399 #> 400 24  96.0    0    NA   4.5  NA    1           400 #> 401 24  96.0    0    NA  28.0  NA    2           401 #> 402 24 120.0    0    NA   3.7  NA    1           402 #> 403 24 120.0    0    NA  50.0  NA    2           403 #> 404 25   0.0    1 112.0    NA   1   NA           404 #> 405 25   0.0    0    NA 100.0  NA    2           405 #> 406 25  24.0    0    NA  11.0  NA    1           406 #> 407 25  24.0    0    NA  32.0  NA    2           407 #> 408 25  36.0    0    NA  10.0  NA    1           408 #> 409 25  36.0    0    NA  20.0  NA    2           409 #> 410 25  48.0    0    NA   8.2  NA    1           410 #> 411 25  48.0    0    NA  17.0  NA    2           411 #> 412 25  72.0    0    NA   6.0  NA    1           412 #> 413 25  72.0    0    NA  19.0  NA    2           413 #> 414 25  96.0    0    NA   3.7  NA    1           414 #> 415 25  96.0    0    NA  21.0  NA    2           415 #> 416 25 120.0    0    NA   2.6  NA    1           416 #> 417 25 120.0    0    NA  30.0  NA    2           417 #> 418 26   0.0    1 120.0    NA   1   NA           418 #> 419 26   0.0    0    NA 100.0  NA    2           419 #> 420 26  24.0    0    NA  10.0  NA    1           420 #> 421 26  24.0    0    NA  41.0  NA    2           421 #> 422 26  36.0    0    NA   9.0  NA    1           422 #> 423 26  36.0    0    NA  28.0  NA    2           423 #> 424 26  48.0    0    NA   7.3  NA    1           424 #> 425 26  48.0    0    NA  19.0  NA    2           425 #> 426 26  72.0    0    NA   5.2  NA    1           426 #> 427 26  72.0    0    NA  17.0  NA    2           427 #> 428 26  96.0    0    NA   3.7  NA    1           428 #> 429 26  96.0    0    NA  17.0  NA    2           429 #> 430 26 120.0    0    NA   2.7  NA    1           430 #> 431 26 120.0    0    NA  24.0  NA    2           431 #> 432 27   0.0    1 120.0    NA   1   NA           432 #> 433 27   0.0    0    NA 100.0  NA    2           433 #> 434 27  24.0    0    NA  11.8  NA    1           434 #> 435 27  24.0    0    NA  32.0  NA    2           435 #> 436 27  36.0    0    NA   9.2  NA    1           436 #> 437 27  36.0    0    NA  21.0  NA    2           437 #> 438 27  48.0    0    NA   7.7  NA    1           438 #> 439 27  48.0    0    NA  19.0  NA    2           439 #> 440 27  72.0    0    NA   4.9  NA    1           440 #> 441 27  72.0    0    NA  22.0  NA    2           441 #> 442 27  96.0    0    NA   3.4  NA    1           442 #> 443 27  96.0    0    NA  33.0  NA    2           443 #> 444 27 120.0    0    NA   2.7  NA    1           444 #> 445 27 120.0    0    NA  46.0  NA    2           445 #> 446 28   0.0    1 120.0    NA   1   NA           446 #> 447 28   0.0    0    NA 100.0  NA    2           447 #> 448 28  24.0    0    NA  10.1  NA    1           448 #> 449 28  24.0    0    NA  39.0  NA    2           449 #> 450 28  36.0    0    NA   8.0  NA    1           450 #> 451 28  36.0    0    NA  25.0  NA    2           451 #> 452 28  48.0    0    NA   6.0  NA    1           452 #> 453 28  48.0    0    NA  16.0  NA    2           453 #> 454 28  72.0    0    NA   4.9  NA    1           454 #> 455 28  72.0    0    NA  14.0  NA    2           455 #> 456 28  96.0    0    NA   3.4  NA    1           456 #> 457 28  96.0    0    NA  15.0  NA    2           457 #> 458 28 120.0    0    NA   2.0  NA    1           458 #> 459 28 120.0    0    NA  20.0  NA    2           459 #> 460 29   0.0    1 153.0    NA   1   NA           460 #> 461 29   0.0    0    NA  86.0  NA    2           461 #> 462 29  24.0    0    NA   8.3  NA    1           462 #> 463 29  24.0    0    NA  35.0  NA    2           463 #> 464 29  36.0    0    NA   7.0  NA    1           464 #> 465 29  36.0    0    NA  21.0  NA    2           465 #> 466 29  48.0    0    NA   5.6  NA    1           466 #> 467 29  48.0    0    NA  18.0  NA    2           467 #> 468 29  72.0    0    NA   4.1  NA    1           468 #> 469 29  72.0    0    NA  20.0  NA    2           469 #> 470 29  96.0    0    NA   3.1  NA    1           470 #> 471 29  96.0    0    NA  29.0  NA    2           471 #> 472 29 120.0    0    NA   2.2  NA    1           472 #> 473 29 120.0    0    NA  41.0  NA    2           473 #> 474 30   0.0    1 105.0    NA   1   NA           474 #> 475 30   0.0    0    NA 100.0  NA    2           475 #> 476 30  24.0    0    NA   9.9  NA    1           476 #> 477 30  24.0    0    NA  45.0  NA    2           477 #> 478 30  36.0    0    NA   7.5  NA    1           478 #> 479 30  36.0    0    NA  24.0  NA    2           479 #> 480 30  48.0    0    NA   6.5  NA    1           480 #> 481 30  48.0    0    NA  23.0  NA    2           481 #> 482 30  72.0    0    NA   4.1  NA    1           482 #> 483 30  72.0    0    NA  26.0  NA    2           483 #> 484 30  96.0    0    NA   2.9  NA    1           484 #> 485 30  96.0    0    NA  28.0  NA    2           485 #> 486 30 120.0    0    NA   2.3  NA    1           486 #> 487 30 120.0    0    NA  39.0  NA    2           487 #> 488 31   0.0    1 125.0    NA   1   NA           488 #> 489 31   0.0    0    NA 100.0  NA    2           489 #> 490 31  24.0    0    NA   9.5  NA    1           490 #> 491 31  24.0    0    NA  45.0  NA    2           491 #> 492 31  36.0    0    NA   7.8  NA    1           492 #> 493 31  36.0    0    NA  30.0  NA    2           493 #> 494 31  48.0    0    NA   6.4  NA    1           494 #> 495 31  48.0    0    NA  24.0  NA    2           495 #> 496 31  72.0    0    NA   4.5  NA    1           496 #> 497 31  72.0    0    NA  22.0  NA    2           497 #> 498 31  96.0    0    NA   3.4  NA    1           498 #> 499 31  96.0    0    NA  28.0  NA    2           499 #> 500 31 120.0    0    NA   2.5  NA    1           500 #> 501 31 120.0    0    NA  42.0  NA    2           501 #> 502 32   0.0    1  93.0    NA   1   NA           502 #> 503 32   0.0    0    NA 100.0  NA    2           503 #> 504 32  24.0    0    NA   8.9  NA    1           504 #> 505 32  24.0    0    NA  36.0  NA    2           505 #> 506 32  36.0    0    NA   7.7  NA    1           506 #> 507 32  36.0    0    NA  27.0  NA    2           507 #> 508 32  48.0    0    NA   6.9  NA    1           508 #> 509 32  48.0    0    NA  24.0  NA    2           509 #> 510 32  72.0    0    NA   4.4  NA    1           510 #> 511 32  72.0    0    NA  23.0  NA    2           511 #> 512 32  96.0    0    NA   3.5  NA    1           512 #> 513 32  96.0    0    NA  20.0  NA    2           513 #> 514 32 120.0    0    NA   2.5  NA    1           514 #> 515 32 120.0    0    NA  22.0  NA    2           515  bblDatToMrgsolve(pk.turnover.emax3, nlmixr2data::warfarin) #>   #>   #>     ID  TIME EVID   AMT II    DV CMT DVID SS nlmixrRowNums #> 1    1   0.0    1 100.0  0    NA   1    0  0             1 #> 2    1   0.5    0    NA  0   0.0   5    1  0             2 #> 3    1   1.0    0    NA  0   1.9   5    1  0             3 #> 4    1   2.0    0    NA  0   3.3   5    1  0             4 #> 5    1   3.0    0    NA  0   6.6   5    1  0             5 #> 6    1   6.0    0    NA  0   9.1   5    1  0             6 #> 7    1   9.0    0    NA  0  10.8   5    1  0             7 #> 8    1  12.0    0    NA  0   8.6   5    1  0             8 #> 9    1  24.0    0    NA  0   5.6   5    1  0             9 #> 10   1  24.0    0    NA  0  44.0   6    2  0            10 #> 11   1  36.0    0    NA  0   4.0   5    1  0            11 #> 12   1  36.0    0    NA  0  27.0   6    2  0            12 #> 13   1  48.0    0    NA  0   2.7   5    1  0            13 #> 14   1  48.0    0    NA  0  28.0   6    2  0            14 #> 15   1  72.0    0    NA  0   0.8   5    1  0            15 #> 16   1  72.0    0    NA  0  31.0   6    2  0            16 #> 17   1  96.0    0    NA  0  60.0   6    2  0            17 #> 18   1 120.0    0    NA  0  65.0   6    2  0            18 #> 19   1 144.0    0    NA  0  71.0   6    2  0            19 #> 20   2   0.0    1 100.0  0    NA   1    0  0            20 #> 21   2   0.0    0    NA  0 100.0   6    2  0            21 #> 22   2  24.0    0    NA  0   9.2   5    1  0            22 #> 23   2  24.0    0    NA  0  49.0   6    2  0            23 #> 24   2  36.0    0    NA  0   8.5   5    1  0            24 #> 25   2  36.0    0    NA  0  32.0   6    2  0            25 #> 26   2  48.0    0    NA  0   6.4   5    1  0            26 #> 27   2  48.0    0    NA  0  26.0   6    2  0            27 #> 28   2  72.0    0    NA  0   4.8   5    1  0            28 #> 29   2  72.0    0    NA  0  22.0   6    2  0            29 #> 30   2  96.0    0    NA  0   3.1   5    1  0            30 #> 31   2  96.0    0    NA  0  28.0   6    2  0            31 #> 32   2 120.0    0    NA  0   2.5   5    1  0            32 #> 33   2 120.0    0    NA  0  33.0   6    2  0            33 #> 34   3   0.0    1 100.0  0    NA   1    0  0            34 #> 35   3   0.0    0    NA  0 100.0   6    2  0            35 #> 36   3   0.5    0    NA  0   0.0   5    1  0            36 #> 37   3   2.0    0    NA  0   8.4   5    1  0            37 #> 38   3   3.0    0    NA  0   9.7   5    1  0            38 #> 39   3   6.0    0    NA  0   9.8   5    1  0            39 #> 40   3  12.0    0    NA  0  11.0   5    1  0            40 #> 41   3  24.0    0    NA  0   8.3   5    1  0            41 #> 42   3  24.0    0    NA  0  46.0   6    2  0            42 #> 43   3  36.0    0    NA  0   7.7   5    1  0            43 #> 44   3  36.0    0    NA  0  22.0   6    2  0            44 #> 45   3  48.0    0    NA  0   6.3   5    1  0            45 #> 46   3  48.0    0    NA  0  19.0   6    2  0            46 #> 47   3  72.0    0    NA  0   4.1   5    1  0            47 #> 48   3  72.0    0    NA  0  20.0   6    2  0            48 #> 49   3  96.0    0    NA  0   3.0   5    1  0            49 #> 50   3  96.0    0    NA  0  42.0   6    2  0            50 #> 51   3 120.0    0    NA  0   1.4   5    1  0            51 #> 52   3 120.0    0    NA  0  49.0   6    2  0            52 #> 53   3 144.0    0    NA  0  54.0   6    2  0            53 #> 54   4   0.0    1 120.0  0    NA   1    0  0            54 #> 55   4   0.0    0    NA  0 100.0   6    2  0            55 #> 56   4   3.0    0    NA  0  12.0   5    1  0            56 #> 57   4   6.0    0    NA  0  13.2   5    1  0            57 #> 58   4   9.0    0    NA  0  14.4   5    1  0            58 #> 59   4  24.0    0    NA  0   9.6   5    1  0            59 #> 60   4  24.0    0    NA  0  30.0   6    2  0            60 #> 61   4  36.0    0    NA  0   8.2   5    1  0            61 #> 62   4  36.0    0    NA  0  24.0   6    2  0            62 #> 63   4  48.0    0    NA  0   7.8   5    1  0            63 #> 64   4  48.0    0    NA  0  13.0   6    2  0            64 #> 65   4  72.0    0    NA  0   5.8   5    1  0            65 #> 66   4  72.0    0    NA  0   9.0   6    2  0            66 #> 67   4  96.0    0    NA  0   4.3   5    1  0            67 #> 68   4  96.0    0    NA  0   9.0   6    2  0            68 #> 69   4 120.0    0    NA  0   3.0   5    1  0            69 #> 70   4 120.0    0    NA  0  11.0   6    2  0            70 #> 71   4 144.0    0    NA  0  12.0   6    2  0            71 #> 72   5   0.0    1  60.0  0    NA   1    0  0            72 #> 73   5   0.0    0    NA  0  82.0   6    2  0            73 #> 74   5   3.0    0    NA  0  11.1   5    1  0            74 #> 75   5   6.0    0    NA  0  11.9   5    1  0            75 #> 76   5   9.0    0    NA  0   9.8   5    1  0            76 #> 77   5  12.0    0    NA  0  11.0   5    1  0            77 #> 78   5  24.0    0    NA  0   8.5   5    1  0            78 #> 79   5  24.0    0    NA  0  43.0   6    2  0            79 #> 80   5  36.0    0    NA  0   7.6   5    1  0            80 #> 81   5  36.0    0    NA  0  25.0   6    2  0            81 #> 82   5  48.0    0    NA  0   5.4   5    1  0            82 #> 83   5  48.0    0    NA  0  18.0   6    2  0            83 #> 84   5  72.0    0    NA  0   4.5   5    1  0            84 #> 85   5  72.0    0    NA  0  17.0   6    2  0            85 #> 86   5  96.0    0    NA  0   3.3   5    1  0            86 #> 87   5  96.0    0    NA  0  23.0   6    2  0            87 #> 88   5 120.0    0    NA  0   2.3   5    1  0            88 #> 89   5 120.0    0    NA  0  29.0   6    2  0            89 #> 90   5 144.0    0    NA  0  41.0   6    2  0            90 #> 91   6   0.0    1 113.0  0    NA   1    0  0            91 #> 92   6   0.0    0    NA  0 100.0   6    2  0            92 #> 93   6   6.0    0    NA  0   8.6   5    1  0            93 #> 94   6  12.0    0    NA  0   8.6   5    1  0            94 #> 95   6  24.0    0    NA  0   7.0   5    1  0            95 #> 96   6  24.0    0    NA  0  34.0   6    2  0            96 #> 97   6  36.0    0    NA  0   5.7   5    1  0            97 #> 98   6  36.0    0    NA  0  23.0   6    2  0            98 #> 99   6  48.0    0    NA  0   4.7   5    1  0            99 #> 100  6  48.0    0    NA  0  20.0   6    2  0           100 #> 101  6  72.0    0    NA  0   3.3   5    1  0           101 #> 102  6  72.0    0    NA  0  16.0   6    2  0           102 #> 103  6  96.0    0    NA  0   2.3   5    1  0           103 #> 104  6  96.0    0    NA  0  17.0   6    2  0           104 #> 105  6 120.0    0    NA  0   1.7   5    1  0           105 #> 106  6 120.0    0    NA  0  18.0   6    2  0           106 #> 107  6 144.0    0    NA  0  25.0   6    2  0           107 #> 108  7   0.0    1  90.0  0    NA   1    0  0           108 #> 109  7   3.0    0    NA  0  13.4   5    1  0           109 #> 110  7   6.0    0    NA  0  12.4   5    1  0           110 #> 111  7   9.0    0    NA  0  12.7   5    1  0           111 #> 112  7  12.0    0    NA  0   8.8   5    1  0           112 #> 113  7  24.0    0    NA  0   6.1   5    1  0           113 #> 114  7  24.0    0    NA  0  36.0   6    2  0           114 #> 115  7  36.0    0    NA  0   3.5   5    1  0           115 #> 116  7  36.0    0    NA  0  33.0   6    2  0           116 #> 117  7  48.0    0    NA  0   1.8   5    1  0           117 #> 118  7  48.0    0    NA  0  28.0   6    2  0           118 #> 119  7  72.0    0    NA  0   1.5   5    1  0           119 #> 120  7  72.0    0    NA  0  52.0   6    2  0           120 #> 121  7  96.0    0    NA  0   1.0   5    1  0           121 #> 122  7  96.0    0    NA  0  80.0   6    2  0           122 #> 123  7 120.0    0    NA  0  90.0   6    2  0           123 #> 124  7 144.0    0    NA  0 100.0   6    2  0           124 #> 125  8   0.0    1 135.0  0    NA   1    0  0           125 #> 126  8   0.0    0    NA  0  88.0   6    2  0           126 #> 127  8   2.0    0    NA  0  17.6   5    1  0           127 #> 128  8   3.0    0    NA  0  17.3   5    1  0           128 #> 129  8   6.0    0    NA  0  15.0   5    1  0           129 #> 130  8   9.0    0    NA  0  15.0   5    1  0           130 #> 131  8  12.0    0    NA  0  12.4   5    1  0           131 #> 132  8  24.0    0    NA  0   7.9   5    1  0           132 #> 133  8  24.0    0    NA  0  35.0   6    2  0           133 #> 134  8  36.0    0    NA  0   7.9   5    1  0           134 #> 135  8  36.0    0    NA  0  20.0   6    2  0           135 #> 136  8  48.0    0    NA  0   5.1   5    1  0           136 #> 137  8  48.0    0    NA  0  12.0   6    2  0           137 #> 138  8  72.0    0    NA  0   3.6   5    1  0           138 #> 139  8  72.0    0    NA  0  16.0   6    2  0           139 #> 140  8  96.0    0    NA  0   2.4   5    1  0           140 #> 141  8  96.0    0    NA  0  23.0   6    2  0           141 #> 142  8 120.0    0    NA  0   2.0   5    1  0           142 #> 143  8 120.0    0    NA  0  36.0   6    2  0           143 #> 144  8 144.0    0    NA  0  48.0   6    2  0           144 #> 145  9   0.0    1  75.0  0    NA   1    0  0           145 #> 146  9   0.0    0    NA  0  92.0   6    2  0           146 #> 147  9   0.5    0    NA  0   0.0   5    1  0           147 #> 148  9   1.0    0    NA  0   1.0   5    1  0           148 #> 149  9   2.0    0    NA  0   4.6   5    1  0           149 #> 150  9   3.0    0    NA  0  12.7   5    1  0           150 #> 151  9   3.0    0    NA  0   8.0   5    1  0           151 #> 152  9   6.0    0    NA  0  12.7   5    1  0           152 #> 153  9   6.0    0    NA  0  11.5   5    1  0           153 #> 154  9   9.0    0    NA  0  12.9   5    1  0           154 #> 155  9   9.0    0    NA  0  11.4   5    1  0           155 #> 156  9  12.0    0    NA  0  11.4   5    1  0           156 #> 157  9  12.0    0    NA  0  11.0   5    1  0           157 #> 158  9  24.0    0    NA  0   9.1   5    1  0           158 #> 159  9  24.0    0    NA  0  33.0   6    2  0           159 #> 160  9  36.0    0    NA  0   8.2   5    1  0           160 #> 161  9  36.0    0    NA  0  22.0   6    2  0           161 #> 162  9  48.0    0    NA  0   5.9   5    1  0           162 #> 163  9  48.0    0    NA  0  16.0   6    2  0           163 #> 164  9  72.0    0    NA  0   3.6   5    1  0           164 #> 165  9  72.0    0    NA  0  18.0   6    2  0           165 #> 166  9  96.0    0    NA  0   1.7   5    1  0           166 #> 167  9  96.0    0    NA  0  32.0   6    2  0           167 #> 168  9 120.0    0    NA  0   1.1   5    1  0           168 #> 169  9 120.0    0    NA  0  30.0   6    2  0           169 #> 170  9 144.0    0    NA  0  45.0   6    2  0           170 #> 171 10   0.0    1 105.0  0    NA   1    0  0           171 #> 172 10   0.0    0    NA  0  90.0   6    2  0           172 #> 173 10  24.0    0    NA  0   8.6   5    1  0           173 #> 174 10  24.0    0    NA  0  39.0   6    2  0           174 #> 175 10  36.0    0    NA  0   8.0   5    1  0           175 #> 176 10  36.0    0    NA  0  22.0   6    2  0           176 #> 177 10  48.0    0    NA  0   6.0   5    1  0           177 #> 178 10  48.0    0    NA  0  17.0   6    2  0           178 #> 179 10  72.0    0    NA  0   4.4   5    1  0           179 #> 180 10  72.0    0    NA  0  17.0   6    2  0           180 #> 181 10  96.0    0    NA  0   3.6   5    1  0           181 #> 182 10  96.0    0    NA  0  22.0   6    2  0           182 #> 183 10 120.0    0    NA  0   2.8   5    1  0           183 #> 184 10 120.0    0    NA  0  25.0   6    2  0           184 #> 185 10 144.0    0    NA  0  33.0   6    2  0           185 #> 186 11   0.0    1 123.0  0    NA   1    0  0           186 #> 187 11   0.0    0    NA  0 100.0   6    2  0           187 #> 188 11   1.5    0    NA  0  11.4   5    1  0           188 #> 189 11   3.0    0    NA  0  15.4   5    1  0           189 #> 190 11   6.0    0    NA  0  17.5   5    1  0           190 #> 191 11  12.0    0    NA  0  14.0   5    1  0           191 #> 192 11  24.0    0    NA  0   9.0   5    1  0           192 #> 193 11  24.0    0    NA  0  37.0   6    2  0           193 #> 194 11  36.0    0    NA  0   8.9   5    1  0           194 #> 195 11  36.0    0    NA  0  24.0   6    2  0           195 #> 196 11  48.0    0    NA  0   6.6   5    1  0           196 #> 197 11  48.0    0    NA  0  14.0   6    2  0           197 #> 198 11  72.0    0    NA  0   4.2   5    1  0           198 #> 199 11  72.0    0    NA  0  11.0   6    2  0           199 #> 200 11  96.0    0    NA  0   3.6   5    1  0           200 #> 201 11  96.0    0    NA  0  14.0   6    2  0           201 #> 202 11 120.0    0    NA  0   2.6   5    1  0           202 #> 203 11 120.0    0    NA  0  23.0   6    2  0           203 #> 204 11 144.0    0    NA  0  33.0   6    2  0           204 #> 205 12   0.0    1 113.0  0    NA   1    0  0           205 #> 206 12   0.0    0    NA  0  85.0   6    2  0           206 #> 207 12   1.5    0    NA  0   0.6   5    1  0           207 #> 208 12   3.0    0    NA  0   2.8   5    1  0           208 #> 209 12   6.0    0    NA  0  13.8   5    1  0           209 #> 210 12   9.0    0    NA  0  15.0   5    1  0           210 #> 211 12  24.0    0    NA  0  10.5   5    1  0           211 #> 212 12  24.0    0    NA  0  25.0   6    2  0           212 #> 213 12  36.0    0    NA  0   9.1   5    1  0           213 #> 214 12  36.0    0    NA  0  15.0   6    2  0           214 #> 215 12  48.0    0    NA  0   6.6   5    1  0           215 #> 216 12  48.0    0    NA  0  11.0   6    2  0           216 #> 217 12  72.0    0    NA  0   4.9   5    1  0           217 #> 218 12  96.0    0    NA  0   2.4   5    1  0           218 #> 219 12 120.0    0    NA  0   1.9   5    1  0           219 #> 220 13   0.0    1 113.0  0    NA   1    0  0           220 #> 221 13   0.0    0    NA  0  88.0   6    2  0           221 #> 222 13   1.5    0    NA  0   3.6   5    1  0           222 #> 223 13   3.0    0    NA  0  12.9   5    1  0           223 #> 224 13   6.0    0    NA  0  12.9   5    1  0           224 #> 225 13   9.0    0    NA  0  10.2   5    1  0           225 #> 226 13  24.0    0    NA  0   6.4   5    1  0           226 #> 227 13  24.0    0    NA  0  41.0   6    2  0           227 #> 228 13  36.0    0    NA  0   6.9   5    1  0           228 #> 229 13  36.0    0    NA  0  23.0   6    2  0           229 #> 230 13  48.0    0    NA  0   4.5   5    1  0           230 #> 231 13  48.0    0    NA  0  16.0   6    2  0           231 #> 232 13  72.0    0    NA  0   3.2   5    1  0           232 #> 233 13  72.0    0    NA  0  14.0   6    2  0           233 #> 234 13  96.0    0    NA  0   2.4   5    1  0           234 #> 235 13  96.0    0    NA  0  18.0   6    2  0           235 #> 236 13 120.0    0    NA  0   1.3   5    1  0           236 #> 237 13 120.0    0    NA  0  22.0   6    2  0           237 #> 238 13 144.0    0    NA  0  35.0   6    2  0           238 #> 239 14   0.0    1  75.0  0    NA   1    0  0           239 #> 240 14   0.0    0    NA  0  85.0   6    2  0           240 #> 241 14   0.5    0    NA  0   0.0   5    1  0           241 #> 242 14   1.0    0    NA  0   2.7   5    1  0           242 #> 243 14   2.0    0    NA  0  11.6   5    1  0           243 #> 244 14   3.0    0    NA  0  11.6   5    1  0           244 #> 245 14   6.0    0    NA  0  11.3   5    1  0           245 #> 246 14   9.0    0    NA  0   9.7   5    1  0           246 #> 247 14  24.0    0    NA  0   6.5   5    1  0           247 #> 248 14  24.0    0    NA  0  32.0   6    2  0           248 #> 249 14  36.0    0    NA  0   5.2   5    1  0           249 #> 250 14  36.0    0    NA  0  22.0   6    2  0           250 #> 251 14  48.0    0    NA  0   3.6   5    1  0           251 #> 252 14  48.0    0    NA  0  21.0   6    2  0           252 #> 253 14  72.0    0    NA  0   2.4   5    1  0           253 #> 254 14  72.0    0    NA  0  28.0   6    2  0           254 #> 255 14  96.0    0    NA  0   0.9   5    1  0           255 #> 256 14  96.0    0    NA  0  38.0   6    2  0           256 #> 257 14 120.0    0    NA  0  46.0   6    2  0           257 #> 258 14 144.0    0    NA  0  65.0   6    2  0           258 #> 259 15   0.0    1  85.0  0    NA   1    0  0           259 #> 260 15   0.0    0    NA  0 100.0   6    2  0           260 #> 261 15   1.0    0    NA  0   6.6   5    1  0           261 #> 262 15   3.0    0    NA  0  11.9   5    1  0           262 #> 263 15   6.0    0    NA  0  11.7   5    1  0           263 #> 264 15   9.0    0    NA  0  12.2   5    1  0           264 #> 265 15  24.0    0    NA  0   8.1   5    1  0           265 #> 266 15  24.0    0    NA  0  43.0   6    2  0           266 #> 267 15  36.0    0    NA  0   7.4   5    1  0           267 #> 268 15  36.0    0    NA  0  26.0   6    2  0           268 #> 269 15  48.0    0    NA  0   6.8   5    1  0           269 #> 270 15  48.0    0    NA  0  15.0   6    2  0           270 #> 271 15  72.0    0    NA  0   5.3   5    1  0           271 #> 272 15  72.0    0    NA  0  13.0   6    2  0           272 #> 273 15  96.0    0    NA  0   3.0   5    1  0           273 #> 274 15  96.0    0    NA  0  21.0   6    2  0           274 #> 275 15 120.0    0    NA  0   2.0   5    1  0           275 #> 276 15 120.0    0    NA  0  28.0   6    2  0           276 #> 277 15 144.0    0    NA  0  39.0   6    2  0           277 #> 278 16   0.0    1  87.0  0    NA   1    0  0           278 #> 279 16   0.0    0    NA  0 100.0   6    2  0           279 #> 280 16  24.0    0    NA  0  10.4   5    1  0           280 #> 281 16  24.0    0    NA  0  42.0   6    2  0           281 #> 282 16  36.0    0    NA  0   8.9   5    1  0           282 #> 283 16  36.0    0    NA  0  32.0   6    2  0           283 #> 284 16  48.0    0    NA  0   7.0   5    1  0           284 #> 285 16  48.0    0    NA  0  26.0   6    2  0           285 #> 286 16  72.0    0    NA  0   4.4   5    1  0           286 #> 287 16  72.0    0    NA  0  31.0   6    2  0           287 #> 288 16  96.0    0    NA  0   3.2   5    1  0           288 #> 289 16  96.0    0    NA  0  33.0   6    2  0           289 #> 290 16 120.0    0    NA  0   2.4   5    1  0           290 #> 291 16 120.0    0    NA  0  54.0   6    2  0           291 #> 292 17   0.0    1 117.0  0    NA   1    0  0           292 #> 293 17   0.0    0    NA  0 100.0   6    2  0           293 #> 294 17  24.0    0    NA  0   7.6   5    1  0           294 #> 295 17  24.0    0    NA  0  35.0   6    2  0           295 #> 296 17  36.0    0    NA  0   6.4   5    1  0           296 #> 297 17  36.0    0    NA  0  23.0   6    2  0           297 #> 298 17  48.0    0    NA  0   6.0   5    1  0           298 #> 299 17  48.0    0    NA  0  17.0   6    2  0           299 #> 300 17  72.0    0    NA  0   4.0   5    1  0           300 #> 301 17  72.0    0    NA  0  18.0   6    2  0           301 #> 302 17  96.0    0    NA  0   3.1   5    1  0           302 #> 303 17  96.0    0    NA  0  18.0   6    2  0           303 #> 304 17 120.0    0    NA  0   2.0   5    1  0           304 #> 305 17 120.0    0    NA  0  21.0   6    2  0           305 #> 306 18   0.0    1 112.0  0    NA   1    0  0           306 #> 307 18   0.0    0    NA  0 100.0   6    2  0           307 #> 308 18  24.0    0    NA  0   7.6   5    1  0           308 #> 309 18  24.0    0    NA  0  32.0   6    2  0           309 #> 310 18  36.0    0    NA  0   6.6   5    1  0           310 #> 311 18  36.0    0    NA  0  20.0   6    2  0           311 #> 312 18  48.0    0    NA  0   5.4   5    1  0           312 #> 313 18  48.0    0    NA  0  18.0   6    2  0           313 #> 314 18  72.0    0    NA  0   3.4   5    1  0           314 #> 315 18  72.0    0    NA  0  18.0   6    2  0           315 #> 316 18  96.0    0    NA  0   1.2   5    1  0           316 #> 317 18  96.0    0    NA  0  19.0   6    2  0           317 #> 318 18 120.0    0    NA  0   0.9   5    1  0           318 #> 319 18 120.0    0    NA  0  29.0   6    2  0           319 #> 320 19   0.0    1  95.5  0    NA   1    0  0           320 #> 321 19   0.0    0    NA  0 100.0   6    2  0           321 #> 322 19  24.0    0    NA  0   6.6   5    1  0           322 #> 323 19  24.0    0    NA  0  33.0   6    2  0           323 #> 324 19  36.0    0    NA  0   5.3   5    1  0           324 #> 325 19  36.0    0    NA  0  28.0   6    2  0           325 #> 326 19  48.0    0    NA  0   3.6   5    1  0           326 #> 327 19  48.0    0    NA  0  18.0   6    2  0           327 #> 328 19  72.0    0    NA  0   2.7   5    1  0           328 #> 329 19  72.0    0    NA  0  18.0   6    2  0           329 #> 330 19  96.0    0    NA  0   1.4   5    1  0           330 #> 331 19  96.0    0    NA  0  17.0   6    2  0           331 #> 332 19 120.0    0    NA  0   1.1   5    1  0           332 #> 333 19 120.0    0    NA  0  26.0   6    2  0           333 #> 334 20   0.0    1  88.5  0    NA   1    0  0           334 #> 335 20   0.0    0    NA  0 100.0   6    2  0           335 #> 336 20  24.0    0    NA  0   9.6   5    1  0           336 #> 337 20  24.0    0    NA  0  41.0   6    2  0           337 #> 338 20  36.0    0    NA  0   8.0   5    1  0           338 #> 339 20  36.0    0    NA  0  30.0   6    2  0           339 #> 340 20  48.0    0    NA  0   6.6   5    1  0           340 #> 341 20  48.0    0    NA  0  22.0   6    2  0           341 #> 342 20  72.0    0    NA  0   5.6   5    1  0           342 #> 343 20  72.0    0    NA  0  23.0   6    2  0           343 #> 344 20  96.0    0    NA  0   3.5   5    1  0           344 #> 345 20  96.0    0    NA  0  23.0   6    2  0           345 #> 346 20 120.0    0    NA  0   2.3   5    1  0           346 #> 347 20 120.0    0    NA  0  35.0   6    2  0           347 #> 348 21   0.0    1  93.0  0    NA   1    0  0           348 #> 349 21   0.0    0    NA  0 100.0   6    2  0           349 #> 350 21  24.0    0    NA  0   7.3   5    1  0           350 #> 351 21  24.0    0    NA  0  46.0   6    2  0           351 #> 352 21  36.0    0    NA  0   6.1   5    1  0           352 #> 353 21  36.0    0    NA  0  27.0   6    2  0           353 #> 354 21  48.0    0    NA  0   4.3   5    1  0           354 #> 355 21  48.0    0    NA  0  22.0   6    2  0           355 #> 356 21  72.0    0    NA  0   3.2   5    1  0           356 #> 357 21  72.0    0    NA  0  36.0   6    2  0           357 #> 358 21  96.0    0    NA  0   2.3   5    1  0           358 #> 359 21  96.0    0    NA  0  40.0   6    2  0           359 #> 360 21 120.0    0    NA  0   1.9   5    1  0           360 #> 361 21 120.0    0    NA  0  44.0   6    2  0           361 #> 362 22   0.0    1  87.0  0    NA   1    0  0           362 #> 363 22   0.0    0    NA  0 100.0   6    2  0           363 #> 364 22  24.0    0    NA  0   8.9   5    1  0           364 #> 365 22  24.0    0    NA  0  35.0   6    2  0           365 #> 366 22  36.0    0    NA  0   8.4   5    1  0           366 #> 367 22  36.0    0    NA  0  27.0   6    2  0           367 #> 368 22  48.0    0    NA  0   8.0   5    1  0           368 #> 369 22  48.0    0    NA  0  23.0   6    2  0           369 #> 370 22  72.0    0    NA  0   4.4   5    1  0           370 #> 371 22  72.0    0    NA  0  27.0   6    2  0           371 #> 372 22  96.0    0    NA  0   3.2   5    1  0           372 #> 373 22  96.0    0    NA  0  43.0   6    2  0           373 #> 374 22 120.0    0    NA  0   1.7   5    1  0           374 #> 375 22 120.0    0    NA  0  43.0   6    2  0           375 #> 376 23   0.0    1 110.0  0    NA   1    0  0           376 #> 377 23   0.0    0    NA  0 100.0   6    2  0           377 #> 378 23  24.0    0    NA  0   9.8   5    1  0           378 #> 379 23  24.0    0    NA  0  34.0   6    2  0           379 #> 380 23  36.0    0    NA  0   8.4   5    1  0           380 #> 381 23  36.0    0    NA  0  24.0   6    2  0           381 #> 382 23  48.0    0    NA  0   6.6   5    1  0           382 #> 383 23  48.0    0    NA  0  15.0   6    2  0           383 #> 384 23  72.0    0    NA  0   4.8   5    1  0           384 #> 385 23  72.0    0    NA  0  15.0   6    2  0           385 #> 386 23  96.0    0    NA  0   3.2   5    1  0           386 #> 387 23  96.0    0    NA  0  19.0   6    2  0           387 #> 388 23 120.0    0    NA  0   2.4   5    1  0           388 #> 389 23 120.0    0    NA  0  19.0   6    2  0           389 #> 390 24   0.0    1 115.0  0    NA   1    0  0           390 #> 391 24   0.0    0    NA  0  88.0   6    2  0           391 #> 392 24  24.0    0    NA  0   8.2   5    1  0           392 #> 393 24  24.0    0    NA  0  37.0   6    2  0           393 #> 394 24  36.0    0    NA  0   7.5   5    1  0           394 #> 395 24  36.0    0    NA  0  20.0   6    2  0           395 #> 396 24  48.0    0    NA  0   6.8   5    1  0           396 #> 397 24  48.0    0    NA  0  20.0   6    2  0           397 #> 398 24  72.0    0    NA  0   5.5   5    1  0           398 #> 399 24  72.0    0    NA  0  26.0   6    2  0           399 #> 400 24  96.0    0    NA  0   4.5   5    1  0           400 #> 401 24  96.0    0    NA  0  28.0   6    2  0           401 #> 402 24 120.0    0    NA  0   3.7   5    1  0           402 #> 403 24 120.0    0    NA  0  50.0   6    2  0           403 #> 404 25   0.0    1 112.0  0    NA   1    0  0           404 #> 405 25   0.0    0    NA  0 100.0   6    2  0           405 #> 406 25  24.0    0    NA  0  11.0   5    1  0           406 #> 407 25  24.0    0    NA  0  32.0   6    2  0           407 #> 408 25  36.0    0    NA  0  10.0   5    1  0           408 #> 409 25  36.0    0    NA  0  20.0   6    2  0           409 #> 410 25  48.0    0    NA  0   8.2   5    1  0           410 #> 411 25  48.0    0    NA  0  17.0   6    2  0           411 #> 412 25  72.0    0    NA  0   6.0   5    1  0           412 #> 413 25  72.0    0    NA  0  19.0   6    2  0           413 #> 414 25  96.0    0    NA  0   3.7   5    1  0           414 #> 415 25  96.0    0    NA  0  21.0   6    2  0           415 #> 416 25 120.0    0    NA  0   2.6   5    1  0           416 #> 417 25 120.0    0    NA  0  30.0   6    2  0           417 #> 418 26   0.0    1 120.0  0    NA   1    0  0           418 #> 419 26   0.0    0    NA  0 100.0   6    2  0           419 #> 420 26  24.0    0    NA  0  10.0   5    1  0           420 #> 421 26  24.0    0    NA  0  41.0   6    2  0           421 #> 422 26  36.0    0    NA  0   9.0   5    1  0           422 #> 423 26  36.0    0    NA  0  28.0   6    2  0           423 #> 424 26  48.0    0    NA  0   7.3   5    1  0           424 #> 425 26  48.0    0    NA  0  19.0   6    2  0           425 #> 426 26  72.0    0    NA  0   5.2   5    1  0           426 #> 427 26  72.0    0    NA  0  17.0   6    2  0           427 #> 428 26  96.0    0    NA  0   3.7   5    1  0           428 #> 429 26  96.0    0    NA  0  17.0   6    2  0           429 #> 430 26 120.0    0    NA  0   2.7   5    1  0           430 #> 431 26 120.0    0    NA  0  24.0   6    2  0           431 #> 432 27   0.0    1 120.0  0    NA   1    0  0           432 #> 433 27   0.0    0    NA  0 100.0   6    2  0           433 #> 434 27  24.0    0    NA  0  11.8   5    1  0           434 #> 435 27  24.0    0    NA  0  32.0   6    2  0           435 #> 436 27  36.0    0    NA  0   9.2   5    1  0           436 #> 437 27  36.0    0    NA  0  21.0   6    2  0           437 #> 438 27  48.0    0    NA  0   7.7   5    1  0           438 #> 439 27  48.0    0    NA  0  19.0   6    2  0           439 #> 440 27  72.0    0    NA  0   4.9   5    1  0           440 #> 441 27  72.0    0    NA  0  22.0   6    2  0           441 #> 442 27  96.0    0    NA  0   3.4   5    1  0           442 #> 443 27  96.0    0    NA  0  33.0   6    2  0           443 #> 444 27 120.0    0    NA  0   2.7   5    1  0           444 #> 445 27 120.0    0    NA  0  46.0   6    2  0           445 #> 446 28   0.0    1 120.0  0    NA   1    0  0           446 #> 447 28   0.0    0    NA  0 100.0   6    2  0           447 #> 448 28  24.0    0    NA  0  10.1   5    1  0           448 #> 449 28  24.0    0    NA  0  39.0   6    2  0           449 #> 450 28  36.0    0    NA  0   8.0   5    1  0           450 #> 451 28  36.0    0    NA  0  25.0   6    2  0           451 #> 452 28  48.0    0    NA  0   6.0   5    1  0           452 #> 453 28  48.0    0    NA  0  16.0   6    2  0           453 #> 454 28  72.0    0    NA  0   4.9   5    1  0           454 #> 455 28  72.0    0    NA  0  14.0   6    2  0           455 #> 456 28  96.0    0    NA  0   3.4   5    1  0           456 #> 457 28  96.0    0    NA  0  15.0   6    2  0           457 #> 458 28 120.0    0    NA  0   2.0   5    1  0           458 #> 459 28 120.0    0    NA  0  20.0   6    2  0           459 #> 460 29   0.0    1 153.0  0    NA   1    0  0           460 #> 461 29   0.0    0    NA  0  86.0   6    2  0           461 #> 462 29  24.0    0    NA  0   8.3   5    1  0           462 #> 463 29  24.0    0    NA  0  35.0   6    2  0           463 #> 464 29  36.0    0    NA  0   7.0   5    1  0           464 #> 465 29  36.0    0    NA  0  21.0   6    2  0           465 #> 466 29  48.0    0    NA  0   5.6   5    1  0           466 #> 467 29  48.0    0    NA  0  18.0   6    2  0           467 #> 468 29  72.0    0    NA  0   4.1   5    1  0           468 #> 469 29  72.0    0    NA  0  20.0   6    2  0           469 #> 470 29  96.0    0    NA  0   3.1   5    1  0           470 #> 471 29  96.0    0    NA  0  29.0   6    2  0           471 #> 472 29 120.0    0    NA  0   2.2   5    1  0           472 #> 473 29 120.0    0    NA  0  41.0   6    2  0           473 #> 474 30   0.0    1 105.0  0    NA   1    0  0           474 #> 475 30   0.0    0    NA  0 100.0   6    2  0           475 #> 476 30  24.0    0    NA  0   9.9   5    1  0           476 #> 477 30  24.0    0    NA  0  45.0   6    2  0           477 #> 478 30  36.0    0    NA  0   7.5   5    1  0           478 #> 479 30  36.0    0    NA  0  24.0   6    2  0           479 #> 480 30  48.0    0    NA  0   6.5   5    1  0           480 #> 481 30  48.0    0    NA  0  23.0   6    2  0           481 #> 482 30  72.0    0    NA  0   4.1   5    1  0           482 #> 483 30  72.0    0    NA  0  26.0   6    2  0           483 #> 484 30  96.0    0    NA  0   2.9   5    1  0           484 #> 485 30  96.0    0    NA  0  28.0   6    2  0           485 #> 486 30 120.0    0    NA  0   2.3   5    1  0           486 #> 487 30 120.0    0    NA  0  39.0   6    2  0           487 #> 488 31   0.0    1 125.0  0    NA   1    0  0           488 #> 489 31   0.0    0    NA  0 100.0   6    2  0           489 #> 490 31  24.0    0    NA  0   9.5   5    1  0           490 #> 491 31  24.0    0    NA  0  45.0   6    2  0           491 #> 492 31  36.0    0    NA  0   7.8   5    1  0           492 #> 493 31  36.0    0    NA  0  30.0   6    2  0           493 #> 494 31  48.0    0    NA  0   6.4   5    1  0           494 #> 495 31  48.0    0    NA  0  24.0   6    2  0           495 #> 496 31  72.0    0    NA  0   4.5   5    1  0           496 #> 497 31  72.0    0    NA  0  22.0   6    2  0           497 #> 498 31  96.0    0    NA  0   3.4   5    1  0           498 #> 499 31  96.0    0    NA  0  28.0   6    2  0           499 #> 500 31 120.0    0    NA  0   2.5   5    1  0           500 #> 501 31 120.0    0    NA  0  42.0   6    2  0           501 #> 502 32   0.0    1  93.0  0    NA   1    0  0           502 #> 503 32   0.0    0    NA  0 100.0   6    2  0           503 #> 504 32  24.0    0    NA  0   8.9   5    1  0           504 #> 505 32  24.0    0    NA  0  36.0   6    2  0           505 #> 506 32  36.0    0    NA  0   7.7   5    1  0           506 #> 507 32  36.0    0    NA  0  27.0   6    2  0           507 #> 508 32  48.0    0    NA  0   6.9   5    1  0           508 #> 509 32  48.0    0    NA  0  24.0   6    2  0           509 #> 510 32  72.0    0    NA  0   4.4   5    1  0           510 #> 511 32  72.0    0    NA  0  23.0   6    2  0           511 #> 512 32  96.0    0    NA  0   3.5   5    1  0           512 #> 513 32  96.0    0    NA  0  20.0   6    2  0           513 #> 514 32 120.0    0    NA  0   2.5   5    1  0           514 #> 515 32 120.0    0    NA  0  22.0   6    2  0           515  bblDatToRxode(pk.turnover.emax3, nlmixr2data::warfarin) #>   #>   #>     ID  TIME EVID   AMT II    DV CMT DVID SS nlmixrRowNums #> 1    1   0.0    1 100.0  0    NA   1    0  0             1 #> 2    1   0.5    0    NA  0   0.0   5    1  0             2 #> 3    1   1.0    0    NA  0   1.9   5    1  0             3 #> 4    1   2.0    0    NA  0   3.3   5    1  0             4 #> 5    1   3.0    0    NA  0   6.6   5    1  0             5 #> 6    1   6.0    0    NA  0   9.1   5    1  0             6 #> 7    1   9.0    0    NA  0  10.8   5    1  0             7 #> 8    1  12.0    0    NA  0   8.6   5    1  0             8 #> 9    1  24.0    0    NA  0   5.6   5    1  0             9 #> 10   1  24.0    0    NA  0  44.0   6    2  0            10 #> 11   1  36.0    0    NA  0   4.0   5    1  0            11 #> 12   1  36.0    0    NA  0  27.0   6    2  0            12 #> 13   1  48.0    0    NA  0   2.7   5    1  0            13 #> 14   1  48.0    0    NA  0  28.0   6    2  0            14 #> 15   1  72.0    0    NA  0   0.8   5    1  0            15 #> 16   1  72.0    0    NA  0  31.0   6    2  0            16 #> 17   1  96.0    0    NA  0  60.0   6    2  0            17 #> 18   1 120.0    0    NA  0  65.0   6    2  0            18 #> 19   1 144.0    0    NA  0  71.0   6    2  0            19 #> 20   2   0.0    1 100.0  0    NA   1    0  0            20 #> 21   2   0.0    0    NA  0 100.0   6    2  0            21 #> 22   2  24.0    0    NA  0   9.2   5    1  0            22 #> 23   2  24.0    0    NA  0  49.0   6    2  0            23 #> 24   2  36.0    0    NA  0   8.5   5    1  0            24 #> 25   2  36.0    0    NA  0  32.0   6    2  0            25 #> 26   2  48.0    0    NA  0   6.4   5    1  0            26 #> 27   2  48.0    0    NA  0  26.0   6    2  0            27 #> 28   2  72.0    0    NA  0   4.8   5    1  0            28 #> 29   2  72.0    0    NA  0  22.0   6    2  0            29 #> 30   2  96.0    0    NA  0   3.1   5    1  0            30 #> 31   2  96.0    0    NA  0  28.0   6    2  0            31 #> 32   2 120.0    0    NA  0   2.5   5    1  0            32 #> 33   2 120.0    0    NA  0  33.0   6    2  0            33 #> 34   3   0.0    1 100.0  0    NA   1    0  0            34 #> 35   3   0.0    0    NA  0 100.0   6    2  0            35 #> 36   3   0.5    0    NA  0   0.0   5    1  0            36 #> 37   3   2.0    0    NA  0   8.4   5    1  0            37 #> 38   3   3.0    0    NA  0   9.7   5    1  0            38 #> 39   3   6.0    0    NA  0   9.8   5    1  0            39 #> 40   3  12.0    0    NA  0  11.0   5    1  0            40 #> 41   3  24.0    0    NA  0   8.3   5    1  0            41 #> 42   3  24.0    0    NA  0  46.0   6    2  0            42 #> 43   3  36.0    0    NA  0   7.7   5    1  0            43 #> 44   3  36.0    0    NA  0  22.0   6    2  0            44 #> 45   3  48.0    0    NA  0   6.3   5    1  0            45 #> 46   3  48.0    0    NA  0  19.0   6    2  0            46 #> 47   3  72.0    0    NA  0   4.1   5    1  0            47 #> 48   3  72.0    0    NA  0  20.0   6    2  0            48 #> 49   3  96.0    0    NA  0   3.0   5    1  0            49 #> 50   3  96.0    0    NA  0  42.0   6    2  0            50 #> 51   3 120.0    0    NA  0   1.4   5    1  0            51 #> 52   3 120.0    0    NA  0  49.0   6    2  0            52 #> 53   3 144.0    0    NA  0  54.0   6    2  0            53 #> 54   4   0.0    1 120.0  0    NA   1    0  0            54 #> 55   4   0.0    0    NA  0 100.0   6    2  0            55 #> 56   4   3.0    0    NA  0  12.0   5    1  0            56 #> 57   4   6.0    0    NA  0  13.2   5    1  0            57 #> 58   4   9.0    0    NA  0  14.4   5    1  0            58 #> 59   4  24.0    0    NA  0   9.6   5    1  0            59 #> 60   4  24.0    0    NA  0  30.0   6    2  0            60 #> 61   4  36.0    0    NA  0   8.2   5    1  0            61 #> 62   4  36.0    0    NA  0  24.0   6    2  0            62 #> 63   4  48.0    0    NA  0   7.8   5    1  0            63 #> 64   4  48.0    0    NA  0  13.0   6    2  0            64 #> 65   4  72.0    0    NA  0   5.8   5    1  0            65 #> 66   4  72.0    0    NA  0   9.0   6    2  0            66 #> 67   4  96.0    0    NA  0   4.3   5    1  0            67 #> 68   4  96.0    0    NA  0   9.0   6    2  0            68 #> 69   4 120.0    0    NA  0   3.0   5    1  0            69 #> 70   4 120.0    0    NA  0  11.0   6    2  0            70 #> 71   4 144.0    0    NA  0  12.0   6    2  0            71 #> 72   5   0.0    1  60.0  0    NA   1    0  0            72 #> 73   5   0.0    0    NA  0  82.0   6    2  0            73 #> 74   5   3.0    0    NA  0  11.1   5    1  0            74 #> 75   5   6.0    0    NA  0  11.9   5    1  0            75 #> 76   5   9.0    0    NA  0   9.8   5    1  0            76 #> 77   5  12.0    0    NA  0  11.0   5    1  0            77 #> 78   5  24.0    0    NA  0   8.5   5    1  0            78 #> 79   5  24.0    0    NA  0  43.0   6    2  0            79 #> 80   5  36.0    0    NA  0   7.6   5    1  0            80 #> 81   5  36.0    0    NA  0  25.0   6    2  0            81 #> 82   5  48.0    0    NA  0   5.4   5    1  0            82 #> 83   5  48.0    0    NA  0  18.0   6    2  0            83 #> 84   5  72.0    0    NA  0   4.5   5    1  0            84 #> 85   5  72.0    0    NA  0  17.0   6    2  0            85 #> 86   5  96.0    0    NA  0   3.3   5    1  0            86 #> 87   5  96.0    0    NA  0  23.0   6    2  0            87 #> 88   5 120.0    0    NA  0   2.3   5    1  0            88 #> 89   5 120.0    0    NA  0  29.0   6    2  0            89 #> 90   5 144.0    0    NA  0  41.0   6    2  0            90 #> 91   6   0.0    1 113.0  0    NA   1    0  0            91 #> 92   6   0.0    0    NA  0 100.0   6    2  0            92 #> 93   6   6.0    0    NA  0   8.6   5    1  0            93 #> 94   6  12.0    0    NA  0   8.6   5    1  0            94 #> 95   6  24.0    0    NA  0   7.0   5    1  0            95 #> 96   6  24.0    0    NA  0  34.0   6    2  0            96 #> 97   6  36.0    0    NA  0   5.7   5    1  0            97 #> 98   6  36.0    0    NA  0  23.0   6    2  0            98 #> 99   6  48.0    0    NA  0   4.7   5    1  0            99 #> 100  6  48.0    0    NA  0  20.0   6    2  0           100 #> 101  6  72.0    0    NA  0   3.3   5    1  0           101 #> 102  6  72.0    0    NA  0  16.0   6    2  0           102 #> 103  6  96.0    0    NA  0   2.3   5    1  0           103 #> 104  6  96.0    0    NA  0  17.0   6    2  0           104 #> 105  6 120.0    0    NA  0   1.7   5    1  0           105 #> 106  6 120.0    0    NA  0  18.0   6    2  0           106 #> 107  6 144.0    0    NA  0  25.0   6    2  0           107 #> 108  7   0.0    1  90.0  0    NA   1    0  0           108 #> 109  7   3.0    0    NA  0  13.4   5    1  0           109 #> 110  7   6.0    0    NA  0  12.4   5    1  0           110 #> 111  7   9.0    0    NA  0  12.7   5    1  0           111 #> 112  7  12.0    0    NA  0   8.8   5    1  0           112 #> 113  7  24.0    0    NA  0   6.1   5    1  0           113 #> 114  7  24.0    0    NA  0  36.0   6    2  0           114 #> 115  7  36.0    0    NA  0   3.5   5    1  0           115 #> 116  7  36.0    0    NA  0  33.0   6    2  0           116 #> 117  7  48.0    0    NA  0   1.8   5    1  0           117 #> 118  7  48.0    0    NA  0  28.0   6    2  0           118 #> 119  7  72.0    0    NA  0   1.5   5    1  0           119 #> 120  7  72.0    0    NA  0  52.0   6    2  0           120 #> 121  7  96.0    0    NA  0   1.0   5    1  0           121 #> 122  7  96.0    0    NA  0  80.0   6    2  0           122 #> 123  7 120.0    0    NA  0  90.0   6    2  0           123 #> 124  7 144.0    0    NA  0 100.0   6    2  0           124 #> 125  8   0.0    1 135.0  0    NA   1    0  0           125 #> 126  8   0.0    0    NA  0  88.0   6    2  0           126 #> 127  8   2.0    0    NA  0  17.6   5    1  0           127 #> 128  8   3.0    0    NA  0  17.3   5    1  0           128 #> 129  8   6.0    0    NA  0  15.0   5    1  0           129 #> 130  8   9.0    0    NA  0  15.0   5    1  0           130 #> 131  8  12.0    0    NA  0  12.4   5    1  0           131 #> 132  8  24.0    0    NA  0   7.9   5    1  0           132 #> 133  8  24.0    0    NA  0  35.0   6    2  0           133 #> 134  8  36.0    0    NA  0   7.9   5    1  0           134 #> 135  8  36.0    0    NA  0  20.0   6    2  0           135 #> 136  8  48.0    0    NA  0   5.1   5    1  0           136 #> 137  8  48.0    0    NA  0  12.0   6    2  0           137 #> 138  8  72.0    0    NA  0   3.6   5    1  0           138 #> 139  8  72.0    0    NA  0  16.0   6    2  0           139 #> 140  8  96.0    0    NA  0   2.4   5    1  0           140 #> 141  8  96.0    0    NA  0  23.0   6    2  0           141 #> 142  8 120.0    0    NA  0   2.0   5    1  0           142 #> 143  8 120.0    0    NA  0  36.0   6    2  0           143 #> 144  8 144.0    0    NA  0  48.0   6    2  0           144 #> 145  9   0.0    1  75.0  0    NA   1    0  0           145 #> 146  9   0.0    0    NA  0  92.0   6    2  0           146 #> 147  9   0.5    0    NA  0   0.0   5    1  0           147 #> 148  9   1.0    0    NA  0   1.0   5    1  0           148 #> 149  9   2.0    0    NA  0   4.6   5    1  0           149 #> 150  9   3.0    0    NA  0  12.7   5    1  0           150 #> 151  9   3.0    0    NA  0   8.0   5    1  0           151 #> 152  9   6.0    0    NA  0  12.7   5    1  0           152 #> 153  9   6.0    0    NA  0  11.5   5    1  0           153 #> 154  9   9.0    0    NA  0  12.9   5    1  0           154 #> 155  9   9.0    0    NA  0  11.4   5    1  0           155 #> 156  9  12.0    0    NA  0  11.4   5    1  0           156 #> 157  9  12.0    0    NA  0  11.0   5    1  0           157 #> 158  9  24.0    0    NA  0   9.1   5    1  0           158 #> 159  9  24.0    0    NA  0  33.0   6    2  0           159 #> 160  9  36.0    0    NA  0   8.2   5    1  0           160 #> 161  9  36.0    0    NA  0  22.0   6    2  0           161 #> 162  9  48.0    0    NA  0   5.9   5    1  0           162 #> 163  9  48.0    0    NA  0  16.0   6    2  0           163 #> 164  9  72.0    0    NA  0   3.6   5    1  0           164 #> 165  9  72.0    0    NA  0  18.0   6    2  0           165 #> 166  9  96.0    0    NA  0   1.7   5    1  0           166 #> 167  9  96.0    0    NA  0  32.0   6    2  0           167 #> 168  9 120.0    0    NA  0   1.1   5    1  0           168 #> 169  9 120.0    0    NA  0  30.0   6    2  0           169 #> 170  9 144.0    0    NA  0  45.0   6    2  0           170 #> 171 10   0.0    1 105.0  0    NA   1    0  0           171 #> 172 10   0.0    0    NA  0  90.0   6    2  0           172 #> 173 10  24.0    0    NA  0   8.6   5    1  0           173 #> 174 10  24.0    0    NA  0  39.0   6    2  0           174 #> 175 10  36.0    0    NA  0   8.0   5    1  0           175 #> 176 10  36.0    0    NA  0  22.0   6    2  0           176 #> 177 10  48.0    0    NA  0   6.0   5    1  0           177 #> 178 10  48.0    0    NA  0  17.0   6    2  0           178 #> 179 10  72.0    0    NA  0   4.4   5    1  0           179 #> 180 10  72.0    0    NA  0  17.0   6    2  0           180 #> 181 10  96.0    0    NA  0   3.6   5    1  0           181 #> 182 10  96.0    0    NA  0  22.0   6    2  0           182 #> 183 10 120.0    0    NA  0   2.8   5    1  0           183 #> 184 10 120.0    0    NA  0  25.0   6    2  0           184 #> 185 10 144.0    0    NA  0  33.0   6    2  0           185 #> 186 11   0.0    1 123.0  0    NA   1    0  0           186 #> 187 11   0.0    0    NA  0 100.0   6    2  0           187 #> 188 11   1.5    0    NA  0  11.4   5    1  0           188 #> 189 11   3.0    0    NA  0  15.4   5    1  0           189 #> 190 11   6.0    0    NA  0  17.5   5    1  0           190 #> 191 11  12.0    0    NA  0  14.0   5    1  0           191 #> 192 11  24.0    0    NA  0   9.0   5    1  0           192 #> 193 11  24.0    0    NA  0  37.0   6    2  0           193 #> 194 11  36.0    0    NA  0   8.9   5    1  0           194 #> 195 11  36.0    0    NA  0  24.0   6    2  0           195 #> 196 11  48.0    0    NA  0   6.6   5    1  0           196 #> 197 11  48.0    0    NA  0  14.0   6    2  0           197 #> 198 11  72.0    0    NA  0   4.2   5    1  0           198 #> 199 11  72.0    0    NA  0  11.0   6    2  0           199 #> 200 11  96.0    0    NA  0   3.6   5    1  0           200 #> 201 11  96.0    0    NA  0  14.0   6    2  0           201 #> 202 11 120.0    0    NA  0   2.6   5    1  0           202 #> 203 11 120.0    0    NA  0  23.0   6    2  0           203 #> 204 11 144.0    0    NA  0  33.0   6    2  0           204 #> 205 12   0.0    1 113.0  0    NA   1    0  0           205 #> 206 12   0.0    0    NA  0  85.0   6    2  0           206 #> 207 12   1.5    0    NA  0   0.6   5    1  0           207 #> 208 12   3.0    0    NA  0   2.8   5    1  0           208 #> 209 12   6.0    0    NA  0  13.8   5    1  0           209 #> 210 12   9.0    0    NA  0  15.0   5    1  0           210 #> 211 12  24.0    0    NA  0  10.5   5    1  0           211 #> 212 12  24.0    0    NA  0  25.0   6    2  0           212 #> 213 12  36.0    0    NA  0   9.1   5    1  0           213 #> 214 12  36.0    0    NA  0  15.0   6    2  0           214 #> 215 12  48.0    0    NA  0   6.6   5    1  0           215 #> 216 12  48.0    0    NA  0  11.0   6    2  0           216 #> 217 12  72.0    0    NA  0   4.9   5    1  0           217 #> 218 12  96.0    0    NA  0   2.4   5    1  0           218 #> 219 12 120.0    0    NA  0   1.9   5    1  0           219 #> 220 13   0.0    1 113.0  0    NA   1    0  0           220 #> 221 13   0.0    0    NA  0  88.0   6    2  0           221 #> 222 13   1.5    0    NA  0   3.6   5    1  0           222 #> 223 13   3.0    0    NA  0  12.9   5    1  0           223 #> 224 13   6.0    0    NA  0  12.9   5    1  0           224 #> 225 13   9.0    0    NA  0  10.2   5    1  0           225 #> 226 13  24.0    0    NA  0   6.4   5    1  0           226 #> 227 13  24.0    0    NA  0  41.0   6    2  0           227 #> 228 13  36.0    0    NA  0   6.9   5    1  0           228 #> 229 13  36.0    0    NA  0  23.0   6    2  0           229 #> 230 13  48.0    0    NA  0   4.5   5    1  0           230 #> 231 13  48.0    0    NA  0  16.0   6    2  0           231 #> 232 13  72.0    0    NA  0   3.2   5    1  0           232 #> 233 13  72.0    0    NA  0  14.0   6    2  0           233 #> 234 13  96.0    0    NA  0   2.4   5    1  0           234 #> 235 13  96.0    0    NA  0  18.0   6    2  0           235 #> 236 13 120.0    0    NA  0   1.3   5    1  0           236 #> 237 13 120.0    0    NA  0  22.0   6    2  0           237 #> 238 13 144.0    0    NA  0  35.0   6    2  0           238 #> 239 14   0.0    1  75.0  0    NA   1    0  0           239 #> 240 14   0.0    0    NA  0  85.0   6    2  0           240 #> 241 14   0.5    0    NA  0   0.0   5    1  0           241 #> 242 14   1.0    0    NA  0   2.7   5    1  0           242 #> 243 14   2.0    0    NA  0  11.6   5    1  0           243 #> 244 14   3.0    0    NA  0  11.6   5    1  0           244 #> 245 14   6.0    0    NA  0  11.3   5    1  0           245 #> 246 14   9.0    0    NA  0   9.7   5    1  0           246 #> 247 14  24.0    0    NA  0   6.5   5    1  0           247 #> 248 14  24.0    0    NA  0  32.0   6    2  0           248 #> 249 14  36.0    0    NA  0   5.2   5    1  0           249 #> 250 14  36.0    0    NA  0  22.0   6    2  0           250 #> 251 14  48.0    0    NA  0   3.6   5    1  0           251 #> 252 14  48.0    0    NA  0  21.0   6    2  0           252 #> 253 14  72.0    0    NA  0   2.4   5    1  0           253 #> 254 14  72.0    0    NA  0  28.0   6    2  0           254 #> 255 14  96.0    0    NA  0   0.9   5    1  0           255 #> 256 14  96.0    0    NA  0  38.0   6    2  0           256 #> 257 14 120.0    0    NA  0  46.0   6    2  0           257 #> 258 14 144.0    0    NA  0  65.0   6    2  0           258 #> 259 15   0.0    1  85.0  0    NA   1    0  0           259 #> 260 15   0.0    0    NA  0 100.0   6    2  0           260 #> 261 15   1.0    0    NA  0   6.6   5    1  0           261 #> 262 15   3.0    0    NA  0  11.9   5    1  0           262 #> 263 15   6.0    0    NA  0  11.7   5    1  0           263 #> 264 15   9.0    0    NA  0  12.2   5    1  0           264 #> 265 15  24.0    0    NA  0   8.1   5    1  0           265 #> 266 15  24.0    0    NA  0  43.0   6    2  0           266 #> 267 15  36.0    0    NA  0   7.4   5    1  0           267 #> 268 15  36.0    0    NA  0  26.0   6    2  0           268 #> 269 15  48.0    0    NA  0   6.8   5    1  0           269 #> 270 15  48.0    0    NA  0  15.0   6    2  0           270 #> 271 15  72.0    0    NA  0   5.3   5    1  0           271 #> 272 15  72.0    0    NA  0  13.0   6    2  0           272 #> 273 15  96.0    0    NA  0   3.0   5    1  0           273 #> 274 15  96.0    0    NA  0  21.0   6    2  0           274 #> 275 15 120.0    0    NA  0   2.0   5    1  0           275 #> 276 15 120.0    0    NA  0  28.0   6    2  0           276 #> 277 15 144.0    0    NA  0  39.0   6    2  0           277 #> 278 16   0.0    1  87.0  0    NA   1    0  0           278 #> 279 16   0.0    0    NA  0 100.0   6    2  0           279 #> 280 16  24.0    0    NA  0  10.4   5    1  0           280 #> 281 16  24.0    0    NA  0  42.0   6    2  0           281 #> 282 16  36.0    0    NA  0   8.9   5    1  0           282 #> 283 16  36.0    0    NA  0  32.0   6    2  0           283 #> 284 16  48.0    0    NA  0   7.0   5    1  0           284 #> 285 16  48.0    0    NA  0  26.0   6    2  0           285 #> 286 16  72.0    0    NA  0   4.4   5    1  0           286 #> 287 16  72.0    0    NA  0  31.0   6    2  0           287 #> 288 16  96.0    0    NA  0   3.2   5    1  0           288 #> 289 16  96.0    0    NA  0  33.0   6    2  0           289 #> 290 16 120.0    0    NA  0   2.4   5    1  0           290 #> 291 16 120.0    0    NA  0  54.0   6    2  0           291 #> 292 17   0.0    1 117.0  0    NA   1    0  0           292 #> 293 17   0.0    0    NA  0 100.0   6    2  0           293 #> 294 17  24.0    0    NA  0   7.6   5    1  0           294 #> 295 17  24.0    0    NA  0  35.0   6    2  0           295 #> 296 17  36.0    0    NA  0   6.4   5    1  0           296 #> 297 17  36.0    0    NA  0  23.0   6    2  0           297 #> 298 17  48.0    0    NA  0   6.0   5    1  0           298 #> 299 17  48.0    0    NA  0  17.0   6    2  0           299 #> 300 17  72.0    0    NA  0   4.0   5    1  0           300 #> 301 17  72.0    0    NA  0  18.0   6    2  0           301 #> 302 17  96.0    0    NA  0   3.1   5    1  0           302 #> 303 17  96.0    0    NA  0  18.0   6    2  0           303 #> 304 17 120.0    0    NA  0   2.0   5    1  0           304 #> 305 17 120.0    0    NA  0  21.0   6    2  0           305 #> 306 18   0.0    1 112.0  0    NA   1    0  0           306 #> 307 18   0.0    0    NA  0 100.0   6    2  0           307 #> 308 18  24.0    0    NA  0   7.6   5    1  0           308 #> 309 18  24.0    0    NA  0  32.0   6    2  0           309 #> 310 18  36.0    0    NA  0   6.6   5    1  0           310 #> 311 18  36.0    0    NA  0  20.0   6    2  0           311 #> 312 18  48.0    0    NA  0   5.4   5    1  0           312 #> 313 18  48.0    0    NA  0  18.0   6    2  0           313 #> 314 18  72.0    0    NA  0   3.4   5    1  0           314 #> 315 18  72.0    0    NA  0  18.0   6    2  0           315 #> 316 18  96.0    0    NA  0   1.2   5    1  0           316 #> 317 18  96.0    0    NA  0  19.0   6    2  0           317 #> 318 18 120.0    0    NA  0   0.9   5    1  0           318 #> 319 18 120.0    0    NA  0  29.0   6    2  0           319 #> 320 19   0.0    1  95.5  0    NA   1    0  0           320 #> 321 19   0.0    0    NA  0 100.0   6    2  0           321 #> 322 19  24.0    0    NA  0   6.6   5    1  0           322 #> 323 19  24.0    0    NA  0  33.0   6    2  0           323 #> 324 19  36.0    0    NA  0   5.3   5    1  0           324 #> 325 19  36.0    0    NA  0  28.0   6    2  0           325 #> 326 19  48.0    0    NA  0   3.6   5    1  0           326 #> 327 19  48.0    0    NA  0  18.0   6    2  0           327 #> 328 19  72.0    0    NA  0   2.7   5    1  0           328 #> 329 19  72.0    0    NA  0  18.0   6    2  0           329 #> 330 19  96.0    0    NA  0   1.4   5    1  0           330 #> 331 19  96.0    0    NA  0  17.0   6    2  0           331 #> 332 19 120.0    0    NA  0   1.1   5    1  0           332 #> 333 19 120.0    0    NA  0  26.0   6    2  0           333 #> 334 20   0.0    1  88.5  0    NA   1    0  0           334 #> 335 20   0.0    0    NA  0 100.0   6    2  0           335 #> 336 20  24.0    0    NA  0   9.6   5    1  0           336 #> 337 20  24.0    0    NA  0  41.0   6    2  0           337 #> 338 20  36.0    0    NA  0   8.0   5    1  0           338 #> 339 20  36.0    0    NA  0  30.0   6    2  0           339 #> 340 20  48.0    0    NA  0   6.6   5    1  0           340 #> 341 20  48.0    0    NA  0  22.0   6    2  0           341 #> 342 20  72.0    0    NA  0   5.6   5    1  0           342 #> 343 20  72.0    0    NA  0  23.0   6    2  0           343 #> 344 20  96.0    0    NA  0   3.5   5    1  0           344 #> 345 20  96.0    0    NA  0  23.0   6    2  0           345 #> 346 20 120.0    0    NA  0   2.3   5    1  0           346 #> 347 20 120.0    0    NA  0  35.0   6    2  0           347 #> 348 21   0.0    1  93.0  0    NA   1    0  0           348 #> 349 21   0.0    0    NA  0 100.0   6    2  0           349 #> 350 21  24.0    0    NA  0   7.3   5    1  0           350 #> 351 21  24.0    0    NA  0  46.0   6    2  0           351 #> 352 21  36.0    0    NA  0   6.1   5    1  0           352 #> 353 21  36.0    0    NA  0  27.0   6    2  0           353 #> 354 21  48.0    0    NA  0   4.3   5    1  0           354 #> 355 21  48.0    0    NA  0  22.0   6    2  0           355 #> 356 21  72.0    0    NA  0   3.2   5    1  0           356 #> 357 21  72.0    0    NA  0  36.0   6    2  0           357 #> 358 21  96.0    0    NA  0   2.3   5    1  0           358 #> 359 21  96.0    0    NA  0  40.0   6    2  0           359 #> 360 21 120.0    0    NA  0   1.9   5    1  0           360 #> 361 21 120.0    0    NA  0  44.0   6    2  0           361 #> 362 22   0.0    1  87.0  0    NA   1    0  0           362 #> 363 22   0.0    0    NA  0 100.0   6    2  0           363 #> 364 22  24.0    0    NA  0   8.9   5    1  0           364 #> 365 22  24.0    0    NA  0  35.0   6    2  0           365 #> 366 22  36.0    0    NA  0   8.4   5    1  0           366 #> 367 22  36.0    0    NA  0  27.0   6    2  0           367 #> 368 22  48.0    0    NA  0   8.0   5    1  0           368 #> 369 22  48.0    0    NA  0  23.0   6    2  0           369 #> 370 22  72.0    0    NA  0   4.4   5    1  0           370 #> 371 22  72.0    0    NA  0  27.0   6    2  0           371 #> 372 22  96.0    0    NA  0   3.2   5    1  0           372 #> 373 22  96.0    0    NA  0  43.0   6    2  0           373 #> 374 22 120.0    0    NA  0   1.7   5    1  0           374 #> 375 22 120.0    0    NA  0  43.0   6    2  0           375 #> 376 23   0.0    1 110.0  0    NA   1    0  0           376 #> 377 23   0.0    0    NA  0 100.0   6    2  0           377 #> 378 23  24.0    0    NA  0   9.8   5    1  0           378 #> 379 23  24.0    0    NA  0  34.0   6    2  0           379 #> 380 23  36.0    0    NA  0   8.4   5    1  0           380 #> 381 23  36.0    0    NA  0  24.0   6    2  0           381 #> 382 23  48.0    0    NA  0   6.6   5    1  0           382 #> 383 23  48.0    0    NA  0  15.0   6    2  0           383 #> 384 23  72.0    0    NA  0   4.8   5    1  0           384 #> 385 23  72.0    0    NA  0  15.0   6    2  0           385 #> 386 23  96.0    0    NA  0   3.2   5    1  0           386 #> 387 23  96.0    0    NA  0  19.0   6    2  0           387 #> 388 23 120.0    0    NA  0   2.4   5    1  0           388 #> 389 23 120.0    0    NA  0  19.0   6    2  0           389 #> 390 24   0.0    1 115.0  0    NA   1    0  0           390 #> 391 24   0.0    0    NA  0  88.0   6    2  0           391 #> 392 24  24.0    0    NA  0   8.2   5    1  0           392 #> 393 24  24.0    0    NA  0  37.0   6    2  0           393 #> 394 24  36.0    0    NA  0   7.5   5    1  0           394 #> 395 24  36.0    0    NA  0  20.0   6    2  0           395 #> 396 24  48.0    0    NA  0   6.8   5    1  0           396 #> 397 24  48.0    0    NA  0  20.0   6    2  0           397 #> 398 24  72.0    0    NA  0   5.5   5    1  0           398 #> 399 24  72.0    0    NA  0  26.0   6    2  0           399 #> 400 24  96.0    0    NA  0   4.5   5    1  0           400 #> 401 24  96.0    0    NA  0  28.0   6    2  0           401 #> 402 24 120.0    0    NA  0   3.7   5    1  0           402 #> 403 24 120.0    0    NA  0  50.0   6    2  0           403 #> 404 25   0.0    1 112.0  0    NA   1    0  0           404 #> 405 25   0.0    0    NA  0 100.0   6    2  0           405 #> 406 25  24.0    0    NA  0  11.0   5    1  0           406 #> 407 25  24.0    0    NA  0  32.0   6    2  0           407 #> 408 25  36.0    0    NA  0  10.0   5    1  0           408 #> 409 25  36.0    0    NA  0  20.0   6    2  0           409 #> 410 25  48.0    0    NA  0   8.2   5    1  0           410 #> 411 25  48.0    0    NA  0  17.0   6    2  0           411 #> 412 25  72.0    0    NA  0   6.0   5    1  0           412 #> 413 25  72.0    0    NA  0  19.0   6    2  0           413 #> 414 25  96.0    0    NA  0   3.7   5    1  0           414 #> 415 25  96.0    0    NA  0  21.0   6    2  0           415 #> 416 25 120.0    0    NA  0   2.6   5    1  0           416 #> 417 25 120.0    0    NA  0  30.0   6    2  0           417 #> 418 26   0.0    1 120.0  0    NA   1    0  0           418 #> 419 26   0.0    0    NA  0 100.0   6    2  0           419 #> 420 26  24.0    0    NA  0  10.0   5    1  0           420 #> 421 26  24.0    0    NA  0  41.0   6    2  0           421 #> 422 26  36.0    0    NA  0   9.0   5    1  0           422 #> 423 26  36.0    0    NA  0  28.0   6    2  0           423 #> 424 26  48.0    0    NA  0   7.3   5    1  0           424 #> 425 26  48.0    0    NA  0  19.0   6    2  0           425 #> 426 26  72.0    0    NA  0   5.2   5    1  0           426 #> 427 26  72.0    0    NA  0  17.0   6    2  0           427 #> 428 26  96.0    0    NA  0   3.7   5    1  0           428 #> 429 26  96.0    0    NA  0  17.0   6    2  0           429 #> 430 26 120.0    0    NA  0   2.7   5    1  0           430 #> 431 26 120.0    0    NA  0  24.0   6    2  0           431 #> 432 27   0.0    1 120.0  0    NA   1    0  0           432 #> 433 27   0.0    0    NA  0 100.0   6    2  0           433 #> 434 27  24.0    0    NA  0  11.8   5    1  0           434 #> 435 27  24.0    0    NA  0  32.0   6    2  0           435 #> 436 27  36.0    0    NA  0   9.2   5    1  0           436 #> 437 27  36.0    0    NA  0  21.0   6    2  0           437 #> 438 27  48.0    0    NA  0   7.7   5    1  0           438 #> 439 27  48.0    0    NA  0  19.0   6    2  0           439 #> 440 27  72.0    0    NA  0   4.9   5    1  0           440 #> 441 27  72.0    0    NA  0  22.0   6    2  0           441 #> 442 27  96.0    0    NA  0   3.4   5    1  0           442 #> 443 27  96.0    0    NA  0  33.0   6    2  0           443 #> 444 27 120.0    0    NA  0   2.7   5    1  0           444 #> 445 27 120.0    0    NA  0  46.0   6    2  0           445 #> 446 28   0.0    1 120.0  0    NA   1    0  0           446 #> 447 28   0.0    0    NA  0 100.0   6    2  0           447 #> 448 28  24.0    0    NA  0  10.1   5    1  0           448 #> 449 28  24.0    0    NA  0  39.0   6    2  0           449 #> 450 28  36.0    0    NA  0   8.0   5    1  0           450 #> 451 28  36.0    0    NA  0  25.0   6    2  0           451 #> 452 28  48.0    0    NA  0   6.0   5    1  0           452 #> 453 28  48.0    0    NA  0  16.0   6    2  0           453 #> 454 28  72.0    0    NA  0   4.9   5    1  0           454 #> 455 28  72.0    0    NA  0  14.0   6    2  0           455 #> 456 28  96.0    0    NA  0   3.4   5    1  0           456 #> 457 28  96.0    0    NA  0  15.0   6    2  0           457 #> 458 28 120.0    0    NA  0   2.0   5    1  0           458 #> 459 28 120.0    0    NA  0  20.0   6    2  0           459 #> 460 29   0.0    1 153.0  0    NA   1    0  0           460 #> 461 29   0.0    0    NA  0  86.0   6    2  0           461 #> 462 29  24.0    0    NA  0   8.3   5    1  0           462 #> 463 29  24.0    0    NA  0  35.0   6    2  0           463 #> 464 29  36.0    0    NA  0   7.0   5    1  0           464 #> 465 29  36.0    0    NA  0  21.0   6    2  0           465 #> 466 29  48.0    0    NA  0   5.6   5    1  0           466 #> 467 29  48.0    0    NA  0  18.0   6    2  0           467 #> 468 29  72.0    0    NA  0   4.1   5    1  0           468 #> 469 29  72.0    0    NA  0  20.0   6    2  0           469 #> 470 29  96.0    0    NA  0   3.1   5    1  0           470 #> 471 29  96.0    0    NA  0  29.0   6    2  0           471 #> 472 29 120.0    0    NA  0   2.2   5    1  0           472 #> 473 29 120.0    0    NA  0  41.0   6    2  0           473 #> 474 30   0.0    1 105.0  0    NA   1    0  0           474 #> 475 30   0.0    0    NA  0 100.0   6    2  0           475 #> 476 30  24.0    0    NA  0   9.9   5    1  0           476 #> 477 30  24.0    0    NA  0  45.0   6    2  0           477 #> 478 30  36.0    0    NA  0   7.5   5    1  0           478 #> 479 30  36.0    0    NA  0  24.0   6    2  0           479 #> 480 30  48.0    0    NA  0   6.5   5    1  0           480 #> 481 30  48.0    0    NA  0  23.0   6    2  0           481 #> 482 30  72.0    0    NA  0   4.1   5    1  0           482 #> 483 30  72.0    0    NA  0  26.0   6    2  0           483 #> 484 30  96.0    0    NA  0   2.9   5    1  0           484 #> 485 30  96.0    0    NA  0  28.0   6    2  0           485 #> 486 30 120.0    0    NA  0   2.3   5    1  0           486 #> 487 30 120.0    0    NA  0  39.0   6    2  0           487 #> 488 31   0.0    1 125.0  0    NA   1    0  0           488 #> 489 31   0.0    0    NA  0 100.0   6    2  0           489 #> 490 31  24.0    0    NA  0   9.5   5    1  0           490 #> 491 31  24.0    0    NA  0  45.0   6    2  0           491 #> 492 31  36.0    0    NA  0   7.8   5    1  0           492 #> 493 31  36.0    0    NA  0  30.0   6    2  0           493 #> 494 31  48.0    0    NA  0   6.4   5    1  0           494 #> 495 31  48.0    0    NA  0  24.0   6    2  0           495 #> 496 31  72.0    0    NA  0   4.5   5    1  0           496 #> 497 31  72.0    0    NA  0  22.0   6    2  0           497 #> 498 31  96.0    0    NA  0   3.4   5    1  0           498 #> 499 31  96.0    0    NA  0  28.0   6    2  0           499 #> 500 31 120.0    0    NA  0   2.5   5    1  0           500 #> 501 31 120.0    0    NA  0  42.0   6    2  0           501 #> 502 32   0.0    1  93.0  0    NA   1    0  0           502 #> 503 32   0.0    0    NA  0 100.0   6    2  0           503 #> 504 32  24.0    0    NA  0   8.9   5    1  0           504 #> 505 32  24.0    0    NA  0  36.0   6    2  0           505 #> 506 32  36.0    0    NA  0   7.7   5    1  0           506 #> 507 32  36.0    0    NA  0  27.0   6    2  0           507 #> 508 32  48.0    0    NA  0   6.9   5    1  0           508 #> 509 32  48.0    0    NA  0  24.0   6    2  0           509 #> 510 32  72.0    0    NA  0   4.4   5    1  0           510 #> 511 32  72.0    0    NA  0  23.0   6    2  0           511 #> 512 32  96.0    0    NA  0   3.5   5    1  0           512 #> 513 32  96.0    0    NA  0  20.0   6    2  0           513 #> 514 32 120.0    0    NA  0   2.5   5    1  0           514 #> 515 32 120.0    0    NA  0  22.0   6    2  0           515"},{"path":"/reference/getStandardColNames.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine standardized rxode2 column names from data — getStandardColNames","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"Determine standardized rxode2 column names data","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data)"},{"path":"/reference/getStandardColNames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"data data.frame source column names","code":""},{"path":"/reference/getStandardColNames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"named character vector names standardized names values either name column data NA column present data.","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data.frame(ID=1, DV=2, Time=3, CmT=4)) #>     id   time    amt   rate    dur   evid    cmt     ss     ii   addl     dv  #>   \"ID\" \"Time\"     NA     NA     NA     NA  \"CmT\"     NA     NA     NA   \"DV\"  #>    mdv   dvid   cens  limit  #>     NA     NA     NA     NA"},{"path":"/reference/modelUnitConversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit conversion for pharmacokinetic models — modelUnitConversion","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"Unit conversion pharmacokinetic models","code":""},{"path":"/reference/modelUnitConversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion(   dvu = NA_character_,   amtu = NA_character_,   timeu = NA_character_,   volumeu = NA_character_ )"},{"path":"/reference/modelUnitConversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"dvu, amtu, timeu units DV, AMT, TIME columns data volumeu units volume parameters model","code":""},{"path":"/reference/modelUnitConversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"list names units associated parameter (\"amtu\", \"clearanceu\", \"volumeu\", \"timeu\", \"dvu\") numeric value multiply modeled estimate (example, cp) model consistent data units.","code":""},{"path":[]},{"path":"/reference/modelUnitConversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion(dvu = \"ng/mL\", amtu = \"mg\", timeu = \"hr\", volumeu = \"L\") #> Loading required namespace: testthat #> $amtu #> [1] \"mg\" #>  #> $clearanceu #> [1] \"L/h\" #>  #> $volumeu #> [1] \"L\" #>  #> $timeu #> [1] \"hr\" #>  #> $dvu #> [1] \"ng/mL\" #>  #> $cmtu #> [1] \"mg/L\" #>  #> $dvConversion #> [1] 1000 #>"},{"path":"/reference/monolixControl.html","id":null,"dir":"Reference","previous_headings":"","what":"Monolix Controller for nlmixr2 — monolixControl","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Monolix Controller nlmixr2","code":""},{"path":"/reference/monolixControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Monolix Controller for nlmixr2 — monolixControl","text":"","code":"monolixControl(   nbSSDoses = 7,   useLinearization = FALSE,   stiff = FALSE,   addProp = c(\"combined2\", \"combined1\"),   exploratoryAutoStop = FALSE,   smoothingAutoStop = FALSE,   burnInIterations = 5,   smoothingIterations = 200,   exploratoryIterations = 250,   simulatedAnnealingIterations = 250,   exploratoryInterval = 200,   exploratoryAlpha = 0,   omegaTau = 0.95,   errorModelTau = 0.95,   variability = c(\"none\", \"firstStage\", \"decreasing\"),   runCommand = getOption(\"babelmixr2.monolix\", \"\"),   rxControl = NULL,   sumProd = FALSE,   optExpression = TRUE,   calcTables = TRUE,   compress = TRUE,   ci = 0.95,   sigdigTable = NULL,   absolutePath = FALSE,   modelName = NULL,   muRefCovAlg = TRUE,   ... )"},{"path":"/reference/monolixControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Monolix Controller for nlmixr2 — monolixControl","text":"nbSSDoses Number steady state doses (default 7) useLinearization Use linearization log likelihood fim. stiff boolean using stiff ODE solver addProp specifies type additive plus proportional   errors, one standard deviations add (combined1)   type variances add (combined2). combined1 error type can described following equation: $$y = f + (+ b\\times f^c) \\times \\varepsilon$$ combined2 error model can described following equation: $$y = f + \\sqrt{^2 + b^2\\times f^{2\\times c}} \\times \\varepsilon$$ : - y represents observed value - f represents predicted value -  additive standard deviation - b proportional/power standard deviation - c power exponent (proportional case c=1) exploratoryAutoStop logical turn exploratory phase auto-stop SAEM (default 250) smoothingAutoStop Boolean indicating smoothing automatically stop (default FALSE) burnInIterations Number burn iterations smoothingIterations Number smoothing iterations exploratoryIterations Number iterations exploratory phase (default 250) simulatedAnnealingIterations Number simulating annealing iterations exploratoryInterval Minimum number iterations exploratory phase (default 200) exploratoryAlpha Convergence memory exploratory phase (used exploratoryAutoStop TRUE) omegaTau Proportional rate variance simulated annealing errorModelTau Proportional rate error model simulated annealing variability describes methodology parameters without variability.  : - Fixed throughout (none) - Variability first stage (firstStage) - Decreasing reaches fixed value (decreasing) runCommand shell command function run monolix; can specify default options(\"babelmixr2.monolix\"=\"runMonolix\"). empty 'lixoftConnectors' available, use lixoftConnectors run monolix. See details function usage. rxControl `rxode2` ODE solving options fitting, created `rxControl()` sumProd boolean indicating model change multiplication high precision multiplication sums high precision sums using PreciseSums package.  default FALSE. optExpression Optimize rxode2 expression speed calculation. default turned . calcTables boolean determine foceiFit calculate tables. default TRUE compress object compressed items ci Confidence level tables.  default 0.95 95% confidence. sigdigTable Significant digits final output table. specified, matches significant digits `sigdig` optimization algorithm.  `sigdig` NULL, use 3. absolutePath Boolean indicating absolute path used monolix runs modelName Model name used generate NONMEM output.  NULL try infer model name (x clear).  Otherwise use character outputs. muRefCovAlg controls algebraic expressions can   mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#`      algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old   `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model.  Therefore, default `TRUE`. ... Ignored parameters","code":""},{"path":"/reference/monolixControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Monolix Controller for nlmixr2 — monolixControl","text":"monolix control object","code":""},{"path":"/reference/monolixControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Monolix Controller for nlmixr2 — monolixControl","text":"runCommand given string, called system() command like: runCommand mlxtran. example, runCommand=\"'/path//monolix/mlxbsub2021' -p \" command line used look like following: '/path//monolix/mlxbsub2021' monolix.mlxtran runCommand given function, called FUN(mlxtran, directory, ui) run Monolix.  allows run Monolix way may need, long can write R.  babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting Monolix.","code":""},{"path":"/reference/monolixControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Matthew Fidler","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Estimate starting parameters using PKNCA","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"","code":"# S3 method for pknca nlmixr2Est(env, ...)"},{"path":"/reference/nlmixr2Est.pknca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"env Environment nlmixr2 estimation routines. needs : - rxode2 ui object `$ui` - data fit estimation routine `$data` - control estimation routine's control options `$ui` ... arguments provided `nlmixr2Est()` provided flexibility currently used inside nlmixr","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"model updated starting parameters.  model new element named \"nca\" available includes PKNCA results used calculation.","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Parameters estimated follows: ka 4 half-lives Tmax higher 3:  log(2)/(tmax/4) vc Inverse dose-normalized Cmax cl Estimated median clearance vp,vp22- 4-fold vc, respectively default, controlled vpMult vp2Mult arguments pkncaControl q,q2 0.5- 0.25-fold cl, respectively default, controlled qMult q2Mult arguments pkncaControl bounds parameter estimates set 10% first percentile 10 times 99th percentile.  (ka, lower bound set lower 10% first percentile 0.03 upper bound modified 10 times 99th percentile.) Parameter estimation methods may changed future version.","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"","code":"nmGetDistributionMonolixLines(line)  # S3 method for rxUi nmGetDistributionMonolixLines(line)  # S3 method for norm nmGetDistributionMonolixLines(line)"},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Lines estimation monolix","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Matthew Fidler","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"","code":"nmGetDistributionNonmemLines(line)  # S3 method for rxUi nmGetDistributionNonmemLines(line)  # S3 method for norm nmGetDistributionNonmemLines(line)"},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Lines estimation nonmem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Matthew Fidler","code":""},{"path":"/reference/nonmemControl.html","id":null,"dir":"Reference","previous_headings":"","what":"NONMEM estimation control — nonmemControl","title":"NONMEM estimation control — nonmemControl","text":"NONMEM estimation control","code":""},{"path":"/reference/nonmemControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl(   est = c(\"focei\", \"imp\", \"its\", \"posthoc\"),   advanOde = c(\"advan13\", \"advan8\", \"advan6\"),   cov = c(\"r,s\", \"r\", \"s\", \"\"),   maxeval = 1e+05,   tol = 6,   atol = 12,   sstol = 6,   ssatol = 12,   sigl = 12,   sigdig = 3,   print = 1,   extension = getOption(\"babelmixr2.nmModelExtension\", \".nmctl\"),   outputExtension = getOption(\"babelmixr2.nmOutputExtension\", \".lst\"),   runCommand = getOption(\"babelmixr2.nonmem\", \"\"),   iniSigDig = 5,   protectZeros = TRUE,   muRef = TRUE,   addProp = c(\"combined2\", \"combined1\"),   rxControl = NULL,   sumProd = FALSE,   optExpression = TRUE,   calcTables = TRUE,   compress = TRUE,   ci = 0.95,   sigdigTable = NULL,   readRounding = FALSE,   readBadOpt = FALSE,   niter = 100L,   isample = 1000L,   iaccept = 0.4,   iscaleMin = 0.1,   iscaleMax = 10,   df = 4,   seed = 14456,   mapiter = 1,   mapinter = 0,   noabort = TRUE,   modelName = NULL,   muRefCovAlg = TRUE,   ... )"},{"path":"/reference/nonmemControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NONMEM estimation control — nonmemControl","text":"est NONMEM estimation method advanOde ODE solving method NONMEM cov NONMEM covariance method maxeval NONMEM's maxeval (non posthoc methods) tol NONMEM tolerance ODE solving advan atol NONMEM absolute tolerance ODE solving sstol NONMEM tolerance steady state ODE solving ssatol NONMEM absolute tolerance steady state ODE solving sigl NONMEM sigl estimation option sigdig significant digits NONMEM print print number NONMEM extension NONMEM file extensions outputExtension Extension use NONMEM output listing runCommand Command run NONMEM (typically path \"nmfe75\") function.  See details information. iniSigDig many significant digits printed $THETA $OMEGA estimate zero.  Also controls zero protection numbers protectZeros Add methods protect divide zero muRef Automatically mu-reference control stream addProp, sumProd, optExpression, calcTables, compress, ci, sigdigTable Passed nlmixr2est::foceiControl rxControl Options pass rxode2::rxControl simulations readRounding Try read NONMEM output NONMEM terminated due rounding errors readBadOpt Try read NONMEM output NONMEM terminated due apparent failed optimization niter number iterations NONMEM estimation methods isample Isample argument NONMEM  estimation method iaccept Iaccept NONMEM estimation methods iscaleMin parameter IMP NONMEM method (ISCALE_MIN) iscaleMax parameter IMP NONMEM method (ISCALE_MAX) df degrees freedom IMP method seed seed NONMEM methods mapiter number map iterations IMP method mapinter MAPINTER parameter IMP method noabort Add NOABORT option $EST modelName Model name used generate NONMEM output.  NULL try infer model name (x clear).  Otherwise use character outputs. muRefCovAlg controls algebraic expressions can   mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#`      algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old   `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model.  Therefore, default `TRUE`. ... optional genRxControl argument controlling automatic rxControl generation.","code":""},{"path":"/reference/nonmemControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NONMEM estimation control — nonmemControl","text":"babelmixr2 control option generating NONMEM control stream reading back babelmixr2/nlmixr2","code":""},{"path":"/reference/nonmemControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NONMEM estimation control — nonmemControl","text":"runCommand given string, called system() command like: runCommand controlFile outputFile. example, runCommand=\"'/path//nmfe75'\" command line used look like following: '/path//nmfe75' one.cmt.nmctl one.cmt.lst runCommand given function, called FUN(ctl, directory, ui) run NONMEM.  allows run NONMEM way may need, long can write R.  babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting NONMEM.","code":""},{"path":"/reference/nonmemControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"NONMEM estimation control — nonmemControl","text":"Matthew L. Fidler","code":""},{"path":"/reference/nonmemControl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl() #> $est #> [1] \"focei\" #>  #> $cov #> [1] \"r,s\" #>  #> $advanOde #> [1] \"advan13\" #>  #> $maxeval #> [1] 1e+05 #>  #> $print #> [1] 1 #>  #> $noabort #> [1] TRUE #>  #> $iniSigDig #> [1] 5 #>  #> $tol #> [1] 6 #>  #> $atol #> [1] 12 #>  #> $sstol #> [1] 6 #>  #> $ssatol #> [1] 12 #>  #> $sigl #> [1] 12 #>  #> $muRef #> [1] TRUE #>  #> $sigdig #> [1] 3 #>  #> $protectZeros #> [1] TRUE #>  #> $runCommand #> [1] \"\" #>  #> $outputExtension #> [1] \".lst\" #>  #> $addProp #> [1] \"combined2\" #>  #> $rxControl #> $scale #> NULL #>  #> $method #> liblsoda  #>        2  #>  #> $atol #> [1] 1e-12 #>  #> $rtol #> [1] 1e-06 #>  #> $maxsteps #> [1] 70000 #>  #> $hmin #> [1] 0 #>  #> $hmax #> [1] NA #>  #> $hini #> [1] 0 #>  #> $maxordn #> [1] 12 #>  #> $maxords #> [1] 5 #>  #> $covsInterpolation #> nocb  #>    2  #>  #> $addCov #> [1] TRUE #>  #> $returnType #> rxSolve  #>       0  #>  #> $sigma #> NULL #>  #> $sigmaDf #> NULL #>  #> $nCoresRV #> [1] 1 #>  #> $sigmaIsChol #> [1] FALSE #>  #> $sigmaSeparation #> [1] \"auto\" #>  #> $sigmaXform #> identity  #>        4  #>  #> $nDisplayProgress #> [1] 10000 #>  #> $amountUnits #> [1] NA #>  #> $timeUnits #> [1] \"hours\" #>  #> $addDosing #> [1] FALSE #>  #> $stateTrim #> [1] Inf #>  #> $updateObject #> [1] FALSE #>  #> $omega #> NULL #>  #> $omegaDf #> NULL #>  #> $omegaIsChol #> [1] FALSE #>  #> $omegaSeparation #> [1] \"auto\" #>  #> $omegaXform #> variance  #>        6  #>  #> $nSub #> [1] 1 #>  #> $thetaMat #> NULL #>  #> $thetaDf #> NULL #>  #> $thetaIsChol #> [1] FALSE #>  #> $nStud #> [1] 1 #>  #> $dfSub #> [1] 0 #>  #> $dfObs #> [1] 0 #>  #> $seed #> NULL #>  #> $nsim #> NULL #>  #> $minSS #> [1] 10 #>  #> $maxSS #> [1] 1000 #>  #> $strictSS #> [1] 1 #>  #> $infSSstep #> [1] 12 #>  #> $istateReset #> [1] TRUE #>  #> $subsetNonmem #> [1] TRUE #>  #> $hmaxSd #> [1] 0 #>  #> $maxAtolRtolFactor #> [1] 0.1 #>  #> $from #> NULL #>  #> $to #> NULL #>  #> $by #> NULL #>  #> $length.out #> NULL #>  #> $iCov #> NULL #>  #> $keep #> NULL #>  #> $keepF #> character(0) #>  #> $drop #> NULL #>  #> $warnDrop #> [1] TRUE #>  #> $omegaLower #> [1] -Inf #>  #> $omegaUpper #> [1] Inf #>  #> $sigmaLower #> [1] -Inf #>  #> $sigmaUpper #> [1] Inf #>  #> $thetaLower #> [1] -Inf #>  #> $thetaUpper #> [1] Inf #>  #> $indLinPhiM #> [1] 0 #>  #> $indLinPhiTol #> [1] 1e-07 #>  #> $indLinMatExpType #> expokit  #>       2  #>  #> $indLinMatExpOrder #> [1] 6 #>  #> $idFactor #> [1] TRUE #>  #> $mxhnil #> [1] 0 #>  #> $hmxi #> [1] 0 #>  #> $warnIdSort #> [1] TRUE #>  #> $ssAtol #> [1] 1e-12 #>  #> $ssRtol #> [1] 1e-06 #>  #> $safeZero #> [1] 0 #>  #> $sumType #> pairwise  #>        1  #>  #> $prodType #> long double  #>           1  #>  #> $sensType #> advan  #>     4  #>  #> $linDiff #>    tlag       f    rate     dur   tlag2      f2   rate2    dur2  #> 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05  #>  #> $linDiffCentral #>  tlag     f  rate   dur tlag2    f2 rate2  dur2  #>  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  #>  #> $resample #> NULL #>  #> $resampleID #> [1] TRUE #>  #> $maxwhile #> [1] 100000 #>  #> $cores #> [1] 0 #>  #> $atolSens #> [1] 1e-08 #>  #> $rtolSens #> [1] 1e-06 #>  #> $ssAtolSens #> [1] 1e-08 #>  #> $ssRtolSens #> [1] 1e-06 #>  #> $simVariability #> [1] NA #>  #> $nLlikAlloc #> NULL #>  #> $useStdPow #> [1] 0 #>  #> $naTimeHandle #> ignore  #>      1  #>  #> $addlKeepsCov #> [1] FALSE #>  #> $addlDropSs #> [1] TRUE #>  #> $ssAtDoseTime #> [1] TRUE #>  #> $ss2cancelAllPending #> [1] FALSE #>  #> $.zeros #> NULL #>  #> attr(,\"class\") #> [1] \"rxControl\" #>  #> $sumProd #> [1] FALSE #>  #> $optExpression #> [1] TRUE #>  #> $calcTables #> [1] TRUE #>  #> $compress #> [1] TRUE #>  #> $ci #> [1] 0.95 #>  #> $sigdigTable #> NULL #>  #> $readRounding #> [1] FALSE #>  #> $readBadOpt #> [1] FALSE #>  #> $genRxControl #> [1] TRUE #>  #> $niter #> [1] 100 #>  #> $isample #> [1] 1000 #>  #> $iaccept #> [1] 0.4 #>  #> $iscaleMin #> [1] 0.1 #>  #> $iscaleMax #> [1] 10 #>  #> $df #> [1] 4 #>  #> $seed #> [1] 14456 #>  #> $mapiter #> [1] 1 #>  #> $modelName #> NULL #>  #> $muRefCovAlg #> [1] TRUE #>  #> attr(,\"class\") #> [1] \"nonmemControl\""},{"path":"/reference/pkncaControl.html","id":null,"dir":"Reference","previous_headings":"","what":"PKNCA estimation control — pkncaControl","title":"PKNCA estimation control — pkncaControl","text":"PKNCA estimation control","code":""},{"path":"/reference/pkncaControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PKNCA estimation control — pkncaControl","text":"","code":"pkncaControl(   concu = NA_character_,   doseu = NA_character_,   timeu = NA_character_,   volumeu = NA_character_,   vpMult = 2,   qMult = 1/2,   vp2Mult = 4,   q2Mult = 1/4,   dvParam = \"cp\",   groups = character(),   sparse = FALSE,   ncaData = NULL,   ncaResults = NULL,   rxControl = rxode2::rxControl() )"},{"path":"/reference/pkncaControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PKNCA estimation control — pkncaControl","text":"concu, doseu, timeu concentration, dose, time units source data (passed PKNCA::pknca_units_table()). volumeu compartment volume model (NULL, simplified units source data used) vpMult, qMult, vp2Mult, q2Mult Multipliers vc cl provide initial estimates vp, q, vp2, q2 dvParam parameter name model modified concentration unit conversions.  must assigned line , separate residual error model line. groups Grouping columns NCA summaries group (required sparse = TRUE) sparse concentration-time data sparse PK (commonly used small nonclinical species terminal difficult sampling) dense PK (commonly used clinical studies larger nonclinical species)? ncaData Data use calculating NCA parameters.  Typical use subset original data informative NCA. ncaResults Already computed NCA results (PKNCAresults object) bypass automatic calculations.  least following parameters must calculated NCA: tmax, cmax.dn, cl.last rxControl Control options sent rxode2::rxControl()","code":""},{"path":"/reference/pkncaControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PKNCA estimation control — pkncaControl","text":"list parameters","code":""},{"path":"/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. nlmixr2est getValidNlmixrCtl, nlmixr2Est, nmObjGetControl, nmObjGetFoceiControl, nmObjHandleControlObject nonmem2rx .nonmem2rx, nonmem2rx rxode2 .minfo, rxModelVars, rxUiGet","code":""},{"path":"/reference/rxToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to monolix syntax — rxToMonolix","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Convert RxODE syntax monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"","code":"rxToMonolix(x, ui)"},{"path":"/reference/rxToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Matthew Fidler","code":""},{"path":"/reference/rxToNonmem.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Convert RxODE syntax NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"","code":"rxToNonmem(x, ui)"},{"path":"/reference/rxToNonmem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToNonmem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Matthew Fidler","code":""},{"path":"/reference/simplifyUnit.html","id":null,"dir":"Reference","previous_headings":"","what":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"Simplify units removing repeated units numerator denominator","code":""},{"path":"/reference/simplifyUnit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(numerator = \"\", denominator = \"\")"},{"path":"/reference/simplifyUnit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"numerator numerator units (whole unit specification) denominator denominator units (NULL numerator whole unit specification)","code":""},{"path":"/reference/simplifyUnit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"units specified units numerator denominator cancelled.","code":""},{"path":"/reference/simplifyUnit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"NA \"\" numerator denominator considered unitless.","code":""},{"path":[]},{"path":"/reference/simplifyUnit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(\"kg\", \"kg/mL\") #> [1] \"mL\" # units that don't match exactly are not cancelled simplifyUnit(\"kg\", \"g/mL\") #> [1] \"kg*mL/g\""},{"path":"/news/index.html","id":"babelmixr2-012","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.2","title":"babelmixr2 0.1.2","text":"Handle algebraic mu expressions PKNCA controller now contains rxControl since used translation options revision load pruned ui model query compartment properties (.e. bioavailability, lag time, etc) writing NONMEM model. fix issues PK block define variables larger calculated variable can used model instead. nonmem2rx different lst file, long nonmem2rx::nminfo(file) works, successful conversion nlmixr2 fit object occur. Fix save parameter history $parHistData accommodate changes focei’s output ($parHist now derived). Changed solving options match new steady state options rxode2 NONMEM implements . Also changed iwres model account rxerr. instead err. updated rxode2 well.","code":""},{"path":"/news/index.html","id":"babelmixr2-011","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.1","title":"babelmixr2 0.1.1","text":"CRAN release: 2023-05-27 Add new method .nlmixr2 convert nonmem2rx methods nlmixr fits Dropped pmxTools favor nonmem2rx conserve methods","code":""},{"path":"/news/index.html","id":"babelmixr2-010","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.0","title":"babelmixr2 0.1.0","text":"CRAN release: 2022-10-28 Babelmixr support “monolix”, “nonmem”, “pknca” methods release. Added NEWS.md file track changes package.","code":""}]
+[{"path":"/articles/new-estimation.html","id":"create-a-nlmixr2est-method","dir":"Articles","previous_headings":"","what":"Create a nlmixr2Est() method","title":"Creating a New Estimation Method","text":"method input environment nlmixr2est UI object (see ?nlmixr2Est). output fit object.","code":""},{"path":"/articles/new-estimation.html","id":"create-a-control-method","dir":"Articles","previous_headings":"","what":"Create a control method","title":"Creating a New Estimation Method","text":"control method gives access controls required estimation.","code":""},{"path":"/articles/running-monlix.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-monolix-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run Monolix from a nlmixr2 model","title":"Running Monolix","text":"use Monolix nlmixr2, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run Monolix. setup lixoftConnectors package Monolix, setup needed. Instead run Monolix command line grid processing (example) can figure command run Monolix (often useful use full command path set options, ie options(\"babelmixr2.monolix\"=\"monolix\") use monolixControl(runCommand=\"monolix\"). needed, prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-monlix.html","id":"step-1-run-a-nlmixr2-in-monolix","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in Monolix","title":"Running Monolix","text":"Lets take classic warfarin example. model use nlmixr2 vignettes : monolix run, can run nlmixr2 model using Monolix new estimation method: fit issues informational tidbit - monolix parameter history needs exported charts, please export charts automatically generated well lixoftConnectors package generated recent version Monolix. don’t information important parameter history plots imported see plots. Just like NONMEM translation, monolixControl() modelName helps control output directory Monolix (specified babelmixr2 tries guess based model name based input). Printing nlmixr2 fit see: particular interest comparison Monolix predictions nlmixr predictions. case, believe also imply models predicting thing. Note model predictions close NONMEM Monolix use lsoda ODE solver. Hence small deviation expected, still gives validated Monolix model.","code":"pk.turnover.emax3 <- function() {   ini({     tktr <- log(1)     tka <- log(1)     tcl <- log(0.1)     tv <- log(10)     ##     eta.ktr ~ 1     eta.ka ~ 1     eta.cl ~ 2     eta.v ~ 1     prop.err <- 0.1     pkadd.err <- 0.1     ##     temax <- logit(0.8)     tec50 <- log(0.5)     tkout <- log(0.05)     te0 <- log(100)     ##     eta.emax ~ .5     eta.ec50  ~ .5     eta.kout ~ .5     eta.e0 ~ .5     ##     pdadd.err <- 10   })   model({     ktr <- exp(tktr + eta.ktr)     ka <- exp(tka + eta.ka)     cl <- exp(tcl + eta.cl)     v <- exp(tv + eta.v)     emax = expit(temax+eta.emax)     ec50 =  exp(tec50 + eta.ec50)     kout = exp(tkout + eta.kout)     e0 = exp(te0 + eta.e0)     ##     DCP = center/v     PD=1-emax*DCP/(ec50+DCP)     ##     effect(0) = e0     kin = e0*kout     ##     d/dt(depot) = -ktr * depot     d/dt(gut) =  ktr * depot -ka * gut     d/dt(center) =  ka * gut - cl / v * center     d/dt(effect) = kin*PD -kout*effect     ##     cp = center / v     cp ~ prop(prop.err) + add(pkadd.err)     effect ~ add(pdadd.err) | pca   }) } fit <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"monolix\",                               monolixControl(modelName=\"pk.turnover.emax3\")) #> ℹ assuming monolix is running because 'pk.turnover.emax3-monolix.txt' is present #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> ℹ monolix parameter history needs exported charts, please export charts fit #> ── nlmixr² monolix ver 2021R1 ── #>  #>              OBJF      AIC      BIC Log-likelihood Condition#(Cov) #> monolix  1522.704 2448.398 2527.819      -1205.199        2203.836 #>          Condition#(Cor) #> monolix         2.697324 #>  #> ── Time (sec fit$time): ── #>  #>            setup table compress    other #> elapsed 0.001851 0.093    0.006 3.802149 #>  #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #>  #>              Est.     SE     %RSE Back-transformed(95%CI) BSV(CV% or SD) #> tktr        0.218  0.179       82      1.24 (0.876, 1.77)           84.0 #> tka       0.00533  0.117 2.19e+03        1.01 (0.8, 1.26)           48.6 #> tcl         -2.01 0.0518     2.58    0.135 (0.122, 0.149)           28.5 #> tv           2.04 0.0438     2.14       7.73 (7.09, 8.42)           22.6 #> prop.err   0.0986                                  0.0986                #> pkadd.err   0.533                                   0.533                #> temax        4.46  0.527     11.8    0.989 (0.969, 0.996)          0.380 #> tec50      0.0786 0.0889      113      1.08 (0.909, 1.29)           47.8 #> tkout       -2.94 0.0261    0.888  0.053 (0.0503, 0.0558)           7.87 #> te0          4.57 0.0114    0.249       96.7 (94.5, 98.9)           5.08 #> pdadd.err    3.79                                    3.79                #>           Shrink(SD)% #> tktr           47.9%  #> tka            48.9%  #> tcl            1.25%  #> tv             6.09%  #> prop.err              #> pkadd.err             #> temax          91.9%  #> tec50          6.29%  #> tkout          36.6%  #> te0            19.9%  #> pdadd.err             #>   #>   Covariance Type (fit$covMethod): MonolixLin #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink  #>   Censoring (fit$censInformation): No censoring #>   Minimization message (fit$message):   #>     IPRED relative difference compared to Monolix IPRED: 0.09%; 95% percentile: (0.01%,0.49%); rtol=0.000941 #>     PRED relative difference compared to Monolix PRED: 0.04%; 95% percentile: (0%,0.2%); rtol=0.000428 #>     IPRED absolute difference compared to Monolix IPRED: atol=0.00911; 95% percentile: (0.000493, 0.0928) #>     PRED absolute difference compared to Monolix PRED: atol=0.000428; 95% percentile: (3.14e-07, 0.203) #>     monolix model: 'pk.turnover.emax3-monolix.mlxtran'  #>  #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 483 × 35 #>   ID     TIME CMT      DV  PRED   RES IPRED   IRES  IWRES eta.ktr eta.ka eta.cl #>   <fct> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl> #> 1 1       0.5 cp      0    1.40 -1.40 0.500 -0.500 -0.934  -0.638 -0.447  0.689 #> 2 1       1   cp      1.9  3.94 -2.04 1.62   0.284  0.511  -0.638 -0.447  0.689 #> 3 1       2   cp      3.3  8.30 -5.00 4.29  -0.987 -1.45   -0.638 -0.447  0.689 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v <dbl>, eta.emax <dbl>, eta.ec50 <dbl>, #> #   eta.kout <dbl>, eta.e0 <dbl>, cp <dbl>, depot <dbl>, gut <dbl>, #> #   center <dbl>, effect <dbl>, ktr <dbl>, ka <dbl>, cl <dbl>, v <dbl>, #> #   emax <dbl>, ec50 <dbl>, kout <dbl>, e0 <dbl>, DCP <dbl>, PD <dbl>, #> #   kin <dbl>, tad <dbl>, dosenum <dbl>"},{"path":"/articles/running-monlix.html","id":"optional-step-2-add-conditional-weighted-residualsfocei-objf-to-monolix","dir":"Articles","previous_headings":"","what":"Optional Step 2: Add conditional weighted residuals/focei objf to Monolix","title":"Running Monolix","text":"case NONMEM, gives things available Monolix, like adding conditional weighted residuals: add nlmixr’s CWRES well adding nlmixr2 FOCEi objective function now objective function compared based assumptions, compare performance Monolix NONMEM based objective function. fair, objective function values must always used caution. model performs predicts data far valuable.","code":"fit <- addCwres(fit) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done"},{"path":"/articles/running-monlix.html","id":"optional-step-3-use-nlmixr2-for-vpc-reporting-etc-","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 for vpc, reporting, etc.","title":"Running Monolix","text":"Also since nlmixr2 object easy perform VPC :","code":"v1s <- vpcPlot(fit, show=list(obs_dv=TRUE), scales=\"free_y\") +   ylab(\"Warfarin Cp [mg/L] or PCA\") +   xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’  v2s <- vpcPlot(fit, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") +   ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") +   xlab(\"Time [h]\")  v1s v2s"},{"path":"/articles/running-monlix.html","id":"notes-about-monolix-data-translation","dir":"Articles","previous_headings":"","what":"Notes about Monolix data translation","title":"Running Monolix","text":"input dataset expected compatible rxode2 nlmixr2. dataset converted Monolix format: combination CMT Dose type creates unique ADM variable. ADM definition saved monolix model file babelmixr2 creates macro describing compartment, ie compartment(cmt=#, amount=stateName) babelmixr2 also creates macro type dosing: Bolus/infusion uses depot() adds modeled lag time (Tlag) bioavailability (p) specified Modeled rate uses depot() Tk0=amtDose/rate. babelmixr2 also adds modeled lag time (Tlag) bioavailability (p) specified Modeled duration uses depot() Tk0=dur, also add adds modeled lag time (Tlag) bioavailability (p) specified Turning compartment uses empty macro","code":""},{"path":"/articles/running-nonmem.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-nonmem-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run NONMEM from a nlmixr2 model","title":"Running NONMEM with nlmixr2","text":"use NONMEM nlmixr, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run NONMEM. many cases easy; simply figure command run NONMEM (often useful use full command path). can set options(\"babelmixr2.nonmem\"=\"nmfe743\") use nonmemControl(runCommand=\"nmfe743\"). prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-nonmem.html","id":"step-1-run-a-nlmixr2-in-nonmem","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in NONMEM","title":"Running NONMEM with nlmixr2","text":"Lets take classic warfarin example start comparison. model use nlmixr2 vignettes : Now can run nlmixr2 model using NONMEM simply can run directly: way run ordinary nlmixr2 model, simply new estimation method \"nonmem\" new controller (nonmemControl()) setup options estimation. options nonmemControl() modelName helps control output directory NONMEM (specified babelmixr2 tries guess based model name based input). try , see NONMEM fails rounding errors. standard approach changing sigdig, sigl, tol etc, get successful NONMEM model convergence, course supported. babelmixr2 can .","code":"library(babelmixr2) pk.turnover.emax3 <- function() {   ini({     tktr <- log(1)     tka <- log(1)     tcl <- log(0.1)     tv <- log(10)     ##     eta.ktr ~ 1     eta.ka ~ 1     eta.cl ~ 2     eta.v ~ 1     prop.err <- 0.1     pkadd.err <- 0.1     ##     temax <- logit(0.8)     tec50 <- log(0.5)     tkout <- log(0.05)     te0 <- log(100)     ##     eta.emax ~ .5     eta.ec50  ~ .5     eta.kout ~ .5     eta.e0 ~ .5     ##     pdadd.err <- 10   })   model({     ktr <- exp(tktr + eta.ktr)     ka <- exp(tka + eta.ka)     cl <- exp(tcl + eta.cl)     v <- exp(tv + eta.v)     emax = expit(temax+eta.emax)     ec50 =  exp(tec50 + eta.ec50)     kout = exp(tkout + eta.kout)     e0 = exp(te0 + eta.e0)     ##     DCP = center/v     PD=1-emax*DCP/(ec50+DCP)     ##     effect(0) = e0     kin = e0*kout     ##     d/dt(depot) = -ktr * depot     d/dt(gut) =  ktr * depot -ka * gut     d/dt(center) =  ka * gut - cl / v * center     d/dt(effect) = kin*PD -kout*effect     ##     cp = center / v     cp ~ prop(prop.err) + add(pkadd.err)     effect ~ add(pdadd.err) | pca   }) } try(nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\",            nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax3\")),     silent=TRUE) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #>  #>  #>  WARNINGS AND ERRORS (IF ANY) FOR PROBLEM    1 #>  #>  (WARNING  2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #>  #>  #> 0MINIMIZATION TERMINATED #>  DUE TO ROUNDING ERRORS (ERROR=134) #>  NO. OF FUNCTION EVALUATIONS USED:     1088 #>  NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #>  #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> → terminated with rounding errors, can force nlmixr2/rxode2 to read with nonmemControl(readRounding=TRUE) #> Error : nonmem minimization not successful"},{"path":"/articles/running-nonmem.html","id":"optional-step-2-recover-a-failed-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 2: Recover a failed NONMEM run","title":"Running NONMEM with nlmixr2","text":"One approaches ignore rounding errors occurred read nlmixr2 anyway: may see work happening expected need already completed model. reading NONMEM model, babelmixr2 grabs: NONMEM’s objective function value NONMEM’s covariance (available) NONMEM’s optimization history NONMEM’s final parameter estimates (including ETAs) NONMEM’s PRED IPRED values (validation purposes) used solve ODEs came nlmixr2 optimization procedure. means can compare IPRED PRED values nlmixr2/rxode2 know immediately model validates. similar procedure Kyle Baron advocates validating NONMEM model mrgsolve model (see https://mrgsolve.org/blog/posts/2022-05-validate-translation/ https://mrgsolve.org/blog/posts/2023-update-validation.html), advantage method need simply write one model get validated roxde2/nlmixr2 model. case can see validation print fit object: shows preds ipreds match NONMEM nlmixr2 quite well.","code":"# Can still load the model to get information (possibly pipe) and create a new model f <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\",             nonmemControl(readRounding=TRUE, modelName=\"pk.turnover.emax3\")) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.1.0 using 2 threads (see ?getRxThreads) #>   no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 5536 print(f) #> ── nlmixr² nonmem ver 7.4.3 ── #>  #>                 OBJF      AIC      BIC Log-likelihood Condition#(Cov) #> nonmem focei 1326.91 2252.605 2332.025      -1107.302              NA #>              Condition#(Cor) #> nonmem focei              NA #>  #> ── Time (sec $time): ── #>  #>            setup table compress NONMEM #> elapsed 0.023798 0.055     0.01 320.27 #>  #> ── Population Parameters ($parFixed or $parFixedDf): ── #>  #>                Est. Back-transformed BSV(CV% or SD) Shrink(SD)% #> tktr       6.24e-07                1           86.5      59.8%  #> tka       -3.01e-06                1           86.5      59.8%  #> tcl              -2            0.135           28.6      1.34%  #> tv             2.05             7.78           22.8      6.44%  #> prop.err     0.0986           0.0986                            #> pkadd.err     0.512            0.512                            #> temax          6.42            0.998        0.00707      100.%  #> tec50         0.141             1.15           45.0      6.06%  #> tkout         -2.95           0.0522           9.16      32.4%  #> te0            4.57             96.6           5.24      18.1%  #> pdadd.err      3.72             3.72                            #>   #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink  #>   Information about run found ($runInfo): #>    • NONMEM terminated due to rounding errors, but reading into nlmixr2/rxode2 anyway  #>   Censoring ($censInformation): No censoring #>   Minimization message ($message):   #>      #>  #>  WARNINGS AND ERRORS (IF ANY) FOR PROBLEM    1 #>  #>  (WARNING  2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #>  #>      #> 0MINIMIZATION TERMINATED #>  DUE TO ROUNDING ERRORS (ERROR=134) #>  NO. OF FUNCTION EVALUATIONS USED:     1088 #>  NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #>  #>     IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.36e-06 #>     PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.08e-06 #>     IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.53e-06, 0.000502); atol=7.15e-05 #>     PRED absolute difference compared to Nonmem PRED: 95% percentile: (3.79e-07,0.00321); atol=6.08e-06 #>     there are solving errors during optimization (see '$prderr') #>     nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl'  #>  #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 483 × 35 #>   ID     TIME CMT      DV  PRED   RES IPRED   IRES  IWRES eta.ktr eta.ka eta.cl #>   <fct> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl> #> 1 1       0.5 cp      0    1.16 -1.16 0.444 -0.444 -0.864  -0.506 -0.506  0.699 #> 2 1       1   cp      1.9  3.37 -1.47 1.45   0.446  0.840  -0.506 -0.506  0.699 #> 3 1       2   cp      3.3  7.51 -4.21 3.96  -0.660 -1.03   -0.506 -0.506  0.699 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v <dbl>, eta.emax <dbl>, eta.ec50 <dbl>, #> #   eta.kout <dbl>, eta.e0 <dbl>, cp <dbl>, depot <dbl>, gut <dbl>, #> #   center <dbl>, effect <dbl>, ktr <dbl>, ka <dbl>, cl <dbl>, v <dbl>, #> #   emax <dbl>, ec50 <dbl>, kout <dbl>, e0 <dbl>, DCP <dbl>, PD <dbl>, #> #   kin <dbl>, tad <dbl>, dosenum <dbl>"},{"path":"/articles/running-nonmem.html","id":"optional-step-3-use-nlmixr2-to-help-understand-why-nonmem-failed","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 to help understand why NONMEM failed","title":"Running NONMEM with nlmixr2","text":"Since nlmixr2 fit, can interesting things fit couldn’t NONMEM even another translator. example, wanted add covariance step can getVarCov(): nlmixr2 generous constitutes covariance step. r,s covariance matrix “” successful covariance step focei, system fall back methods necessary. covariance matrix r,s, regarded caution, can still give us clues things working NONMEM. examining fit, can see shrinkage high temax, tktr tka, dropped, making things likely converge NONMEM.","code":"getVarCov(f) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:05 #> Warning in foceiFitCpp_(.ret): using R matrix to calculate covariance, can #> check sandwich or S matrix with $covRS and $covS #> Warning in foceiFitCpp_(.ret): gradient problems with covariance; see #> $scaleInfo #> → compress origData in nlmixr2 object, save 27560 #> Updated original fit object f #>                tktr           tka           tcl           tv         temax #> tktr   1.821078e-02 -1.512272e-02 -2.550343e-05 3.216116e-04  0.0015410335 #> tka   -1.512272e-02  1.815814e-02 -1.992622e-05 3.175474e-04  0.0010345827 #> tcl   -2.550343e-05 -1.992622e-05  2.477225e-04 1.181659e-05 -0.0008009162 #> tv     3.216116e-04  3.175474e-04  1.181659e-05 3.184497e-04  0.0010914727 #> temax  1.541033e-03  1.034583e-03 -8.009162e-04 1.091473e-03  7.5815740647 #> tec50  1.410716e-04  1.273505e-04 -3.578298e-04 1.229707e-04  0.0483191718 #> tkout  1.023011e-04  1.011022e-04 -9.757882e-05 1.188260e-04 -0.0189641465 #> te0    1.310259e-05  1.399880e-05 -9.833068e-06 1.232683e-05 -0.0004365713 #>               tec50         tkout           te0 #> tktr   0.0001410716  1.023011e-04  1.310259e-05 #> tka    0.0001273505  1.011022e-04  1.399880e-05 #> tcl   -0.0003578298 -9.757882e-05 -9.833068e-06 #> tv     0.0001229707  1.188260e-04  1.232683e-05 #> temax  0.0483191718 -1.896415e-02 -4.365713e-04 #> tec50  0.0018345990  1.544065e-04 -1.357629e-04 #> tkout  0.0001544065  6.320302e-04  5.220487e-05 #> te0   -0.0001357629  5.220487e-05  8.843897e-05"},{"path":"/articles/running-nonmem.html","id":"optional-step-4-use-model-piping-to-get-a-successful-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 4: Use model piping to get a successful NONMEM run","title":"Running NONMEM with nlmixr2","text":"use model piping remove parameters, new run start last model’s best estimates (saving bunch model development time). case, specify output directory pk.turnover.emax4 control get following: can see NONMEM run now successful validates rxode2 model : One thing emphasize: unlike translators, know immediately translation model validate. Hence can start process confidence - know immediately something wrong. related converting NONMEM nlmixr2 fit. Since nlmixr2 object easy perform VPC (true NONMEM models):","code":"f2 <- f %>% model(ktr <- exp(tktr)) %>%   model(ka <- exp(tka)) %>%   model(emax = expit(temax)) %>%   nlmixr(data=nlmixr2data::warfarin, est=\"nonmem\",          control=nonmemControl(readRounding=FALSE,                                modelName=\"pk.turnover.emax4\")) #> ! remove between subject variability `eta.ktr` #> ! remove between subject variability `eta.ka` #> ! remove between subject variability `eta.emax` #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|==== #> ====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 8800 f2 #> ── nlmixr² nonmem ver 7.4.3 ── #>  #>                  OBJF      AIC      BIC Log-likelihood Condition#(Cov) #> nonmem focei 1418.923 2338.618 2405.498      -1153.309    1.852796e+16 #>              Condition#(Cor) #> nonmem focei        18934770 #>  #> ── Time (sec f2$time): ── #>  #>           setup table compress NONMEM #> elapsed 0.00193 0.053     0.01 505.59 #>  #> ── Population Parameters (f2$parFixed or f2$parFixedDf): ── #>  #>                Est.       SE     %RSE     Back-transformed(95%CI) BSV(CV%) #> tktr       6.24e-07 9.05e-05 1.45e+04                    1 (1, 1)          #> tka       -3.57e-06 0.000153 4.29e+03                    1 (1, 1)          #> tcl           -1.99   0.0639      3.2         0.136 (0.12, 0.154)     27.6 #> tv             2.05     2.66      130      7.76 (0.042, 1.44e+03)     23.6 #> prop.err      0.161                                         0.161          #> pkadd.err     0.571                                         0.571          #> temax          9.98     4.96     49.7                1 (0.565, 1)          #> tec50         0.131     1.61 1.23e+03         1.14 (0.0489, 26.6)     43.6 #> tkout         -2.96     28.3      954 0.0517 (4.63e-26, 5.77e+22)     8.63 #> te0            4.57    0.411        9            96.7 (43.2, 217)     5.19 #> pdadd.err      3.59                                          3.59          #>           Shrink(SD)% #> tktr                  #> tka                   #> tcl            3.19%  #> tv             10.7%  #> prop.err              #> pkadd.err             #> temax                 #> tec50          7.12%  #> tkout          33.8%  #> te0            17.2%  #> pdadd.err             #>   #>   Covariance Type (f2$covMethod): nonmem.r,s #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance (f2$omega) or correlation (f2$omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in f2$shrink  #>   Censoring (f2$censInformation): No censoring #>   Minimization message (f2$message):   #>      #>  #>  WARNINGS AND ERRORS (IF ANY) FOR PROBLEM    1 #>  #>  (WARNING  2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #>  #>      #> 0MINIMIZATION SUCCESSFUL #>  HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. #>  REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY #>  AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. #>  NO. OF FUNCTION EVALUATIONS USED:     2391 #>  NO. OF SIG. DIGITS IN FINAL EST.:  4.1 #>  #>     IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.85e-06 #>     PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.45e-06 #>     IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.89e-06, 0.000506); atol=7.19e-05 #>     PRED absolute difference compared to Nonmem PRED: 95% percentile: (5.14e-07,0.00318); atol=6.45e-06 #>     nonmem model: 'pk.turnover.emax4-nonmem/pk.turnover.emax4.nmctl'  #>  #> ── Fit Data (object f2 is a modified tibble): ── #> # A tibble: 483 × 32 #>   ID     TIME CMT      DV  PRED   RES IPRED   IRES IWRES eta.cl eta.v eta.ec50 #>   <fct> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl>    <dbl> #> 1 1       0.5 cp      0    1.16 -1.16 0.920 -0.920 -1.56  0.689 0.228    0.160 #> 2 1       1   cp      1.9  3.38 -1.48 2.68  -0.780 -1.09  0.689 0.228    0.160 #> 3 1       2   cp      3.3  7.53 -4.23 5.94  -2.64  -2.36  0.689 0.228    0.160 #> # ℹ 480 more rows #> # ℹ 20 more variables: eta.kout <dbl>, eta.e0 <dbl>, cp <dbl>, depot <dbl>, #> #   gut <dbl>, center <dbl>, effect <dbl>, ktr <dbl>, ka <dbl>, cl <dbl>, #> #   v <dbl>, emax <dbl>, ec50 <dbl>, kout <dbl>, e0 <dbl>, DCP <dbl>, PD <dbl>, #> #   kin <dbl>, tad <dbl>, dosenum <dbl> v1s <- vpcPlot(f2, show=list(obs_dv=TRUE), scales=\"free_y\") +   ylab(\"Warfarin Cp [mg/L] or PCA\") +   xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’  v2s <- vpcPlot(f2, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") +   ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") +   xlab(\"Time [h]\")   library()  v1s v2s"},{"path":"/articles/running-pknca.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Obtain initial estimates and unit conversions with PKNCA","text":"Initial estimates compartmental population PK model can obtained using babelmixr2 \"pknca\" estimation method. Also, central compartment scaling factor can auto-generated based units dosing, concentration measurement, desired volume distribution units, time. need perform NCA analysis hand; \"pknca\" estimation method perform NCA analysis using PKNCA package automatically. methods used converting NCA calculations parameter estimates described help nlmixr2Est.pknca().","code":""},{"path":"/articles/running-pknca.html","id":"initial-example","dir":"Articles","previous_headings":"","what":"Initial example","title":"Obtain initial estimates and unit conversions with PKNCA","text":"Initial model setup nlmixr2 model. must load babelmixr2 library nlmixr() function recognizes est = \"pknca\". use PKNCA get initial estimates, use est = \"pknca\" instead one nlmixr2 estimation methods. unit conversions, provide units control = pkncaControl() argument. Unit conversions supported units can automatically converted; mass/volume can converted mass/volume ratio, mass molar molar mass single mass--molar conversion factor. Now, prepared model updated initial estimates NCA results embedded. can see new model PKNCA estimates looking prepared$ui (model interpreted rxode2) prepared$nca (PKNCAresults object). Note new model, fixed effect initial estimates changed original values. residual error -subject variability unchanged. updated model, can perform estimation new model object, model created nlmixr2:","code":"library(babelmixr2) #> Loading required package: nlmixr2 #> Loading required package: nlmixr2data one.compartment <- function() {   ini({     tka <- log(1.57); label(\"Ka (1/hr)\")     tcl <- log(2.72); label(\"Cl (L/hr)\")     tv <- log(31.5); label(\"V (L)\")     eta.ka ~ 0.6     eta.cl ~ 0.3     eta.v ~ 0.1     add.sd <- 0.7; label(\"additive residual error (mg/L)\")   })   # and a model block with the error specification and model specification   model({     ka <- exp(tka + eta.ka)     cl <- exp(tcl + eta.cl)     vc <- exp(tv + eta.v)     d/dt(depot) <- -ka * depot     d/dt(center) <- ka * depot - cl / vc * center     cp <- center / vc     cp ~ add(add.sd)   }) } prepared <-   nlmixr2(     one.compartment,     data = theo_sd,     est = \"pknca\",     control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\")   ) #> Loading required namespace: testthat #> ℹ change initial estimate (0.89314878960486) and upper/lower bound (-3.50655789731998 to 3.72508541597241) of `tka` #> → significant model change detected #> → removed from model: '$getSplitModel' #> ℹ change initial estimate (8.41044546236311) and upper/lower bound (5.51439905878865 to 10.899462850803) of `tcl` #> ℹ change initial estimate (10.5377244826318) and upper/lower bound (7.94567233496473 to 13.1050053785005) of `tv` prepared$ui #>  ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>        tka        tcl         tv     add.sd  #>  0.8931488  8.4104455 10.5377245  0.7000000  #>  #> Omega ($omega):  #>        eta.ka eta.cl eta.v #> eta.ka    0.6    0.0   0.0 #> eta.cl    0.0    0.3   0.0 #> eta.v     0.0    0.0   0.1 #>  #> States ($state or $stateDf):  #>   Compartment Number Compartment Name #> 1                  1            depot #> 2                  2           center #>  ── μ-referencing ($muRefTable): ──   #>   theta    eta level #> 1   tka eta.ka    id #> 2   tcl eta.cl    id #> 3    tv  eta.v    id #>  #>  ── Model (Normalized Syntax): ──  #> function() { #>     ini({ #>         tka <- c(-3.50655789731998, 0.89314878960486, 3.72508541597241) #>         label(\"Ka (1/hr)\") #>         tcl <- c(5.51439905878865, 8.41044546236311, 10.899462850803) #>         label(\"Cl (L/hr)\") #>         tv <- c(7.94567233496473, 10.5377244826318, 13.1050053785005) #>         label(\"V (L)\") #>         add.sd <- c(0, 0.7) #>         label(\"additive residual error (mg/L)\") #>         eta.ka ~ 0.6 #>         eta.cl ~ 0.3 #>         eta.v ~ 0.1 #>     }) #>     model({ #>         ka <- exp(tka + eta.ka) #>         cl <- exp(tcl + eta.cl) #>         vc <- exp(tv + eta.v) #>         d/dt(depot) <- -ka * depot #>         d/dt(center) <- ka * depot - cl/vc * center #>         cp <- 1000 * center/vc #>         cp ~ add(add.sd) #>     }) #> }  knitr::knit_print(   summary(prepared$nca) ) #>  Interval Start Interval End  N AUClast (hr*ng/mL) Cmax (ng/mL) #>               0           24 12        74.6 [24.3]            . #>               0          Inf 12                  .  8.65 [17.0] #>           Tmax (hr) CL (based on AUClast) (mg/(hr*ng/mL)) #>                   .                           4.22 [23.0] #>  1.14 [0.630, 3.55]                                     . #>  Vss (based on AUClast) (mg/(ng/mL)) Half-life (hr) AUCinf,obs (hr*ng/mL) #>                          25.0 [18.5]              .                     . #>                                    .    8.18 [2.12]            115 [28.4] #>  Cmax (dose-normalized) ((ng/mL)/mg) #>                                    . #>                        0.0274 [18.1] #>  #> Caption: AUClast, Cmax, CL (based on AUClast), Vss (based on AUClast), AUCinf,obs, Cmax (dose-normalized): geometric mean and geometric coefficient of variation; Tmax: median and range; Half-life: arithmetic mean and standard deviation fit <- nlmixr(prepared, data = theo_sd, est = \"focei\", control = list(print = 0)) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.1.0 using 2 threads (see ?getRxThreads) #>   no cache: create with `rxCreateCache()` #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:00  #> done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 5952 #> → compress parHistData in nlmixr2 object, save 10632  fit #> ── nlmixr² FOCEi (outer: nlminb) ── #>  #>           OBJF      AIC      BIC Log-likelihood Condition#(Cov) Condition#(Cor) #> FOCEi 116.9548 373.5546 393.7342      -179.7773         66.5244        12.97786 #>  #> ── Time (sec fit$time): ── #>  #>            setup optimize covariance table compress    other #> elapsed 0.025541 0.498633   0.498634 0.058    0.013 7.510192 #>  #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #>  #>                             Parameter  Est.     SE  %RSE #> tka                         Ka (1/hr) 0.469  0.224  47.6 #> tcl                         Cl (L/hr)  7.92 0.0929  1.17 #> tv                              V (L)  10.4 0.0602 0.581 #> add.sd additive residual error (mg/L) 0.697              #>              Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> tka                 1.6 (1.03, 2.48)     68.0    -0.666%  #> tcl     2.75e+03 (2.29e+03, 3.3e+03)     26.1      3.95%  #> tv     3.19e+04 (2.83e+04, 3.59e+04)     15.4      14.5%  #> add.sd                         0.697                      #>   #>   Covariance Type (fit$covMethod): r,s #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink  #>   Information about run found (fit$runInfo): #>    • gradient problems with initial estimate and covariance; see $scaleInfo  #>    • last objective function was not at minimum, possible problems in optimization  #>    • ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))  #>    • initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))  #>   Censoring (fit$censInformation): No censoring #>   Minimization message (fit$message):   #>     false convergence (8)  #>   In an ODE system, false convergence may mean \"useless\" evaluations were performed. #>   See https://tinyurl.com/yyrrwkce #>   It could also mean the convergence is poor, check results before accepting fit #>   You may also try a good derivative free optimization: #>     nlmixr2(...,control=list(outerOpt=\"bobyqa\")) #>  #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 132 × 22 #>   ID     TIME    DV  PRED    RES   WRES IPRED   IRES  IWRES CPRED   CRES  CWRES #>   <fct> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl> #> 1 1      0     0.74  0     0.74   1.06   0     0.74   1.06   0     0.74   1.06  #> 2 1      0.25  2.84  3.27 -0.432 -0.234  3.84 -1.00  -1.44   3.23 -0.389 -0.185 #> 3 1      0.57  6.57  5.84  0.730  0.297  6.78 -0.215 -0.308  5.78  0.786  0.287 #> # ℹ 129 more rows #> # ℹ 10 more variables: eta.ka <dbl>, eta.cl <dbl>, eta.v <dbl>, depot <dbl>, #> #   center <dbl>, ka <dbl>, cl <dbl>, vc <dbl>, tad <dbl>, dosenum <dbl>"},{"path":"/articles/running-pknca.html","id":"give-pknca-a-different-dataset-or-a-completed-nca-analysis","dir":"Articles","previous_headings":"","what":"Give PKNCA a different dataset or a completed NCA analysis","title":"Obtain initial estimates and unit conversions with PKNCA","text":"get initial estimate, babelmixr2 automatically converts modeling dataset format needed PKNCA, NCA automatically performed using data. cases (e.g. studies sparse data), NCA may feasible. cases, can provide different dataset PKNCA compared full modeling dataset. Usually, simplest method provide single-dose, dense-sampling, dose-ranging data (.e. single-ascending dose portion first--human study) estimated. , give data PKNCA using ncaData argument pkncaControl() follows: initial estimates now based NCA calculated dNCA dataset rather full theo_sd dataset. already NCA results calculated PKNCA required parameters (“tmax”, “cmax.dn”, “cllast”), can provide instead using pkncaControl(ncaResults) argument.","code":"# Choose a subset of the full dataset for NCA dNCA <- theo_sd[theo_sd$ID <= 6, ]  preparedNcaData <-   nlmixr2(     one.compartment,     data = theo_sd,     est = \"pknca\",     control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\", ncaData = dNCA)   ) #> ℹ change initial estimate (0.929027077269762) and upper/lower bound (-3.50655789731998 to 3.32136703319919) of `tka` #> → significant model change detected #> → removed from model: '$getSplitModel' #> ℹ change initial estimate (8.3955404628088) and upper/lower bound (5.85241523541802 to 10.7637056987378) of `tcl` #> ℹ change initial estimate (10.5377244826318) and upper/lower bound (7.94370069836702 to 13.1024358787022) of `tv`  preparedNcaData$ui #>  ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>        tka        tcl         tv     add.sd  #>  0.9290271  8.3955405 10.5377245  0.7000000  #>  #> Omega ($omega):  #>        eta.ka eta.cl eta.v #> eta.ka    0.6    0.0   0.0 #> eta.cl    0.0    0.3   0.0 #> eta.v     0.0    0.0   0.1 #>  #> States ($state or $stateDf):  #>   Compartment Number Compartment Name #> 1                  1            depot #> 2                  2           center #>  ── μ-referencing ($muRefTable): ──   #>   theta    eta level #> 1   tka eta.ka    id #> 2   tcl eta.cl    id #> 3    tv  eta.v    id #>  #>  ── Model (Normalized Syntax): ──  #> function() { #>     ini({ #>         tka <- c(-3.50655789731998, 0.929027077269762, 3.32136703319919) #>         label(\"Ka (1/hr)\") #>         tcl <- c(5.85241523541802, 8.3955404628088, 10.7637056987378) #>         label(\"Cl (L/hr)\") #>         tv <- c(7.94370069836702, 10.5377244826318, 13.1024358787022) #>         label(\"V (L)\") #>         add.sd <- c(0, 0.7) #>         label(\"additive residual error (mg/L)\") #>         eta.ka ~ 0.6 #>         eta.cl ~ 0.3 #>         eta.v ~ 0.1 #>     }) #>     model({ #>         ka <- exp(tka + eta.ka) #>         cl <- exp(tcl + eta.cl) #>         vc <- exp(tv + eta.v) #>         d/dt(depot) <- -ka * depot #>         d/dt(center) <- ka * depot - cl/vc * center #>         cp <- 1000 * center/vc #>         cp ~ add(add.sd) #>     }) #> }"},{"path":"/articles/running-pknca.html","id":"model-requirements","dir":"Articles","previous_headings":"","what":"Model requirements","title":"Obtain initial estimates and unit conversions with PKNCA","text":"update initial estimates, model must parameters model() block names expected est = \"pknca\". expected names : ka vc cl vp vp2 q q2 parameter names found model() block automatically traced back initial conditions (ini() block), parameter values updated. parameter estimated log scale, updated parameter value automatically converted log scale.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthew Fidler. Author, maintainer. Bill Denney. Author. Nook Fulloption. Contributor.            goldfish art","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Fidler M, Xiong Y, Schoemaker R, Wilkins J, Trame M, Hooijmaijers R, Post T, Wang W (2023). nlmixr: Nonlinear Mixed Effects Models Population Pharmacokinetics Pharmacodynamics. R package version 0.1.2, https://CRAN.R-project.org/package=nlmixr. Fidler M, Wilkins J, Hooijmaijers R, Post T, Schoemaker R, Trame M, Xiong Y, Wang W (2019). “Nonlinear Mixed-Effects Model Development Simulation Using nlmixr Related R Open-Source Packages.” CPT: Pharmacometrics & Systems Pharmacology, 8(9), 621–633. https://doi.org/10.1002/psp4.12445. Schoemaker R, Fidler M, Laveille C, Wilkins J, Hooijmaijers R, Post T, Trame M, Xiong Y, Wang W (2019). “Performance SAEM FOCEI Algorithms Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.” CPT: Pharmacometrics & Systems Pharmacology, 8(12), 923–930. https://doi.org/10.1002/psp4.12471.","code":"@Manual{,   title = {{nlmixr}: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics},   author = {Matthew Fidler and Yuan Xiong and Rik Schoemaker and Justin Wilkins and Mirjam Trame and Richard Hooijmaijers and Teun Post and Wenping Wang},   year = {2023},   note = {R package version 0.1.2},   url = {https://CRAN.R-project.org/package=nlmixr}, } @Article{,   title = {Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages},   author = {Matthew Fidler and Justin Wilkins and Richard Hooijmaijers and Teun Post and Rik Schoemaker and Mirjam Trame and Yuan Xiong and Wenping Wang},   journal = {CPT: Pharmacometrics \\& Systems Pharmacology},   year = {2019},   volume = {8},   pages = {621--633},   number = {9},   month = {sep},   abstract = {nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.},   address = {Hoboken},   publisher = {John Wiley and Sons Inc.},   url = {https://doi.org/10.1002/psp4.12445}, } @Article{,   title = {Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr},   author = {Rik Schoemaker and Matthew Fidler and Christian Laveille and Justin Wilkins and Richard Hooijmaijers and Teun Post and Mirjam Trame and Yuan Xiong and Wenping Wang},   journal = {CPT: Pharmacometrics \\& Systems Pharmacology},   year = {2019},   volume = {8},   pages = {923--930},   number = {12},   month = {dec},   abstract = {The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.},   url = {https://doi.org/10.1002/psp4.12471}, }"},{"path":"/index.html","id":"babelmixr2","dir":"","previous_headings":"","what":"Use nlmixr2 to Interact with Open Source and Commercial Software","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"goal babelmixr2 convert nlmixr2 syntax commonly used tools.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"can install released version babelmixr2 CRAN : can install r-universe : Otherwise can always install GitHub:","code":"install.packages(\"babelmixr2\") # Download and install babelmixr2 in R install.packages('babelmixr2',                  repos = c(                    nlmixr2 = 'https://nlmixr2.r-universe.dev',                    CRAN = 'https://cloud.r-project.org'))"},{"path":"/index.html","id":"what-you-can-do-with-babelmixr2","dir":"","previous_headings":"","what":"What you can do with babelmixr2","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"Babelmixr2 can help : Running nlmixr2 model commercial nonlinear mixed effects modeling tool like NONMEM Monolix Convert NONMEM model nlmixr2 model (conjunction nonmem2rx) Calculate scaling factors automatically add initial conditions based non-compartmental analysis (using PKNCA)","code":""},{"path":"/index.html","id":"monolix-setup","dir":"","previous_headings":"","what":"Monolix Setup","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"required, can get/install R ‘lixoftConnectors’ package ‘Monolix’ installation, described following url https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/. ‘lixoftConnectors’ available, R can run ‘Monolix’ directly instead using command line.","code":""},{"path":"/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"installed, use standard interface, can obtain new initial estimates PKNCA: , can convert Monolix , can convert NONMEM ","code":"mod <-   nlmixr2(     nlmixrFun, nlmmixrData, est = \"pknca\",     control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\")   ) mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"monolix\") mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"nonmem\")"},{"path":"/reference/as.nlmixr2.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an object to a nlmixr2 fit object — as.nlmixr2","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Convert object nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"as.nlmixr2(   x,   ...,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl() )  as.nlmixr(   x,   ...,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl() )"},{"path":"/reference/as.nlmixr2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"x Object convert ... arguments table nlmixr2est::tableControl() options rxControl rxode2::rxControl() options, generally needed addl doses handled translation","code":""},{"path":"/reference/as.nlmixr2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Matthew L. Fidler","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"# \\donttest{  # First read in the model (but without residuals) mod <- nonmem2rx(system.file(\"mods/cpt/runODE032.ctl\", package=\"nonmem2rx\"),                  determineError=FALSE, lst=\".res\", save=FALSE) #> ℹ getting information from  '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> ℹ reading in xml file #> ℹ done #> ℹ reading in phi file #> ℹ done #> ℹ reading in lst file #> ℹ abbreviated list parsing #> ℹ done #> ℹ done #> ℹ splitting control stream by records #> ℹ done #> ℹ Processing record $INPUT #> ℹ Processing record $MODEL #> ℹ Processing record $THETA #> ℹ Processing record $OMEGA #> ℹ Processing record $SIGMA #> ℹ Processing record $PROBLEM #> ℹ Processing record $DATA #> ℹ Processing record $SUBROUTINES #> ℹ Processing record $PK #> ℹ Processing record $DES #> ℹ Processing record $ERROR #> ℹ Processing record $ESTIMATION #> ℹ Ignore record $ESTIMATION #> ℹ Processing record $COVARIANCE #> ℹ Ignore record $COVARIANCE #> ℹ Processing record $TABLE #> ℹ change initial estimate of `theta1` to `1.37034036528946` #> ℹ change initial estimate of `theta2` to `4.19814911033061` #> ℹ change initial estimate of `theta3` to `1.38003493562413` #> ℹ change initial estimate of `theta4` to `3.87657341967489` #> ℹ change initial estimate of `theta5` to `0.196446108190896` #> ℹ change initial estimate of `eta1` to `0.101251418415006` #> ℹ change initial estimate of `eta2` to `0.0993872449483344` #> ℹ change initial estimate of `eta3` to `0.101302674763154` #> ℹ change initial estimate of `eta4` to `0.0730497519364148` #> ℹ read in nonmem input data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/Bolus_2CPT.csv #> ℹ ignoring lines that begin with a letter (IGNORE=@)' #> ℹ applying names specified by $INPUT #> ℹ subsetting accept/ignore filters code: .data[-which((.data$SD == 0)),] #> ℹ done #>   #>   #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ read in nonmem IPRED data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.csv #> ℹ done #> ℹ changing most variables to lower case #> ℹ done #> ℹ replace theta names #> ℹ done #> ℹ replace eta names #> ℹ done (no labels) #> ℹ renaming compartments #> ℹ done #>   #>   #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done  # define the model with residuals (and change the name of the # parameters) In this step you need to be careful to not change the # estimates and make sure the residual estimates are correct (could # have to change var to sd).   mod2 <-function() {    ini({      lcl <- 1.37034036528946      lvc <- 4.19814911033061      lq <- 1.38003493562413      lvp <- 3.87657341967489      RSV <- c(0, 0.196446108190896, 1)      eta.cl ~ 0.101251418415006      eta.v ~ 0.0993872449483344      eta.q ~ 0.101302674763154      eta.v2 ~ 0.0730497519364148    })    model({      cmt(CENTRAL)      cmt(PERI)      cl <- exp(lcl + eta.cl)      v <- exp(lvc + eta.v)      q <- exp(lq + eta.q)      v2 <- exp(lvp + eta.v2)      v1 <- v      scale1 <- v      k21 <- q/v2      k12 <- q/v      d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1      d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL      f <- CENTRAL/scale1      f ~ prop(RSV)    })  }  # now we create another nonmem2rx object that validates the model above:  new <- as.nonmem2rx(mod2, mod) #>   #>   #> ℹ copy 'dfSub' to nonmem2rx model #> ℹ copy 'thetaMat' to nonmem2rx model #> ℹ copy 'dfObs' to nonmem2rx model #>   #>   #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done  # once that is done, you can translate to a full nlmixr2 fit (if you wish)  fit <- as.nlmixr2(new) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> → optimizing duplicate expressions in EBE model... #> → compiling EBE model... #>   #>   #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.1.0 using 2 threads (see ?getRxThreads) #>   no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 204016 #> → compress parHistData in nlmixr2 object, save 2176  print(fit) #> ── nlmixr² nonmem2rx reading NONMEM ver 7.4.3 ── #>  #>               OBJF      AIC      BIC Log-likelihood Condition#(Cov) #> nonmem2rx 15977.28 20185.64 20237.23      -10083.82        335.4129 #>           Condition#(Cor) #> nonmem2rx        2.096559 #>  #> ── Time (sec $time): ── #>  #>            setup table compress NONMEM as.nlmixr2 #> elapsed 0.030514 0.075    0.016 100.95      2.582 #>  #> ── Population Parameters ($parFixed or $parFixedDf): ── #>  #>      Est.     SE  %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> lcl  1.37 0.0298  2.17       3.94 (3.71, 4.17)     32.6      1.94%  #> lvc   4.2 0.0295 0.703       66.6 (62.8, 70.5)     32.3      2.46%  #> lq   1.38 0.0547  3.96       3.98 (3.57, 4.42)     32.7      40.5%  #> lvp  3.88 0.0348 0.899       48.3 (45.1, 51.7)     27.5      28.4%  #> RSV 0.196                                0.196                      #>   #>   Covariance Type ($covMethod): nonmem2rx #>   No correlations in between subject variability (BSV) matrix #>   Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs)  #>   Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink  #>   Censoring ($censInformation): No censoring #>   Minimization message ($message):   #>      #>  #>  WARNINGS AND ERRORS (IF ANY) FOR PROBLEM    1 #>  #>  (WARNING  2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #>  #>      #> 0MINIMIZATION SUCCESSFUL #>  NO. OF FUNCTION EVALUATIONS USED:      320 #>  NO. OF SIG. DIGITS IN FINAL EST.:  2.5 #>  #>     IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.43e-06 #>     PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.41e-06 #>     IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.25e-05, 0.0418); atol=0.00167 #>     PRED absolute difference compared to Nonmem PRED: 95% percentile: (1.41e-07,0.00382); atol=6.41e-06 #>     nonmem2rx model file: '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl'  #>  #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 2,280 × 25 #>   ID     TIME    DV  PRED    RES IPRED  IRES  IWRES eta.cl eta.v  eta.q eta.v2 #>   <fct> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl> #> 1 1      0.25 1041. 1750. -710.  1215. -175. -0.732 -0.144 0.375 0.0650  0.241 #> 2 1      0.5  1629  1700.  -70.8 1192.  437.  1.87  -0.144 0.375 0.0650  0.241 #> 3 1      0.75  878. 1651. -774.  1169. -291. -1.27  -0.144 0.375 0.0650  0.241 #> # ℹ 2,277 more rows #> # ℹ 13 more variables: f <dbl>, CENTRAL <dbl>, PERI <dbl>, cl <dbl>, v <dbl>, #> #   q <dbl>, v2 <dbl>, v1 <dbl>, scale1 <dbl>, k21 <dbl>, k12 <dbl>, tad <dbl>, #> #   dosenum <dbl>  # }"},{"path":"/reference/bblDatToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Convert nlmixr2-compatible data formats (possible)","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"bblDatToMonolix(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )  bblDatToNonmem(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )  bblDatToRxode(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )  bblDatToMrgsolve(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )  bblDatToPknca(   model,   data,   table = nlmixr2est::tableControl(),   rxControl = rxode2::rxControl(),   env = NULL )"},{"path":"/reference/bblDatToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"model rxode2 model conversion data Input dataset. table table control; mostly figure additional columns keep. rxControl rxode2 control options; figure handle addl dosing information. env NULL (default) nothing done.  environment, function nlmixr2est::.foceiPreProcessData(data, env, model, rxControl) called provided environment.","code":""},{"path":"/reference/bblDatToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"function bblDatToMonolix() return list : Monolix compatible dataset ($monolix) Monolix ADM information ($adm) function nlmixrDataToNonmem() return dataset compatible NONMEM. function nlmixrDataToMrgsolve() return dataset compatible mrgsolve.  Unlike NONMEM, supports replacement events evid=8 (note rxode2 replacement evid 5). function nlmixrDataToRxode() normalize dataset use newer evid definitions closer NONMEM instead classic definitions used lower level","code":""},{"path":"/reference/bblDatToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Matthew L. Fidler","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"pk.turnover.emax3 <- function() {   ini({     tktr <- log(1)     tka <- log(1)     tcl <- log(0.1)     tv <- log(10)     ##     eta.ktr ~ 1     eta.ka ~ 1     eta.cl ~ 2     eta.v ~ 1     prop.err <- 0.1     pkadd.err <- 0.1     ##     temax <- logit(0.8)     tec50 <- log(0.5)     tkout <- log(0.05)     te0 <- log(100)     ##     eta.emax ~ .5     eta.ec50  ~ .5     eta.kout ~ .5     eta.e0 ~ .5     ##     pdadd.err <- 10   })   model({     ktr <- exp(tktr + eta.ktr)     ka <- exp(tka + eta.ka)     cl <- exp(tcl + eta.cl)     v <- exp(tv + eta.v)     emax = expit(temax+eta.emax)     ec50 =  exp(tec50 + eta.ec50)     kout = exp(tkout + eta.kout)     e0 = exp(te0 + eta.e0)     ##     DCP = center/v     PD=1-emax*DCP/(ec50+DCP)     ##     effect(0) = e0     kin = e0*kout     ##     d/dt(depot) = -ktr * depot     d/dt(gut) =  ktr * depot -ka * gut     d/dt(center) =  ka * gut - cl / v * center     d/dt(effect) = kin*PD -kout*effect     ##     cp = center / v     cp ~ prop(prop.err) + add(pkadd.err)     effect ~ add(pdadd.err) | pca   }) }  bblDatToMonolix(pk.turnover.emax3, nlmixr2data::warfarin) #>   #>   #> $monolix #>     ID  TIME EVID   AMT II    DV ADM YTYPE SS nlmixrRowNums #> 1    1   0.0    1 100.0  0    NA   1     0  0             1 #> 2    1   0.5    0    NA  0   0.0   0     1  0             2 #> 3    1   1.0    0    NA  0   1.9   0     1  0             3 #> 4    1   2.0    0    NA  0   3.3   0     1  0             4 #> 5    1   3.0    0    NA  0   6.6   0     1  0             5 #> 6    1   6.0    0    NA  0   9.1   0     1  0             6 #> 7    1   9.0    0    NA  0  10.8   0     1  0             7 #> 8    1  12.0    0    NA  0   8.6   0     1  0             8 #> 9    1  24.0    0    NA  0   5.6   0     1  0             9 #> 10   1  24.0    0    NA  0  44.0   0     2  0            10 #> 11   1  36.0    0    NA  0   4.0   0     1  0            11 #> 12   1  36.0    0    NA  0  27.0   0     2  0            12 #> 13   1  48.0    0    NA  0   2.7   0     1  0            13 #> 14   1  48.0    0    NA  0  28.0   0     2  0            14 #> 15   1  72.0    0    NA  0   0.8   0     1  0            15 #> 16   1  72.0    0    NA  0  31.0   0     2  0            16 #> 17   1  96.0    0    NA  0  60.0   0     2  0            17 #> 18   1 120.0    0    NA  0  65.0   0     2  0            18 #> 19   1 144.0    0    NA  0  71.0   0     2  0            19 #> 20   2   0.0    1 100.0  0    NA   1     0  0            20 #> 21   2   0.0    0    NA  0 100.0   0     2  0            21 #> 22   2  24.0    0    NA  0   9.2   0     1  0            22 #> 23   2  24.0    0    NA  0  49.0   0     2  0            23 #> 24   2  36.0    0    NA  0   8.5   0     1  0            24 #> 25   2  36.0    0    NA  0  32.0   0     2  0            25 #> 26   2  48.0    0    NA  0   6.4   0     1  0            26 #> 27   2  48.0    0    NA  0  26.0   0     2  0            27 #> 28   2  72.0    0    NA  0   4.8   0     1  0            28 #> 29   2  72.0    0    NA  0  22.0   0     2  0            29 #> 30   2  96.0    0    NA  0   3.1   0     1  0            30 #> 31   2  96.0    0    NA  0  28.0   0     2  0            31 #> 32   2 120.0    0    NA  0   2.5   0     1  0            32 #> 33   2 120.0    0    NA  0  33.0   0     2  0            33 #> 34   3   0.0    1 100.0  0    NA   1     0  0            34 #> 35   3   0.0    0    NA  0 100.0   0     2  0            35 #> 36   3   0.5    0    NA  0   0.0   0     1  0            36 #> 37   3   2.0    0    NA  0   8.4   0     1  0            37 #> 38   3   3.0    0    NA  0   9.7   0     1  0            38 #> 39   3   6.0    0    NA  0   9.8   0     1  0            39 #> 40   3  12.0    0    NA  0  11.0   0     1  0            40 #> 41   3  24.0    0    NA  0   8.3   0     1  0            41 #> 42   3  24.0    0    NA  0  46.0   0     2  0            42 #> 43   3  36.0    0    NA  0   7.7   0     1  0            43 #> 44   3  36.0    0    NA  0  22.0   0     2  0            44 #> 45   3  48.0    0    NA  0   6.3   0     1  0            45 #> 46   3  48.0    0    NA  0  19.0   0     2  0            46 #> 47   3  72.0    0    NA  0   4.1   0     1  0            47 #> 48   3  72.0    0    NA  0  20.0   0     2  0            48 #> 49   3  96.0    0    NA  0   3.0   0     1  0            49 #> 50   3  96.0    0    NA  0  42.0   0     2  0            50 #> 51   3 120.0    0    NA  0   1.4   0     1  0            51 #> 52   3 120.0    0    NA  0  49.0   0     2  0            52 #> 53   3 144.0    0    NA  0  54.0   0     2  0            53 #> 54   4   0.0    1 120.0  0    NA   1     0  0            54 #> 55   4   0.0    0    NA  0 100.0   0     2  0            55 #> 56   4   3.0    0    NA  0  12.0   0     1  0            56 #> 57   4   6.0    0    NA  0  13.2   0     1  0            57 #> 58   4   9.0    0    NA  0  14.4   0     1  0            58 #> 59   4  24.0    0    NA  0   9.6   0     1  0            59 #> 60   4  24.0    0    NA  0  30.0   0     2  0            60 #> 61   4  36.0    0    NA  0   8.2   0     1  0            61 #> 62   4  36.0    0    NA  0  24.0   0     2  0            62 #> 63   4  48.0    0    NA  0   7.8   0     1  0            63 #> 64   4  48.0    0    NA  0  13.0   0     2  0            64 #> 65   4  72.0    0    NA  0   5.8   0     1  0            65 #> 66   4  72.0    0    NA  0   9.0   0     2  0            66 #> 67   4  96.0    0    NA  0   4.3   0     1  0            67 #> 68   4  96.0    0    NA  0   9.0   0     2  0            68 #> 69   4 120.0    0    NA  0   3.0   0     1  0            69 #> 70   4 120.0    0    NA  0  11.0   0     2  0            70 #> 71   4 144.0    0    NA  0  12.0   0     2  0            71 #> 72   5   0.0    1  60.0  0    NA   1     0  0            72 #> 73   5   0.0    0    NA  0  82.0   0     2  0            73 #> 74   5   3.0    0    NA  0  11.1   0     1  0            74 #> 75   5   6.0    0    NA  0  11.9   0     1  0            75 #> 76   5   9.0    0    NA  0   9.8   0     1  0            76 #> 77   5  12.0    0    NA  0  11.0   0     1  0            77 #> 78   5  24.0    0    NA  0   8.5   0     1  0            78 #> 79   5  24.0    0    NA  0  43.0   0     2  0            79 #> 80   5  36.0    0    NA  0   7.6   0     1  0            80 #> 81   5  36.0    0    NA  0  25.0   0     2  0            81 #> 82   5  48.0    0    NA  0   5.4   0     1  0            82 #> 83   5  48.0    0    NA  0  18.0   0     2  0            83 #> 84   5  72.0    0    NA  0   4.5   0     1  0            84 #> 85   5  72.0    0    NA  0  17.0   0     2  0            85 #> 86   5  96.0    0    NA  0   3.3   0     1  0            86 #> 87   5  96.0    0    NA  0  23.0   0     2  0            87 #> 88   5 120.0    0    NA  0   2.3   0     1  0            88 #> 89   5 120.0    0    NA  0  29.0   0     2  0            89 #> 90   5 144.0    0    NA  0  41.0   0     2  0            90 #> 91   6   0.0    1 113.0  0    NA   1     0  0            91 #> 92   6   0.0    0    NA  0 100.0   0     2  0            92 #> 93   6   6.0    0    NA  0   8.6   0     1  0            93 #> 94   6  12.0    0    NA  0   8.6   0     1  0            94 #> 95   6  24.0    0    NA  0   7.0   0     1  0            95 #> 96   6  24.0    0    NA  0  34.0   0     2  0            96 #> 97   6  36.0    0    NA  0   5.7   0     1  0            97 #> 98   6  36.0    0    NA  0  23.0   0     2  0            98 #> 99   6  48.0    0    NA  0   4.7   0     1  0            99 #> 100  6  48.0    0    NA  0  20.0   0     2  0           100 #> 101  6  72.0    0    NA  0   3.3   0     1  0           101 #> 102  6  72.0    0    NA  0  16.0   0     2  0           102 #> 103  6  96.0    0    NA  0   2.3   0     1  0           103 #> 104  6  96.0    0    NA  0  17.0   0     2  0           104 #> 105  6 120.0    0    NA  0   1.7   0     1  0           105 #> 106  6 120.0    0    NA  0  18.0   0     2  0           106 #> 107  6 144.0    0    NA  0  25.0   0     2  0           107 #> 108  7   0.0    1  90.0  0    NA   1     0  0           108 #> 109  7   3.0    0    NA  0  13.4   0     1  0           109 #> 110  7   6.0    0    NA  0  12.4   0     1  0           110 #> 111  7   9.0    0    NA  0  12.7   0     1  0           111 #> 112  7  12.0    0    NA  0   8.8   0     1  0           112 #> 113  7  24.0    0    NA  0   6.1   0     1  0           113 #> 114  7  24.0    0    NA  0  36.0   0     2  0           114 #> 115  7  36.0    0    NA  0   3.5   0     1  0           115 #> 116  7  36.0    0    NA  0  33.0   0     2  0           116 #> 117  7  48.0    0    NA  0   1.8   0     1  0           117 #> 118  7  48.0    0    NA  0  28.0   0     2  0           118 #> 119  7  72.0    0    NA  0   1.5   0     1  0           119 #> 120  7  72.0    0    NA  0  52.0   0     2  0           120 #> 121  7  96.0    0    NA  0   1.0   0     1  0           121 #> 122  7  96.0    0    NA  0  80.0   0     2  0           122 #> 123  7 120.0    0    NA  0  90.0   0     2  0           123 #> 124  7 144.0    0    NA  0 100.0   0     2  0           124 #> 125  8   0.0    1 135.0  0    NA   1     0  0           125 #> 126  8   0.0    0    NA  0  88.0   0     2  0           126 #> 127  8   2.0    0    NA  0  17.6   0     1  0           127 #> 128  8   3.0    0    NA  0  17.3   0     1  0           128 #> 129  8   6.0    0    NA  0  15.0   0     1  0           129 #> 130  8   9.0    0    NA  0  15.0   0     1  0           130 #> 131  8  12.0    0    NA  0  12.4   0     1  0           131 #> 132  8  24.0    0    NA  0   7.9   0     1  0           132 #> 133  8  24.0    0    NA  0  35.0   0     2  0           133 #> 134  8  36.0    0    NA  0   7.9   0     1  0           134 #> 135  8  36.0    0    NA  0  20.0   0     2  0           135 #> 136  8  48.0    0    NA  0   5.1   0     1  0           136 #> 137  8  48.0    0    NA  0  12.0   0     2  0           137 #> 138  8  72.0    0    NA  0   3.6   0     1  0           138 #> 139  8  72.0    0    NA  0  16.0   0     2  0           139 #> 140  8  96.0    0    NA  0   2.4   0     1  0           140 #> 141  8  96.0    0    NA  0  23.0   0     2  0           141 #> 142  8 120.0    0    NA  0   2.0   0     1  0           142 #> 143  8 120.0    0    NA  0  36.0   0     2  0           143 #> 144  8 144.0    0    NA  0  48.0   0     2  0           144 #> 145  9   0.0    1  75.0  0    NA   1     0  0           145 #> 146  9   0.0    0    NA  0  92.0   0     2  0           146 #> 147  9   0.5    0    NA  0   0.0   0     1  0           147 #> 148  9   1.0    0    NA  0   1.0   0     1  0           148 #> 149  9   2.0    0    NA  0   4.6   0     1  0           149 #> 150  9   3.0    0    NA  0  12.7   0     1  0           150 #> 151  9   3.0    0    NA  0   8.0   0     1  0           151 #> 152  9   6.0    0    NA  0  12.7   0     1  0           152 #> 153  9   6.0    0    NA  0  11.5   0     1  0           153 #> 154  9   9.0    0    NA  0  12.9   0     1  0           154 #> 155  9   9.0    0    NA  0  11.4   0     1  0           155 #> 156  9  12.0    0    NA  0  11.4   0     1  0           156 #> 157  9  12.0    0    NA  0  11.0   0     1  0           157 #> 158  9  24.0    0    NA  0   9.1   0     1  0           158 #> 159  9  24.0    0    NA  0  33.0   0     2  0           159 #> 160  9  36.0    0    NA  0   8.2   0     1  0           160 #> 161  9  36.0    0    NA  0  22.0   0     2  0           161 #> 162  9  48.0    0    NA  0   5.9   0     1  0           162 #> 163  9  48.0    0    NA  0  16.0   0     2  0           163 #> 164  9  72.0    0    NA  0   3.6   0     1  0           164 #> 165  9  72.0    0    NA  0  18.0   0     2  0           165 #> 166  9  96.0    0    NA  0   1.7   0     1  0           166 #> 167  9  96.0    0    NA  0  32.0   0     2  0           167 #> 168  9 120.0    0    NA  0   1.1   0     1  0           168 #> 169  9 120.0    0    NA  0  30.0   0     2  0           169 #> 170  9 144.0    0    NA  0  45.0   0     2  0           170 #> 171 10   0.0    1 105.0  0    NA   1     0  0           171 #> 172 10   0.0    0    NA  0  90.0   0     2  0           172 #> 173 10  24.0    0    NA  0   8.6   0     1  0           173 #> 174 10  24.0    0    NA  0  39.0   0     2  0           174 #> 175 10  36.0    0    NA  0   8.0   0     1  0           175 #> 176 10  36.0    0    NA  0  22.0   0     2  0           176 #> 177 10  48.0    0    NA  0   6.0   0     1  0           177 #> 178 10  48.0    0    NA  0  17.0   0     2  0           178 #> 179 10  72.0    0    NA  0   4.4   0     1  0           179 #> 180 10  72.0    0    NA  0  17.0   0     2  0           180 #> 181 10  96.0    0    NA  0   3.6   0     1  0           181 #> 182 10  96.0    0    NA  0  22.0   0     2  0           182 #> 183 10 120.0    0    NA  0   2.8   0     1  0           183 #> 184 10 120.0    0    NA  0  25.0   0     2  0           184 #> 185 10 144.0    0    NA  0  33.0   0     2  0           185 #> 186 11   0.0    1 123.0  0    NA   1     0  0           186 #> 187 11   0.0    0    NA  0 100.0   0     2  0           187 #> 188 11   1.5    0    NA  0  11.4   0     1  0           188 #> 189 11   3.0    0    NA  0  15.4   0     1  0           189 #> 190 11   6.0    0    NA  0  17.5   0     1  0           190 #> 191 11  12.0    0    NA  0  14.0   0     1  0           191 #> 192 11  24.0    0    NA  0   9.0   0     1  0           192 #> 193 11  24.0    0    NA  0  37.0   0     2  0           193 #> 194 11  36.0    0    NA  0   8.9   0     1  0           194 #> 195 11  36.0    0    NA  0  24.0   0     2  0           195 #> 196 11  48.0    0    NA  0   6.6   0     1  0           196 #> 197 11  48.0    0    NA  0  14.0   0     2  0           197 #> 198 11  72.0    0    NA  0   4.2   0     1  0           198 #> 199 11  72.0    0    NA  0  11.0   0     2  0           199 #> 200 11  96.0    0    NA  0   3.6   0     1  0           200 #> 201 11  96.0    0    NA  0  14.0   0     2  0           201 #> 202 11 120.0    0    NA  0   2.6   0     1  0           202 #> 203 11 120.0    0    NA  0  23.0   0     2  0           203 #> 204 11 144.0    0    NA  0  33.0   0     2  0           204 #> 205 12   0.0    1 113.0  0    NA   1     0  0           205 #> 206 12   0.0    0    NA  0  85.0   0     2  0           206 #> 207 12   1.5    0    NA  0   0.6   0     1  0           207 #> 208 12   3.0    0    NA  0   2.8   0     1  0           208 #> 209 12   6.0    0    NA  0  13.8   0     1  0           209 #> 210 12   9.0    0    NA  0  15.0   0     1  0           210 #> 211 12  24.0    0    NA  0  10.5   0     1  0           211 #> 212 12  24.0    0    NA  0  25.0   0     2  0           212 #> 213 12  36.0    0    NA  0   9.1   0     1  0           213 #> 214 12  36.0    0    NA  0  15.0   0     2  0           214 #> 215 12  48.0    0    NA  0   6.6   0     1  0           215 #> 216 12  48.0    0    NA  0  11.0   0     2  0           216 #> 217 12  72.0    0    NA  0   4.9   0     1  0           217 #> 218 12  96.0    0    NA  0   2.4   0     1  0           218 #> 219 12 120.0    0    NA  0   1.9   0     1  0           219 #> 220 13   0.0    1 113.0  0    NA   1     0  0           220 #> 221 13   0.0    0    NA  0  88.0   0     2  0           221 #> 222 13   1.5    0    NA  0   3.6   0     1  0           222 #> 223 13   3.0    0    NA  0  12.9   0     1  0           223 #> 224 13   6.0    0    NA  0  12.9   0     1  0           224 #> 225 13   9.0    0    NA  0  10.2   0     1  0           225 #> 226 13  24.0    0    NA  0   6.4   0     1  0           226 #> 227 13  24.0    0    NA  0  41.0   0     2  0           227 #> 228 13  36.0    0    NA  0   6.9   0     1  0           228 #> 229 13  36.0    0    NA  0  23.0   0     2  0           229 #> 230 13  48.0    0    NA  0   4.5   0     1  0           230 #> 231 13  48.0    0    NA  0  16.0   0     2  0           231 #> 232 13  72.0    0    NA  0   3.2   0     1  0           232 #> 233 13  72.0    0    NA  0  14.0   0     2  0           233 #> 234 13  96.0    0    NA  0   2.4   0     1  0           234 #> 235 13  96.0    0    NA  0  18.0   0     2  0           235 #> 236 13 120.0    0    NA  0   1.3   0     1  0           236 #> 237 13 120.0    0    NA  0  22.0   0     2  0           237 #> 238 13 144.0    0    NA  0  35.0   0     2  0           238 #> 239 14   0.0    1  75.0  0    NA   1     0  0           239 #> 240 14   0.0    0    NA  0  85.0   0     2  0           240 #> 241 14   0.5    0    NA  0   0.0   0     1  0           241 #> 242 14   1.0    0    NA  0   2.7   0     1  0           242 #> 243 14   2.0    0    NA  0  11.6   0     1  0           243 #> 244 14   3.0    0    NA  0  11.6   0     1  0           244 #> 245 14   6.0    0    NA  0  11.3   0     1  0           245 #> 246 14   9.0    0    NA  0   9.7   0     1  0           246 #> 247 14  24.0    0    NA  0   6.5   0     1  0           247 #> 248 14  24.0    0    NA  0  32.0   0     2  0           248 #> 249 14  36.0    0    NA  0   5.2   0     1  0           249 #> 250 14  36.0    0    NA  0  22.0   0     2  0           250 #> 251 14  48.0    0    NA  0   3.6   0     1  0           251 #> 252 14  48.0    0    NA  0  21.0   0     2  0           252 #> 253 14  72.0    0    NA  0   2.4   0     1  0           253 #> 254 14  72.0    0    NA  0  28.0   0     2  0           254 #> 255 14  96.0    0    NA  0   0.9   0     1  0           255 #> 256 14  96.0    0    NA  0  38.0   0     2  0           256 #> 257 14 120.0    0    NA  0  46.0   0     2  0           257 #> 258 14 144.0    0    NA  0  65.0   0     2  0           258 #> 259 15   0.0    1  85.0  0    NA   1     0  0           259 #> 260 15   0.0    0    NA  0 100.0   0     2  0           260 #> 261 15   1.0    0    NA  0   6.6   0     1  0           261 #> 262 15   3.0    0    NA  0  11.9   0     1  0           262 #> 263 15   6.0    0    NA  0  11.7   0     1  0           263 #> 264 15   9.0    0    NA  0  12.2   0     1  0           264 #> 265 15  24.0    0    NA  0   8.1   0     1  0           265 #> 266 15  24.0    0    NA  0  43.0   0     2  0           266 #> 267 15  36.0    0    NA  0   7.4   0     1  0           267 #> 268 15  36.0    0    NA  0  26.0   0     2  0           268 #> 269 15  48.0    0    NA  0   6.8   0     1  0           269 #> 270 15  48.0    0    NA  0  15.0   0     2  0           270 #> 271 15  72.0    0    NA  0   5.3   0     1  0           271 #> 272 15  72.0    0    NA  0  13.0   0     2  0           272 #> 273 15  96.0    0    NA  0   3.0   0     1  0           273 #> 274 15  96.0    0    NA  0  21.0   0     2  0           274 #> 275 15 120.0    0    NA  0   2.0   0     1  0           275 #> 276 15 120.0    0    NA  0  28.0   0     2  0           276 #> 277 15 144.0    0    NA  0  39.0   0     2  0           277 #> 278 16   0.0    1  87.0  0    NA   1     0  0           278 #> 279 16   0.0    0    NA  0 100.0   0     2  0           279 #> 280 16  24.0    0    NA  0  10.4   0     1  0           280 #> 281 16  24.0    0    NA  0  42.0   0     2  0           281 #> 282 16  36.0    0    NA  0   8.9   0     1  0           282 #> 283 16  36.0    0    NA  0  32.0   0     2  0           283 #> 284 16  48.0    0    NA  0   7.0   0     1  0           284 #> 285 16  48.0    0    NA  0  26.0   0     2  0           285 #> 286 16  72.0    0    NA  0   4.4   0     1  0           286 #> 287 16  72.0    0    NA  0  31.0   0     2  0           287 #> 288 16  96.0    0    NA  0   3.2   0     1  0           288 #> 289 16  96.0    0    NA  0  33.0   0     2  0           289 #> 290 16 120.0    0    NA  0   2.4   0     1  0           290 #> 291 16 120.0    0    NA  0  54.0   0     2  0           291 #> 292 17   0.0    1 117.0  0    NA   1     0  0           292 #> 293 17   0.0    0    NA  0 100.0   0     2  0           293 #> 294 17  24.0    0    NA  0   7.6   0     1  0           294 #> 295 17  24.0    0    NA  0  35.0   0     2  0           295 #> 296 17  36.0    0    NA  0   6.4   0     1  0           296 #> 297 17  36.0    0    NA  0  23.0   0     2  0           297 #> 298 17  48.0    0    NA  0   6.0   0     1  0           298 #> 299 17  48.0    0    NA  0  17.0   0     2  0           299 #> 300 17  72.0    0    NA  0   4.0   0     1  0           300 #> 301 17  72.0    0    NA  0  18.0   0     2  0           301 #> 302 17  96.0    0    NA  0   3.1   0     1  0           302 #> 303 17  96.0    0    NA  0  18.0   0     2  0           303 #> 304 17 120.0    0    NA  0   2.0   0     1  0           304 #> 305 17 120.0    0    NA  0  21.0   0     2  0           305 #> 306 18   0.0    1 112.0  0    NA   1     0  0           306 #> 307 18   0.0    0    NA  0 100.0   0     2  0           307 #> 308 18  24.0    0    NA  0   7.6   0     1  0           308 #> 309 18  24.0    0    NA  0  32.0   0     2  0           309 #> 310 18  36.0    0    NA  0   6.6   0     1  0           310 #> 311 18  36.0    0    NA  0  20.0   0     2  0           311 #> 312 18  48.0    0    NA  0   5.4   0     1  0           312 #> 313 18  48.0    0    NA  0  18.0   0     2  0           313 #> 314 18  72.0    0    NA  0   3.4   0     1  0           314 #> 315 18  72.0    0    NA  0  18.0   0     2  0           315 #> 316 18  96.0    0    NA  0   1.2   0     1  0           316 #> 317 18  96.0    0    NA  0  19.0   0     2  0           317 #> 318 18 120.0    0    NA  0   0.9   0     1  0           318 #> 319 18 120.0    0    NA  0  29.0   0     2  0           319 #> 320 19   0.0    1  95.5  0    NA   1     0  0           320 #> 321 19   0.0    0    NA  0 100.0   0     2  0           321 #> 322 19  24.0    0    NA  0   6.6   0     1  0           322 #> 323 19  24.0    0    NA  0  33.0   0     2  0           323 #> 324 19  36.0    0    NA  0   5.3   0     1  0           324 #> 325 19  36.0    0    NA  0  28.0   0     2  0           325 #> 326 19  48.0    0    NA  0   3.6   0     1  0           326 #> 327 19  48.0    0    NA  0  18.0   0     2  0           327 #> 328 19  72.0    0    NA  0   2.7   0     1  0           328 #> 329 19  72.0    0    NA  0  18.0   0     2  0           329 #> 330 19  96.0    0    NA  0   1.4   0     1  0           330 #> 331 19  96.0    0    NA  0  17.0   0     2  0           331 #> 332 19 120.0    0    NA  0   1.1   0     1  0           332 #> 333 19 120.0    0    NA  0  26.0   0     2  0           333 #> 334 20   0.0    1  88.5  0    NA   1     0  0           334 #> 335 20   0.0    0    NA  0 100.0   0     2  0           335 #> 336 20  24.0    0    NA  0   9.6   0     1  0           336 #> 337 20  24.0    0    NA  0  41.0   0     2  0           337 #> 338 20  36.0    0    NA  0   8.0   0     1  0           338 #> 339 20  36.0    0    NA  0  30.0   0     2  0           339 #> 340 20  48.0    0    NA  0   6.6   0     1  0           340 #> 341 20  48.0    0    NA  0  22.0   0     2  0           341 #> 342 20  72.0    0    NA  0   5.6   0     1  0           342 #> 343 20  72.0    0    NA  0  23.0   0     2  0           343 #> 344 20  96.0    0    NA  0   3.5   0     1  0           344 #> 345 20  96.0    0    NA  0  23.0   0     2  0           345 #> 346 20 120.0    0    NA  0   2.3   0     1  0           346 #> 347 20 120.0    0    NA  0  35.0   0     2  0           347 #> 348 21   0.0    1  93.0  0    NA   1     0  0           348 #> 349 21   0.0    0    NA  0 100.0   0     2  0           349 #> 350 21  24.0    0    NA  0   7.3   0     1  0           350 #> 351 21  24.0    0    NA  0  46.0   0     2  0           351 #> 352 21  36.0    0    NA  0   6.1   0     1  0           352 #> 353 21  36.0    0    NA  0  27.0   0     2  0           353 #> 354 21  48.0    0    NA  0   4.3   0     1  0           354 #> 355 21  48.0    0    NA  0  22.0   0     2  0           355 #> 356 21  72.0    0    NA  0   3.2   0     1  0           356 #> 357 21  72.0    0    NA  0  36.0   0     2  0           357 #> 358 21  96.0    0    NA  0   2.3   0     1  0           358 #> 359 21  96.0    0    NA  0  40.0   0     2  0           359 #> 360 21 120.0    0    NA  0   1.9   0     1  0           360 #> 361 21 120.0    0    NA  0  44.0   0     2  0           361 #> 362 22   0.0    1  87.0  0    NA   1     0  0           362 #> 363 22   0.0    0    NA  0 100.0   0     2  0           363 #> 364 22  24.0    0    NA  0   8.9   0     1  0           364 #> 365 22  24.0    0    NA  0  35.0   0     2  0           365 #> 366 22  36.0    0    NA  0   8.4   0     1  0           366 #> 367 22  36.0    0    NA  0  27.0   0     2  0           367 #> 368 22  48.0    0    NA  0   8.0   0     1  0           368 #> 369 22  48.0    0    NA  0  23.0   0     2  0           369 #> 370 22  72.0    0    NA  0   4.4   0     1  0           370 #> 371 22  72.0    0    NA  0  27.0   0     2  0           371 #> 372 22  96.0    0    NA  0   3.2   0     1  0           372 #> 373 22  96.0    0    NA  0  43.0   0     2  0           373 #> 374 22 120.0    0    NA  0   1.7   0     1  0           374 #> 375 22 120.0    0    NA  0  43.0   0     2  0           375 #> 376 23   0.0    1 110.0  0    NA   1     0  0           376 #> 377 23   0.0    0    NA  0 100.0   0     2  0           377 #> 378 23  24.0    0    NA  0   9.8   0     1  0           378 #> 379 23  24.0    0    NA  0  34.0   0     2  0           379 #> 380 23  36.0    0    NA  0   8.4   0     1  0           380 #> 381 23  36.0    0    NA  0  24.0   0     2  0           381 #> 382 23  48.0    0    NA  0   6.6   0     1  0           382 #> 383 23  48.0    0    NA  0  15.0   0     2  0           383 #> 384 23  72.0    0    NA  0   4.8   0     1  0           384 #> 385 23  72.0    0    NA  0  15.0   0     2  0           385 #> 386 23  96.0    0    NA  0   3.2   0     1  0           386 #> 387 23  96.0    0    NA  0  19.0   0     2  0           387 #> 388 23 120.0    0    NA  0   2.4   0     1  0           388 #> 389 23 120.0    0    NA  0  19.0   0     2  0           389 #> 390 24   0.0    1 115.0  0    NA   1     0  0           390 #> 391 24   0.0    0    NA  0  88.0   0     2  0           391 #> 392 24  24.0    0    NA  0   8.2   0     1  0           392 #> 393 24  24.0    0    NA  0  37.0   0     2  0           393 #> 394 24  36.0    0    NA  0   7.5   0     1  0           394 #> 395 24  36.0    0    NA  0  20.0   0     2  0           395 #> 396 24  48.0    0    NA  0   6.8   0     1  0           396 #> 397 24  48.0    0    NA  0  20.0   0     2  0           397 #> 398 24  72.0    0    NA  0   5.5   0     1  0           398 #> 399 24  72.0    0    NA  0  26.0   0     2  0           399 #> 400 24  96.0    0    NA  0   4.5   0     1  0           400 #> 401 24  96.0    0    NA  0  28.0   0     2  0           401 #> 402 24 120.0    0    NA  0   3.7   0     1  0           402 #> 403 24 120.0    0    NA  0  50.0   0     2  0           403 #> 404 25   0.0    1 112.0  0    NA   1     0  0           404 #> 405 25   0.0    0    NA  0 100.0   0     2  0           405 #> 406 25  24.0    0    NA  0  11.0   0     1  0           406 #> 407 25  24.0    0    NA  0  32.0   0     2  0           407 #> 408 25  36.0    0    NA  0  10.0   0     1  0           408 #> 409 25  36.0    0    NA  0  20.0   0     2  0           409 #> 410 25  48.0    0    NA  0   8.2   0     1  0           410 #> 411 25  48.0    0    NA  0  17.0   0     2  0           411 #> 412 25  72.0    0    NA  0   6.0   0     1  0           412 #> 413 25  72.0    0    NA  0  19.0   0     2  0           413 #> 414 25  96.0    0    NA  0   3.7   0     1  0           414 #> 415 25  96.0    0    NA  0  21.0   0     2  0           415 #> 416 25 120.0    0    NA  0   2.6   0     1  0           416 #> 417 25 120.0    0    NA  0  30.0   0     2  0           417 #> 418 26   0.0    1 120.0  0    NA   1     0  0           418 #> 419 26   0.0    0    NA  0 100.0   0     2  0           419 #> 420 26  24.0    0    NA  0  10.0   0     1  0           420 #> 421 26  24.0    0    NA  0  41.0   0     2  0           421 #> 422 26  36.0    0    NA  0   9.0   0     1  0           422 #> 423 26  36.0    0    NA  0  28.0   0     2  0           423 #> 424 26  48.0    0    NA  0   7.3   0     1  0           424 #> 425 26  48.0    0    NA  0  19.0   0     2  0           425 #> 426 26  72.0    0    NA  0   5.2   0     1  0           426 #> 427 26  72.0    0    NA  0  17.0   0     2  0           427 #> 428 26  96.0    0    NA  0   3.7   0     1  0           428 #> 429 26  96.0    0    NA  0  17.0   0     2  0           429 #> 430 26 120.0    0    NA  0   2.7   0     1  0           430 #> 431 26 120.0    0    NA  0  24.0   0     2  0           431 #> 432 27   0.0    1 120.0  0    NA   1     0  0           432 #> 433 27   0.0    0    NA  0 100.0   0     2  0           433 #> 434 27  24.0    0    NA  0  11.8   0     1  0           434 #> 435 27  24.0    0    NA  0  32.0   0     2  0           435 #> 436 27  36.0    0    NA  0   9.2   0     1  0           436 #> 437 27  36.0    0    NA  0  21.0   0     2  0           437 #> 438 27  48.0    0    NA  0   7.7   0     1  0           438 #> 439 27  48.0    0    NA  0  19.0   0     2  0           439 #> 440 27  72.0    0    NA  0   4.9   0     1  0           440 #> 441 27  72.0    0    NA  0  22.0   0     2  0           441 #> 442 27  96.0    0    NA  0   3.4   0     1  0           442 #> 443 27  96.0    0    NA  0  33.0   0     2  0           443 #> 444 27 120.0    0    NA  0   2.7   0     1  0           444 #> 445 27 120.0    0    NA  0  46.0   0     2  0           445 #> 446 28   0.0    1 120.0  0    NA   1     0  0           446 #> 447 28   0.0    0    NA  0 100.0   0     2  0           447 #> 448 28  24.0    0    NA  0  10.1   0     1  0           448 #> 449 28  24.0    0    NA  0  39.0   0     2  0           449 #> 450 28  36.0    0    NA  0   8.0   0     1  0           450 #> 451 28  36.0    0    NA  0  25.0   0     2  0           451 #> 452 28  48.0    0    NA  0   6.0   0     1  0           452 #> 453 28  48.0    0    NA  0  16.0   0     2  0           453 #> 454 28  72.0    0    NA  0   4.9   0     1  0           454 #> 455 28  72.0    0    NA  0  14.0   0     2  0           455 #> 456 28  96.0    0    NA  0   3.4   0     1  0           456 #> 457 28  96.0    0    NA  0  15.0   0     2  0           457 #> 458 28 120.0    0    NA  0   2.0   0     1  0           458 #> 459 28 120.0    0    NA  0  20.0   0     2  0           459 #> 460 29   0.0    1 153.0  0    NA   1     0  0           460 #> 461 29   0.0    0    NA  0  86.0   0     2  0           461 #> 462 29  24.0    0    NA  0   8.3   0     1  0           462 #> 463 29  24.0    0    NA  0  35.0   0     2  0           463 #> 464 29  36.0    0    NA  0   7.0   0     1  0           464 #> 465 29  36.0    0    NA  0  21.0   0     2  0           465 #> 466 29  48.0    0    NA  0   5.6   0     1  0           466 #> 467 29  48.0    0    NA  0  18.0   0     2  0           467 #> 468 29  72.0    0    NA  0   4.1   0     1  0           468 #> 469 29  72.0    0    NA  0  20.0   0     2  0           469 #> 470 29  96.0    0    NA  0   3.1   0     1  0           470 #> 471 29  96.0    0    NA  0  29.0   0     2  0           471 #> 472 29 120.0    0    NA  0   2.2   0     1  0           472 #> 473 29 120.0    0    NA  0  41.0   0     2  0           473 #> 474 30   0.0    1 105.0  0    NA   1     0  0           474 #> 475 30   0.0    0    NA  0 100.0   0     2  0           475 #> 476 30  24.0    0    NA  0   9.9   0     1  0           476 #> 477 30  24.0    0    NA  0  45.0   0     2  0           477 #> 478 30  36.0    0    NA  0   7.5   0     1  0           478 #> 479 30  36.0    0    NA  0  24.0   0     2  0           479 #> 480 30  48.0    0    NA  0   6.5   0     1  0           480 #> 481 30  48.0    0    NA  0  23.0   0     2  0           481 #> 482 30  72.0    0    NA  0   4.1   0     1  0           482 #> 483 30  72.0    0    NA  0  26.0   0     2  0           483 #> 484 30  96.0    0    NA  0   2.9   0     1  0           484 #> 485 30  96.0    0    NA  0  28.0   0     2  0           485 #> 486 30 120.0    0    NA  0   2.3   0     1  0           486 #> 487 30 120.0    0    NA  0  39.0   0     2  0           487 #> 488 31   0.0    1 125.0  0    NA   1     0  0           488 #> 489 31   0.0    0    NA  0 100.0   0     2  0           489 #> 490 31  24.0    0    NA  0   9.5   0     1  0           490 #> 491 31  24.0    0    NA  0  45.0   0     2  0           491 #> 492 31  36.0    0    NA  0   7.8   0     1  0           492 #> 493 31  36.0    0    NA  0  30.0   0     2  0           493 #> 494 31  48.0    0    NA  0   6.4   0     1  0           494 #> 495 31  48.0    0    NA  0  24.0   0     2  0           495 #> 496 31  72.0    0    NA  0   4.5   0     1  0           496 #> 497 31  72.0    0    NA  0  22.0   0     2  0           497 #> 498 31  96.0    0    NA  0   3.4   0     1  0           498 #> 499 31  96.0    0    NA  0  28.0   0     2  0           499 #> 500 31 120.0    0    NA  0   2.5   0     1  0           500 #> 501 31 120.0    0    NA  0  42.0   0     2  0           501 #> 502 32   0.0    1  93.0  0    NA   1     0  0           502 #> 503 32   0.0    0    NA  0 100.0   0     2  0           503 #> 504 32  24.0    0    NA  0   8.9   0     1  0           504 #> 505 32  24.0    0    NA  0  36.0   0     2  0           505 #> 506 32  36.0    0    NA  0   7.7   0     1  0           506 #> 507 32  36.0    0    NA  0  27.0   0     2  0           507 #> 508 32  48.0    0    NA  0   6.9   0     1  0           508 #> 509 32  48.0    0    NA  0  24.0   0     2  0           509 #> 510 32  72.0    0    NA  0   4.4   0     1  0           510 #> 511 32  72.0    0    NA  0  23.0   0     2  0           511 #> 512 32  96.0    0    NA  0   3.5   0     1  0           512 #> 513 32  96.0    0    NA  0  20.0   0     2  0           513 #> 514 32 120.0    0    NA  0   2.5   0     1  0           514 #> 515 32 120.0    0    NA  0  22.0   0     2  0           515 #>  #> $adm #>   adm cmt  type #> 1   1   1 bolus #>   bblDatToNonmem(pk.turnover.emax3, nlmixr2data::warfarin) #>   #>   #>     ID  TIME EVID   AMT    DV CMT DVID nlmixrRowNums #> 1    1   0.0    1 100.0    NA   1   NA             1 #> 2    1   0.5    0    NA   0.0  NA    1             2 #> 3    1   1.0    0    NA   1.9  NA    1             3 #> 4    1   2.0    0    NA   3.3  NA    1             4 #> 5    1   3.0    0    NA   6.6  NA    1             5 #> 6    1   6.0    0    NA   9.1  NA    1             6 #> 7    1   9.0    0    NA  10.8  NA    1             7 #> 8    1  12.0    0    NA   8.6  NA    1             8 #> 9    1  24.0    0    NA   5.6  NA    1             9 #> 10   1  24.0    0    NA  44.0  NA    2            10 #> 11   1  36.0    0    NA   4.0  NA    1            11 #> 12   1  36.0    0    NA  27.0  NA    2            12 #> 13   1  48.0    0    NA   2.7  NA    1            13 #> 14   1  48.0    0    NA  28.0  NA    2            14 #> 15   1  72.0    0    NA   0.8  NA    1            15 #> 16   1  72.0    0    NA  31.0  NA    2            16 #> 17   1  96.0    0    NA  60.0  NA    2            17 #> 18   1 120.0    0    NA  65.0  NA    2            18 #> 19   1 144.0    0    NA  71.0  NA    2            19 #> 20   2   0.0    1 100.0    NA   1   NA            20 #> 21   2   0.0    0    NA 100.0  NA    2            21 #> 22   2  24.0    0    NA   9.2  NA    1            22 #> 23   2  24.0    0    NA  49.0  NA    2            23 #> 24   2  36.0    0    NA   8.5  NA    1            24 #> 25   2  36.0    0    NA  32.0  NA    2            25 #> 26   2  48.0    0    NA   6.4  NA    1            26 #> 27   2  48.0    0    NA  26.0  NA    2            27 #> 28   2  72.0    0    NA   4.8  NA    1            28 #> 29   2  72.0    0    NA  22.0  NA    2            29 #> 30   2  96.0    0    NA   3.1  NA    1            30 #> 31   2  96.0    0    NA  28.0  NA    2            31 #> 32   2 120.0    0    NA   2.5  NA    1            32 #> 33   2 120.0    0    NA  33.0  NA    2            33 #> 34   3   0.0    1 100.0    NA   1   NA            34 #> 35   3   0.0    0    NA 100.0  NA    2            35 #> 36   3   0.5    0    NA   0.0  NA    1            36 #> 37   3   2.0    0    NA   8.4  NA    1            37 #> 38   3   3.0    0    NA   9.7  NA    1            38 #> 39   3   6.0    0    NA   9.8  NA    1            39 #> 40   3  12.0    0    NA  11.0  NA    1            40 #> 41   3  24.0    0    NA   8.3  NA    1            41 #> 42   3  24.0    0    NA  46.0  NA    2            42 #> 43   3  36.0    0    NA   7.7  NA    1            43 #> 44   3  36.0    0    NA  22.0  NA    2            44 #> 45   3  48.0    0    NA   6.3  NA    1            45 #> 46   3  48.0    0    NA  19.0  NA    2            46 #> 47   3  72.0    0    NA   4.1  NA    1            47 #> 48   3  72.0    0    NA  20.0  NA    2            48 #> 49   3  96.0    0    NA   3.0  NA    1            49 #> 50   3  96.0    0    NA  42.0  NA    2            50 #> 51   3 120.0    0    NA   1.4  NA    1            51 #> 52   3 120.0    0    NA  49.0  NA    2            52 #> 53   3 144.0    0    NA  54.0  NA    2            53 #> 54   4   0.0    1 120.0    NA   1   NA            54 #> 55   4   0.0    0    NA 100.0  NA    2            55 #> 56   4   3.0    0    NA  12.0  NA    1            56 #> 57   4   6.0    0    NA  13.2  NA    1            57 #> 58   4   9.0    0    NA  14.4  NA    1            58 #> 59   4  24.0    0    NA   9.6  NA    1            59 #> 60   4  24.0    0    NA  30.0  NA    2            60 #> 61   4  36.0    0    NA   8.2  NA    1            61 #> 62   4  36.0    0    NA  24.0  NA    2            62 #> 63   4  48.0    0    NA   7.8  NA    1            63 #> 64   4  48.0    0    NA  13.0  NA    2            64 #> 65   4  72.0    0    NA   5.8  NA    1            65 #> 66   4  72.0    0    NA   9.0  NA    2            66 #> 67   4  96.0    0    NA   4.3  NA    1            67 #> 68   4  96.0    0    NA   9.0  NA    2            68 #> 69   4 120.0    0    NA   3.0  NA    1            69 #> 70   4 120.0    0    NA  11.0  NA    2            70 #> 71   4 144.0    0    NA  12.0  NA    2            71 #> 72   5   0.0    1  60.0    NA   1   NA            72 #> 73   5   0.0    0    NA  82.0  NA    2            73 #> 74   5   3.0    0    NA  11.1  NA    1            74 #> 75   5   6.0    0    NA  11.9  NA    1            75 #> 76   5   9.0    0    NA   9.8  NA    1            76 #> 77   5  12.0    0    NA  11.0  NA    1            77 #> 78   5  24.0    0    NA   8.5  NA    1            78 #> 79   5  24.0    0    NA  43.0  NA    2            79 #> 80   5  36.0    0    NA   7.6  NA    1            80 #> 81   5  36.0    0    NA  25.0  NA    2            81 #> 82   5  48.0    0    NA   5.4  NA    1            82 #> 83   5  48.0    0    NA  18.0  NA    2            83 #> 84   5  72.0    0    NA   4.5  NA    1            84 #> 85   5  72.0    0    NA  17.0  NA    2            85 #> 86   5  96.0    0    NA   3.3  NA    1            86 #> 87   5  96.0    0    NA  23.0  NA    2            87 #> 88   5 120.0    0    NA   2.3  NA    1            88 #> 89   5 120.0    0    NA  29.0  NA    2            89 #> 90   5 144.0    0    NA  41.0  NA    2            90 #> 91   6   0.0    1 113.0    NA   1   NA            91 #> 92   6   0.0    0    NA 100.0  NA    2            92 #> 93   6   6.0    0    NA   8.6  NA    1            93 #> 94   6  12.0    0    NA   8.6  NA    1            94 #> 95   6  24.0    0    NA   7.0  NA    1            95 #> 96   6  24.0    0    NA  34.0  NA    2            96 #> 97   6  36.0    0    NA   5.7  NA    1            97 #> 98   6  36.0    0    NA  23.0  NA    2            98 #> 99   6  48.0    0    NA   4.7  NA    1            99 #> 100  6  48.0    0    NA  20.0  NA    2           100 #> 101  6  72.0    0    NA   3.3  NA    1           101 #> 102  6  72.0    0    NA  16.0  NA    2           102 #> 103  6  96.0    0    NA   2.3  NA    1           103 #> 104  6  96.0    0    NA  17.0  NA    2           104 #> 105  6 120.0    0    NA   1.7  NA    1           105 #> 106  6 120.0    0    NA  18.0  NA    2           106 #> 107  6 144.0    0    NA  25.0  NA    2           107 #> 108  7   0.0    1  90.0    NA   1   NA           108 #> 109  7   3.0    0    NA  13.4  NA    1           109 #> 110  7   6.0    0    NA  12.4  NA    1           110 #> 111  7   9.0    0    NA  12.7  NA    1           111 #> 112  7  12.0    0    NA   8.8  NA    1           112 #> 113  7  24.0    0    NA   6.1  NA    1           113 #> 114  7  24.0    0    NA  36.0  NA    2           114 #> 115  7  36.0    0    NA   3.5  NA    1           115 #> 116  7  36.0    0    NA  33.0  NA    2           116 #> 117  7  48.0    0    NA   1.8  NA    1           117 #> 118  7  48.0    0    NA  28.0  NA    2           118 #> 119  7  72.0    0    NA   1.5  NA    1           119 #> 120  7  72.0    0    NA  52.0  NA    2           120 #> 121  7  96.0    0    NA   1.0  NA    1           121 #> 122  7  96.0    0    NA  80.0  NA    2           122 #> 123  7 120.0    0    NA  90.0  NA    2           123 #> 124  7 144.0    0    NA 100.0  NA    2           124 #> 125  8   0.0    1 135.0    NA   1   NA           125 #> 126  8   0.0    0    NA  88.0  NA    2           126 #> 127  8   2.0    0    NA  17.6  NA    1           127 #> 128  8   3.0    0    NA  17.3  NA    1           128 #> 129  8   6.0    0    NA  15.0  NA    1           129 #> 130  8   9.0    0    NA  15.0  NA    1           130 #> 131  8  12.0    0    NA  12.4  NA    1           131 #> 132  8  24.0    0    NA   7.9  NA    1           132 #> 133  8  24.0    0    NA  35.0  NA    2           133 #> 134  8  36.0    0    NA   7.9  NA    1           134 #> 135  8  36.0    0    NA  20.0  NA    2           135 #> 136  8  48.0    0    NA   5.1  NA    1           136 #> 137  8  48.0    0    NA  12.0  NA    2           137 #> 138  8  72.0    0    NA   3.6  NA    1           138 #> 139  8  72.0    0    NA  16.0  NA    2           139 #> 140  8  96.0    0    NA   2.4  NA    1           140 #> 141  8  96.0    0    NA  23.0  NA    2           141 #> 142  8 120.0    0    NA   2.0  NA    1           142 #> 143  8 120.0    0    NA  36.0  NA    2           143 #> 144  8 144.0    0    NA  48.0  NA    2           144 #> 145  9   0.0    1  75.0    NA   1   NA           145 #> 146  9   0.0    0    NA  92.0  NA    2           146 #> 147  9   0.5    0    NA   0.0  NA    1           147 #> 148  9   1.0    0    NA   1.0  NA    1           148 #> 149  9   2.0    0    NA   4.6  NA    1           149 #> 150  9   3.0    0    NA  12.7  NA    1           150 #> 151  9   3.0    0    NA   8.0  NA    1           151 #> 152  9   6.0    0    NA  12.7  NA    1           152 #> 153  9   6.0    0    NA  11.5  NA    1           153 #> 154  9   9.0    0    NA  12.9  NA    1           154 #> 155  9   9.0    0    NA  11.4  NA    1           155 #> 156  9  12.0    0    NA  11.4  NA    1           156 #> 157  9  12.0    0    NA  11.0  NA    1           157 #> 158  9  24.0    0    NA   9.1  NA    1           158 #> 159  9  24.0    0    NA  33.0  NA    2           159 #> 160  9  36.0    0    NA   8.2  NA    1           160 #> 161  9  36.0    0    NA  22.0  NA    2           161 #> 162  9  48.0    0    NA   5.9  NA    1           162 #> 163  9  48.0    0    NA  16.0  NA    2           163 #> 164  9  72.0    0    NA   3.6  NA    1           164 #> 165  9  72.0    0    NA  18.0  NA    2           165 #> 166  9  96.0    0    NA   1.7  NA    1           166 #> 167  9  96.0    0    NA  32.0  NA    2           167 #> 168  9 120.0    0    NA   1.1  NA    1           168 #> 169  9 120.0    0    NA  30.0  NA    2           169 #> 170  9 144.0    0    NA  45.0  NA    2           170 #> 171 10   0.0    1 105.0    NA   1   NA           171 #> 172 10   0.0    0    NA  90.0  NA    2           172 #> 173 10  24.0    0    NA   8.6  NA    1           173 #> 174 10  24.0    0    NA  39.0  NA    2           174 #> 175 10  36.0    0    NA   8.0  NA    1           175 #> 176 10  36.0    0    NA  22.0  NA    2           176 #> 177 10  48.0    0    NA   6.0  NA    1           177 #> 178 10  48.0    0    NA  17.0  NA    2           178 #> 179 10  72.0    0    NA   4.4  NA    1           179 #> 180 10  72.0    0    NA  17.0  NA    2           180 #> 181 10  96.0    0    NA   3.6  NA    1           181 #> 182 10  96.0    0    NA  22.0  NA    2           182 #> 183 10 120.0    0    NA   2.8  NA    1           183 #> 184 10 120.0    0    NA  25.0  NA    2           184 #> 185 10 144.0    0    NA  33.0  NA    2           185 #> 186 11   0.0    1 123.0    NA   1   NA           186 #> 187 11   0.0    0    NA 100.0  NA    2           187 #> 188 11   1.5    0    NA  11.4  NA    1           188 #> 189 11   3.0    0    NA  15.4  NA    1           189 #> 190 11   6.0    0    NA  17.5  NA    1           190 #> 191 11  12.0    0    NA  14.0  NA    1           191 #> 192 11  24.0    0    NA   9.0  NA    1           192 #> 193 11  24.0    0    NA  37.0  NA    2           193 #> 194 11  36.0    0    NA   8.9  NA    1           194 #> 195 11  36.0    0    NA  24.0  NA    2           195 #> 196 11  48.0    0    NA   6.6  NA    1           196 #> 197 11  48.0    0    NA  14.0  NA    2           197 #> 198 11  72.0    0    NA   4.2  NA    1           198 #> 199 11  72.0    0    NA  11.0  NA    2           199 #> 200 11  96.0    0    NA   3.6  NA    1           200 #> 201 11  96.0    0    NA  14.0  NA    2           201 #> 202 11 120.0    0    NA   2.6  NA    1           202 #> 203 11 120.0    0    NA  23.0  NA    2           203 #> 204 11 144.0    0    NA  33.0  NA    2           204 #> 205 12   0.0    1 113.0    NA   1   NA           205 #> 206 12   0.0    0    NA  85.0  NA    2           206 #> 207 12   1.5    0    NA   0.6  NA    1           207 #> 208 12   3.0    0    NA   2.8  NA    1           208 #> 209 12   6.0    0    NA  13.8  NA    1           209 #> 210 12   9.0    0    NA  15.0  NA    1           210 #> 211 12  24.0    0    NA  10.5  NA    1           211 #> 212 12  24.0    0    NA  25.0  NA    2           212 #> 213 12  36.0    0    NA   9.1  NA    1           213 #> 214 12  36.0    0    NA  15.0  NA    2           214 #> 215 12  48.0    0    NA   6.6  NA    1           215 #> 216 12  48.0    0    NA  11.0  NA    2           216 #> 217 12  72.0    0    NA   4.9  NA    1           217 #> 218 12  96.0    0    NA   2.4  NA    1           218 #> 219 12 120.0    0    NA   1.9  NA    1           219 #> 220 13   0.0    1 113.0    NA   1   NA           220 #> 221 13   0.0    0    NA  88.0  NA    2           221 #> 222 13   1.5    0    NA   3.6  NA    1           222 #> 223 13   3.0    0    NA  12.9  NA    1           223 #> 224 13   6.0    0    NA  12.9  NA    1           224 #> 225 13   9.0    0    NA  10.2  NA    1           225 #> 226 13  24.0    0    NA   6.4  NA    1           226 #> 227 13  24.0    0    NA  41.0  NA    2           227 #> 228 13  36.0    0    NA   6.9  NA    1           228 #> 229 13  36.0    0    NA  23.0  NA    2           229 #> 230 13  48.0    0    NA   4.5  NA    1           230 #> 231 13  48.0    0    NA  16.0  NA    2           231 #> 232 13  72.0    0    NA   3.2  NA    1           232 #> 233 13  72.0    0    NA  14.0  NA    2           233 #> 234 13  96.0    0    NA   2.4  NA    1           234 #> 235 13  96.0    0    NA  18.0  NA    2           235 #> 236 13 120.0    0    NA   1.3  NA    1           236 #> 237 13 120.0    0    NA  22.0  NA    2           237 #> 238 13 144.0    0    NA  35.0  NA    2           238 #> 239 14   0.0    1  75.0    NA   1   NA           239 #> 240 14   0.0    0    NA  85.0  NA    2           240 #> 241 14   0.5    0    NA   0.0  NA    1           241 #> 242 14   1.0    0    NA   2.7  NA    1           242 #> 243 14   2.0    0    NA  11.6  NA    1           243 #> 244 14   3.0    0    NA  11.6  NA    1           244 #> 245 14   6.0    0    NA  11.3  NA    1           245 #> 246 14   9.0    0    NA   9.7  NA    1           246 #> 247 14  24.0    0    NA   6.5  NA    1           247 #> 248 14  24.0    0    NA  32.0  NA    2           248 #> 249 14  36.0    0    NA   5.2  NA    1           249 #> 250 14  36.0    0    NA  22.0  NA    2           250 #> 251 14  48.0    0    NA   3.6  NA    1           251 #> 252 14  48.0    0    NA  21.0  NA    2           252 #> 253 14  72.0    0    NA   2.4  NA    1           253 #> 254 14  72.0    0    NA  28.0  NA    2           254 #> 255 14  96.0    0    NA   0.9  NA    1           255 #> 256 14  96.0    0    NA  38.0  NA    2           256 #> 257 14 120.0    0    NA  46.0  NA    2           257 #> 258 14 144.0    0    NA  65.0  NA    2           258 #> 259 15   0.0    1  85.0    NA   1   NA           259 #> 260 15   0.0    0    NA 100.0  NA    2           260 #> 261 15   1.0    0    NA   6.6  NA    1           261 #> 262 15   3.0    0    NA  11.9  NA    1           262 #> 263 15   6.0    0    NA  11.7  NA    1           263 #> 264 15   9.0    0    NA  12.2  NA    1           264 #> 265 15  24.0    0    NA   8.1  NA    1           265 #> 266 15  24.0    0    NA  43.0  NA    2           266 #> 267 15  36.0    0    NA   7.4  NA    1           267 #> 268 15  36.0    0    NA  26.0  NA    2           268 #> 269 15  48.0    0    NA   6.8  NA    1           269 #> 270 15  48.0    0    NA  15.0  NA    2           270 #> 271 15  72.0    0    NA   5.3  NA    1           271 #> 272 15  72.0    0    NA  13.0  NA    2           272 #> 273 15  96.0    0    NA   3.0  NA    1           273 #> 274 15  96.0    0    NA  21.0  NA    2           274 #> 275 15 120.0    0    NA   2.0  NA    1           275 #> 276 15 120.0    0    NA  28.0  NA    2           276 #> 277 15 144.0    0    NA  39.0  NA    2           277 #> 278 16   0.0    1  87.0    NA   1   NA           278 #> 279 16   0.0    0    NA 100.0  NA    2           279 #> 280 16  24.0    0    NA  10.4  NA    1           280 #> 281 16  24.0    0    NA  42.0  NA    2           281 #> 282 16  36.0    0    NA   8.9  NA    1           282 #> 283 16  36.0    0    NA  32.0  NA    2           283 #> 284 16  48.0    0    NA   7.0  NA    1           284 #> 285 16  48.0    0    NA  26.0  NA    2           285 #> 286 16  72.0    0    NA   4.4  NA    1           286 #> 287 16  72.0    0    NA  31.0  NA    2           287 #> 288 16  96.0    0    NA   3.2  NA    1           288 #> 289 16  96.0    0    NA  33.0  NA    2           289 #> 290 16 120.0    0    NA   2.4  NA    1           290 #> 291 16 120.0    0    NA  54.0  NA    2           291 #> 292 17   0.0    1 117.0    NA   1   NA           292 #> 293 17   0.0    0    NA 100.0  NA    2           293 #> 294 17  24.0    0    NA   7.6  NA    1           294 #> 295 17  24.0    0    NA  35.0  NA    2           295 #> 296 17  36.0    0    NA   6.4  NA    1           296 #> 297 17  36.0    0    NA  23.0  NA    2           297 #> 298 17  48.0    0    NA   6.0  NA    1           298 #> 299 17  48.0    0    NA  17.0  NA    2           299 #> 300 17  72.0    0    NA   4.0  NA    1           300 #> 301 17  72.0    0    NA  18.0  NA    2           301 #> 302 17  96.0    0    NA   3.1  NA    1           302 #> 303 17  96.0    0    NA  18.0  NA    2           303 #> 304 17 120.0    0    NA   2.0  NA    1           304 #> 305 17 120.0    0    NA  21.0  NA    2           305 #> 306 18   0.0    1 112.0    NA   1   NA           306 #> 307 18   0.0    0    NA 100.0  NA    2           307 #> 308 18  24.0    0    NA   7.6  NA    1           308 #> 309 18  24.0    0    NA  32.0  NA    2           309 #> 310 18  36.0    0    NA   6.6  NA    1           310 #> 311 18  36.0    0    NA  20.0  NA    2           311 #> 312 18  48.0    0    NA   5.4  NA    1           312 #> 313 18  48.0    0    NA  18.0  NA    2           313 #> 314 18  72.0    0    NA   3.4  NA    1           314 #> 315 18  72.0    0    NA  18.0  NA    2           315 #> 316 18  96.0    0    NA   1.2  NA    1           316 #> 317 18  96.0    0    NA  19.0  NA    2           317 #> 318 18 120.0    0    NA   0.9  NA    1           318 #> 319 18 120.0    0    NA  29.0  NA    2           319 #> 320 19   0.0    1  95.5    NA   1   NA           320 #> 321 19   0.0    0    NA 100.0  NA    2           321 #> 322 19  24.0    0    NA   6.6  NA    1           322 #> 323 19  24.0    0    NA  33.0  NA    2           323 #> 324 19  36.0    0    NA   5.3  NA    1           324 #> 325 19  36.0    0    NA  28.0  NA    2           325 #> 326 19  48.0    0    NA   3.6  NA    1           326 #> 327 19  48.0    0    NA  18.0  NA    2           327 #> 328 19  72.0    0    NA   2.7  NA    1           328 #> 329 19  72.0    0    NA  18.0  NA    2           329 #> 330 19  96.0    0    NA   1.4  NA    1           330 #> 331 19  96.0    0    NA  17.0  NA    2           331 #> 332 19 120.0    0    NA   1.1  NA    1           332 #> 333 19 120.0    0    NA  26.0  NA    2           333 #> 334 20   0.0    1  88.5    NA   1   NA           334 #> 335 20   0.0    0    NA 100.0  NA    2           335 #> 336 20  24.0    0    NA   9.6  NA    1           336 #> 337 20  24.0    0    NA  41.0  NA    2           337 #> 338 20  36.0    0    NA   8.0  NA    1           338 #> 339 20  36.0    0    NA  30.0  NA    2           339 #> 340 20  48.0    0    NA   6.6  NA    1           340 #> 341 20  48.0    0    NA  22.0  NA    2           341 #> 342 20  72.0    0    NA   5.6  NA    1           342 #> 343 20  72.0    0    NA  23.0  NA    2           343 #> 344 20  96.0    0    NA   3.5  NA    1           344 #> 345 20  96.0    0    NA  23.0  NA    2           345 #> 346 20 120.0    0    NA   2.3  NA    1           346 #> 347 20 120.0    0    NA  35.0  NA    2           347 #> 348 21   0.0    1  93.0    NA   1   NA           348 #> 349 21   0.0    0    NA 100.0  NA    2           349 #> 350 21  24.0    0    NA   7.3  NA    1           350 #> 351 21  24.0    0    NA  46.0  NA    2           351 #> 352 21  36.0    0    NA   6.1  NA    1           352 #> 353 21  36.0    0    NA  27.0  NA    2           353 #> 354 21  48.0    0    NA   4.3  NA    1           354 #> 355 21  48.0    0    NA  22.0  NA    2           355 #> 356 21  72.0    0    NA   3.2  NA    1           356 #> 357 21  72.0    0    NA  36.0  NA    2           357 #> 358 21  96.0    0    NA   2.3  NA    1           358 #> 359 21  96.0    0    NA  40.0  NA    2           359 #> 360 21 120.0    0    NA   1.9  NA    1           360 #> 361 21 120.0    0    NA  44.0  NA    2           361 #> 362 22   0.0    1  87.0    NA   1   NA           362 #> 363 22   0.0    0    NA 100.0  NA    2           363 #> 364 22  24.0    0    NA   8.9  NA    1           364 #> 365 22  24.0    0    NA  35.0  NA    2           365 #> 366 22  36.0    0    NA   8.4  NA    1           366 #> 367 22  36.0    0    NA  27.0  NA    2           367 #> 368 22  48.0    0    NA   8.0  NA    1           368 #> 369 22  48.0    0    NA  23.0  NA    2           369 #> 370 22  72.0    0    NA   4.4  NA    1           370 #> 371 22  72.0    0    NA  27.0  NA    2           371 #> 372 22  96.0    0    NA   3.2  NA    1           372 #> 373 22  96.0    0    NA  43.0  NA    2           373 #> 374 22 120.0    0    NA   1.7  NA    1           374 #> 375 22 120.0    0    NA  43.0  NA    2           375 #> 376 23   0.0    1 110.0    NA   1   NA           376 #> 377 23   0.0    0    NA 100.0  NA    2           377 #> 378 23  24.0    0    NA   9.8  NA    1           378 #> 379 23  24.0    0    NA  34.0  NA    2           379 #> 380 23  36.0    0    NA   8.4  NA    1           380 #> 381 23  36.0    0    NA  24.0  NA    2           381 #> 382 23  48.0    0    NA   6.6  NA    1           382 #> 383 23  48.0    0    NA  15.0  NA    2           383 #> 384 23  72.0    0    NA   4.8  NA    1           384 #> 385 23  72.0    0    NA  15.0  NA    2           385 #> 386 23  96.0    0    NA   3.2  NA    1           386 #> 387 23  96.0    0    NA  19.0  NA    2           387 #> 388 23 120.0    0    NA   2.4  NA    1           388 #> 389 23 120.0    0    NA  19.0  NA    2           389 #> 390 24   0.0    1 115.0    NA   1   NA           390 #> 391 24   0.0    0    NA  88.0  NA    2           391 #> 392 24  24.0    0    NA   8.2  NA    1           392 #> 393 24  24.0    0    NA  37.0  NA    2           393 #> 394 24  36.0    0    NA   7.5  NA    1           394 #> 395 24  36.0    0    NA  20.0  NA    2           395 #> 396 24  48.0    0    NA   6.8  NA    1           396 #> 397 24  48.0    0    NA  20.0  NA    2           397 #> 398 24  72.0    0    NA   5.5  NA    1           398 #> 399 24  72.0    0    NA  26.0  NA    2           399 #> 400 24  96.0    0    NA   4.5  NA    1           400 #> 401 24  96.0    0    NA  28.0  NA    2           401 #> 402 24 120.0    0    NA   3.7  NA    1           402 #> 403 24 120.0    0    NA  50.0  NA    2           403 #> 404 25   0.0    1 112.0    NA   1   NA           404 #> 405 25   0.0    0    NA 100.0  NA    2           405 #> 406 25  24.0    0    NA  11.0  NA    1           406 #> 407 25  24.0    0    NA  32.0  NA    2           407 #> 408 25  36.0    0    NA  10.0  NA    1           408 #> 409 25  36.0    0    NA  20.0  NA    2           409 #> 410 25  48.0    0    NA   8.2  NA    1           410 #> 411 25  48.0    0    NA  17.0  NA    2           411 #> 412 25  72.0    0    NA   6.0  NA    1           412 #> 413 25  72.0    0    NA  19.0  NA    2           413 #> 414 25  96.0    0    NA   3.7  NA    1           414 #> 415 25  96.0    0    NA  21.0  NA    2           415 #> 416 25 120.0    0    NA   2.6  NA    1           416 #> 417 25 120.0    0    NA  30.0  NA    2           417 #> 418 26   0.0    1 120.0    NA   1   NA           418 #> 419 26   0.0    0    NA 100.0  NA    2           419 #> 420 26  24.0    0    NA  10.0  NA    1           420 #> 421 26  24.0    0    NA  41.0  NA    2           421 #> 422 26  36.0    0    NA   9.0  NA    1           422 #> 423 26  36.0    0    NA  28.0  NA    2           423 #> 424 26  48.0    0    NA   7.3  NA    1           424 #> 425 26  48.0    0    NA  19.0  NA    2           425 #> 426 26  72.0    0    NA   5.2  NA    1           426 #> 427 26  72.0    0    NA  17.0  NA    2           427 #> 428 26  96.0    0    NA   3.7  NA    1           428 #> 429 26  96.0    0    NA  17.0  NA    2           429 #> 430 26 120.0    0    NA   2.7  NA    1           430 #> 431 26 120.0    0    NA  24.0  NA    2           431 #> 432 27   0.0    1 120.0    NA   1   NA           432 #> 433 27   0.0    0    NA 100.0  NA    2           433 #> 434 27  24.0    0    NA  11.8  NA    1           434 #> 435 27  24.0    0    NA  32.0  NA    2           435 #> 436 27  36.0    0    NA   9.2  NA    1           436 #> 437 27  36.0    0    NA  21.0  NA    2           437 #> 438 27  48.0    0    NA   7.7  NA    1           438 #> 439 27  48.0    0    NA  19.0  NA    2           439 #> 440 27  72.0    0    NA   4.9  NA    1           440 #> 441 27  72.0    0    NA  22.0  NA    2           441 #> 442 27  96.0    0    NA   3.4  NA    1           442 #> 443 27  96.0    0    NA  33.0  NA    2           443 #> 444 27 120.0    0    NA   2.7  NA    1           444 #> 445 27 120.0    0    NA  46.0  NA    2           445 #> 446 28   0.0    1 120.0    NA   1   NA           446 #> 447 28   0.0    0    NA 100.0  NA    2           447 #> 448 28  24.0    0    NA  10.1  NA    1           448 #> 449 28  24.0    0    NA  39.0  NA    2           449 #> 450 28  36.0    0    NA   8.0  NA    1           450 #> 451 28  36.0    0    NA  25.0  NA    2           451 #> 452 28  48.0    0    NA   6.0  NA    1           452 #> 453 28  48.0    0    NA  16.0  NA    2           453 #> 454 28  72.0    0    NA   4.9  NA    1           454 #> 455 28  72.0    0    NA  14.0  NA    2           455 #> 456 28  96.0    0    NA   3.4  NA    1           456 #> 457 28  96.0    0    NA  15.0  NA    2           457 #> 458 28 120.0    0    NA   2.0  NA    1           458 #> 459 28 120.0    0    NA  20.0  NA    2           459 #> 460 29   0.0    1 153.0    NA   1   NA           460 #> 461 29   0.0    0    NA  86.0  NA    2           461 #> 462 29  24.0    0    NA   8.3  NA    1           462 #> 463 29  24.0    0    NA  35.0  NA    2           463 #> 464 29  36.0    0    NA   7.0  NA    1           464 #> 465 29  36.0    0    NA  21.0  NA    2           465 #> 466 29  48.0    0    NA   5.6  NA    1           466 #> 467 29  48.0    0    NA  18.0  NA    2           467 #> 468 29  72.0    0    NA   4.1  NA    1           468 #> 469 29  72.0    0    NA  20.0  NA    2           469 #> 470 29  96.0    0    NA   3.1  NA    1           470 #> 471 29  96.0    0    NA  29.0  NA    2           471 #> 472 29 120.0    0    NA   2.2  NA    1           472 #> 473 29 120.0    0    NA  41.0  NA    2           473 #> 474 30   0.0    1 105.0    NA   1   NA           474 #> 475 30   0.0    0    NA 100.0  NA    2           475 #> 476 30  24.0    0    NA   9.9  NA    1           476 #> 477 30  24.0    0    NA  45.0  NA    2           477 #> 478 30  36.0    0    NA   7.5  NA    1           478 #> 479 30  36.0    0    NA  24.0  NA    2           479 #> 480 30  48.0    0    NA   6.5  NA    1           480 #> 481 30  48.0    0    NA  23.0  NA    2           481 #> 482 30  72.0    0    NA   4.1  NA    1           482 #> 483 30  72.0    0    NA  26.0  NA    2           483 #> 484 30  96.0    0    NA   2.9  NA    1           484 #> 485 30  96.0    0    NA  28.0  NA    2           485 #> 486 30 120.0    0    NA   2.3  NA    1           486 #> 487 30 120.0    0    NA  39.0  NA    2           487 #> 488 31   0.0    1 125.0    NA   1   NA           488 #> 489 31   0.0    0    NA 100.0  NA    2           489 #> 490 31  24.0    0    NA   9.5  NA    1           490 #> 491 31  24.0    0    NA  45.0  NA    2           491 #> 492 31  36.0    0    NA   7.8  NA    1           492 #> 493 31  36.0    0    NA  30.0  NA    2           493 #> 494 31  48.0    0    NA   6.4  NA    1           494 #> 495 31  48.0    0    NA  24.0  NA    2           495 #> 496 31  72.0    0    NA   4.5  NA    1           496 #> 497 31  72.0    0    NA  22.0  NA    2           497 #> 498 31  96.0    0    NA   3.4  NA    1           498 #> 499 31  96.0    0    NA  28.0  NA    2           499 #> 500 31 120.0    0    NA   2.5  NA    1           500 #> 501 31 120.0    0    NA  42.0  NA    2           501 #> 502 32   0.0    1  93.0    NA   1   NA           502 #> 503 32   0.0    0    NA 100.0  NA    2           503 #> 504 32  24.0    0    NA   8.9  NA    1           504 #> 505 32  24.0    0    NA  36.0  NA    2           505 #> 506 32  36.0    0    NA   7.7  NA    1           506 #> 507 32  36.0    0    NA  27.0  NA    2           507 #> 508 32  48.0    0    NA   6.9  NA    1           508 #> 509 32  48.0    0    NA  24.0  NA    2           509 #> 510 32  72.0    0    NA   4.4  NA    1           510 #> 511 32  72.0    0    NA  23.0  NA    2           511 #> 512 32  96.0    0    NA   3.5  NA    1           512 #> 513 32  96.0    0    NA  20.0  NA    2           513 #> 514 32 120.0    0    NA   2.5  NA    1           514 #> 515 32 120.0    0    NA  22.0  NA    2           515  bblDatToMrgsolve(pk.turnover.emax3, nlmixr2data::warfarin) #>   #>   #>     ID  TIME EVID   AMT II    DV CMT DVID SS nlmixrRowNums #> 1    1   0.0    1 100.0  0    NA   1    0  0             1 #> 2    1   0.5    0    NA  0   0.0   5    1  0             2 #> 3    1   1.0    0    NA  0   1.9   5    1  0             3 #> 4    1   2.0    0    NA  0   3.3   5    1  0             4 #> 5    1   3.0    0    NA  0   6.6   5    1  0             5 #> 6    1   6.0    0    NA  0   9.1   5    1  0             6 #> 7    1   9.0    0    NA  0  10.8   5    1  0             7 #> 8    1  12.0    0    NA  0   8.6   5    1  0             8 #> 9    1  24.0    0    NA  0   5.6   5    1  0             9 #> 10   1  24.0    0    NA  0  44.0   6    2  0            10 #> 11   1  36.0    0    NA  0   4.0   5    1  0            11 #> 12   1  36.0    0    NA  0  27.0   6    2  0            12 #> 13   1  48.0    0    NA  0   2.7   5    1  0            13 #> 14   1  48.0    0    NA  0  28.0   6    2  0            14 #> 15   1  72.0    0    NA  0   0.8   5    1  0            15 #> 16   1  72.0    0    NA  0  31.0   6    2  0            16 #> 17   1  96.0    0    NA  0  60.0   6    2  0            17 #> 18   1 120.0    0    NA  0  65.0   6    2  0            18 #> 19   1 144.0    0    NA  0  71.0   6    2  0            19 #> 20   2   0.0    1 100.0  0    NA   1    0  0            20 #> 21   2   0.0    0    NA  0 100.0   6    2  0            21 #> 22   2  24.0    0    NA  0   9.2   5    1  0            22 #> 23   2  24.0    0    NA  0  49.0   6    2  0            23 #> 24   2  36.0    0    NA  0   8.5   5    1  0            24 #> 25   2  36.0    0    NA  0  32.0   6    2  0            25 #> 26   2  48.0    0    NA  0   6.4   5    1  0            26 #> 27   2  48.0    0    NA  0  26.0   6    2  0            27 #> 28   2  72.0    0    NA  0   4.8   5    1  0            28 #> 29   2  72.0    0    NA  0  22.0   6    2  0            29 #> 30   2  96.0    0    NA  0   3.1   5    1  0            30 #> 31   2  96.0    0    NA  0  28.0   6    2  0            31 #> 32   2 120.0    0    NA  0   2.5   5    1  0            32 #> 33   2 120.0    0    NA  0  33.0   6    2  0            33 #> 34   3   0.0    1 100.0  0    NA   1    0  0            34 #> 35   3   0.0    0    NA  0 100.0   6    2  0            35 #> 36   3   0.5    0    NA  0   0.0   5    1  0            36 #> 37   3   2.0    0    NA  0   8.4   5    1  0            37 #> 38   3   3.0    0    NA  0   9.7   5    1  0            38 #> 39   3   6.0    0    NA  0   9.8   5    1  0            39 #> 40   3  12.0    0    NA  0  11.0   5    1  0            40 #> 41   3  24.0    0    NA  0   8.3   5    1  0            41 #> 42   3  24.0    0    NA  0  46.0   6    2  0            42 #> 43   3  36.0    0    NA  0   7.7   5    1  0            43 #> 44   3  36.0    0    NA  0  22.0   6    2  0            44 #> 45   3  48.0    0    NA  0   6.3   5    1  0            45 #> 46   3  48.0    0    NA  0  19.0   6    2  0            46 #> 47   3  72.0    0    NA  0   4.1   5    1  0            47 #> 48   3  72.0    0    NA  0  20.0   6    2  0            48 #> 49   3  96.0    0    NA  0   3.0   5    1  0            49 #> 50   3  96.0    0    NA  0  42.0   6    2  0            50 #> 51   3 120.0    0    NA  0   1.4   5    1  0            51 #> 52   3 120.0    0    NA  0  49.0   6    2  0            52 #> 53   3 144.0    0    NA  0  54.0   6    2  0            53 #> 54   4   0.0    1 120.0  0    NA   1    0  0            54 #> 55   4   0.0    0    NA  0 100.0   6    2  0            55 #> 56   4   3.0    0    NA  0  12.0   5    1  0            56 #> 57   4   6.0    0    NA  0  13.2   5    1  0            57 #> 58   4   9.0    0    NA  0  14.4   5    1  0            58 #> 59   4  24.0    0    NA  0   9.6   5    1  0            59 #> 60   4  24.0    0    NA  0  30.0   6    2  0            60 #> 61   4  36.0    0    NA  0   8.2   5    1  0            61 #> 62   4  36.0    0    NA  0  24.0   6    2  0            62 #> 63   4  48.0    0    NA  0   7.8   5    1  0            63 #> 64   4  48.0    0    NA  0  13.0   6    2  0            64 #> 65   4  72.0    0    NA  0   5.8   5    1  0            65 #> 66   4  72.0    0    NA  0   9.0   6    2  0            66 #> 67   4  96.0    0    NA  0   4.3   5    1  0            67 #> 68   4  96.0    0    NA  0   9.0   6    2  0            68 #> 69   4 120.0    0    NA  0   3.0   5    1  0            69 #> 70   4 120.0    0    NA  0  11.0   6    2  0            70 #> 71   4 144.0    0    NA  0  12.0   6    2  0            71 #> 72   5   0.0    1  60.0  0    NA   1    0  0            72 #> 73   5   0.0    0    NA  0  82.0   6    2  0            73 #> 74   5   3.0    0    NA  0  11.1   5    1  0            74 #> 75   5   6.0    0    NA  0  11.9   5    1  0            75 #> 76   5   9.0    0    NA  0   9.8   5    1  0            76 #> 77   5  12.0    0    NA  0  11.0   5    1  0            77 #> 78   5  24.0    0    NA  0   8.5   5    1  0            78 #> 79   5  24.0    0    NA  0  43.0   6    2  0            79 #> 80   5  36.0    0    NA  0   7.6   5    1  0            80 #> 81   5  36.0    0    NA  0  25.0   6    2  0            81 #> 82   5  48.0    0    NA  0   5.4   5    1  0            82 #> 83   5  48.0    0    NA  0  18.0   6    2  0            83 #> 84   5  72.0    0    NA  0   4.5   5    1  0            84 #> 85   5  72.0    0    NA  0  17.0   6    2  0            85 #> 86   5  96.0    0    NA  0   3.3   5    1  0            86 #> 87   5  96.0    0    NA  0  23.0   6    2  0            87 #> 88   5 120.0    0    NA  0   2.3   5    1  0            88 #> 89   5 120.0    0    NA  0  29.0   6    2  0            89 #> 90   5 144.0    0    NA  0  41.0   6    2  0            90 #> 91   6   0.0    1 113.0  0    NA   1    0  0            91 #> 92   6   0.0    0    NA  0 100.0   6    2  0            92 #> 93   6   6.0    0    NA  0   8.6   5    1  0            93 #> 94   6  12.0    0    NA  0   8.6   5    1  0            94 #> 95   6  24.0    0    NA  0   7.0   5    1  0            95 #> 96   6  24.0    0    NA  0  34.0   6    2  0            96 #> 97   6  36.0    0    NA  0   5.7   5    1  0            97 #> 98   6  36.0    0    NA  0  23.0   6    2  0            98 #> 99   6  48.0    0    NA  0   4.7   5    1  0            99 #> 100  6  48.0    0    NA  0  20.0   6    2  0           100 #> 101  6  72.0    0    NA  0   3.3   5    1  0           101 #> 102  6  72.0    0    NA  0  16.0   6    2  0           102 #> 103  6  96.0    0    NA  0   2.3   5    1  0           103 #> 104  6  96.0    0    NA  0  17.0   6    2  0           104 #> 105  6 120.0    0    NA  0   1.7   5    1  0           105 #> 106  6 120.0    0    NA  0  18.0   6    2  0           106 #> 107  6 144.0    0    NA  0  25.0   6    2  0           107 #> 108  7   0.0    1  90.0  0    NA   1    0  0           108 #> 109  7   3.0    0    NA  0  13.4   5    1  0           109 #> 110  7   6.0    0    NA  0  12.4   5    1  0           110 #> 111  7   9.0    0    NA  0  12.7   5    1  0           111 #> 112  7  12.0    0    NA  0   8.8   5    1  0           112 #> 113  7  24.0    0    NA  0   6.1   5    1  0           113 #> 114  7  24.0    0    NA  0  36.0   6    2  0           114 #> 115  7  36.0    0    NA  0   3.5   5    1  0           115 #> 116  7  36.0    0    NA  0  33.0   6    2  0           116 #> 117  7  48.0    0    NA  0   1.8   5    1  0           117 #> 118  7  48.0    0    NA  0  28.0   6    2  0           118 #> 119  7  72.0    0    NA  0   1.5   5    1  0           119 #> 120  7  72.0    0    NA  0  52.0   6    2  0           120 #> 121  7  96.0    0    NA  0   1.0   5    1  0           121 #> 122  7  96.0    0    NA  0  80.0   6    2  0           122 #> 123  7 120.0    0    NA  0  90.0   6    2  0           123 #> 124  7 144.0    0    NA  0 100.0   6    2  0           124 #> 125  8   0.0    1 135.0  0    NA   1    0  0           125 #> 126  8   0.0    0    NA  0  88.0   6    2  0           126 #> 127  8   2.0    0    NA  0  17.6   5    1  0           127 #> 128  8   3.0    0    NA  0  17.3   5    1  0           128 #> 129  8   6.0    0    NA  0  15.0   5    1  0           129 #> 130  8   9.0    0    NA  0  15.0   5    1  0           130 #> 131  8  12.0    0    NA  0  12.4   5    1  0           131 #> 132  8  24.0    0    NA  0   7.9   5    1  0           132 #> 133  8  24.0    0    NA  0  35.0   6    2  0           133 #> 134  8  36.0    0    NA  0   7.9   5    1  0           134 #> 135  8  36.0    0    NA  0  20.0   6    2  0           135 #> 136  8  48.0    0    NA  0   5.1   5    1  0           136 #> 137  8  48.0    0    NA  0  12.0   6    2  0           137 #> 138  8  72.0    0    NA  0   3.6   5    1  0           138 #> 139  8  72.0    0    NA  0  16.0   6    2  0           139 #> 140  8  96.0    0    NA  0   2.4   5    1  0           140 #> 141  8  96.0    0    NA  0  23.0   6    2  0           141 #> 142  8 120.0    0    NA  0   2.0   5    1  0           142 #> 143  8 120.0    0    NA  0  36.0   6    2  0           143 #> 144  8 144.0    0    NA  0  48.0   6    2  0           144 #> 145  9   0.0    1  75.0  0    NA   1    0  0           145 #> 146  9   0.0    0    NA  0  92.0   6    2  0           146 #> 147  9   0.5    0    NA  0   0.0   5    1  0           147 #> 148  9   1.0    0    NA  0   1.0   5    1  0           148 #> 149  9   2.0    0    NA  0   4.6   5    1  0           149 #> 150  9   3.0    0    NA  0  12.7   5    1  0           150 #> 151  9   3.0    0    NA  0   8.0   5    1  0           151 #> 152  9   6.0    0    NA  0  12.7   5    1  0           152 #> 153  9   6.0    0    NA  0  11.5   5    1  0           153 #> 154  9   9.0    0    NA  0  12.9   5    1  0           154 #> 155  9   9.0    0    NA  0  11.4   5    1  0           155 #> 156  9  12.0    0    NA  0  11.4   5    1  0           156 #> 157  9  12.0    0    NA  0  11.0   5    1  0           157 #> 158  9  24.0    0    NA  0   9.1   5    1  0           158 #> 159  9  24.0    0    NA  0  33.0   6    2  0           159 #> 160  9  36.0    0    NA  0   8.2   5    1  0           160 #> 161  9  36.0    0    NA  0  22.0   6    2  0           161 #> 162  9  48.0    0    NA  0   5.9   5    1  0           162 #> 163  9  48.0    0    NA  0  16.0   6    2  0           163 #> 164  9  72.0    0    NA  0   3.6   5    1  0           164 #> 165  9  72.0    0    NA  0  18.0   6    2  0           165 #> 166  9  96.0    0    NA  0   1.7   5    1  0           166 #> 167  9  96.0    0    NA  0  32.0   6    2  0           167 #> 168  9 120.0    0    NA  0   1.1   5    1  0           168 #> 169  9 120.0    0    NA  0  30.0   6    2  0           169 #> 170  9 144.0    0    NA  0  45.0   6    2  0           170 #> 171 10   0.0    1 105.0  0    NA   1    0  0           171 #> 172 10   0.0    0    NA  0  90.0   6    2  0           172 #> 173 10  24.0    0    NA  0   8.6   5    1  0           173 #> 174 10  24.0    0    NA  0  39.0   6    2  0           174 #> 175 10  36.0    0    NA  0   8.0   5    1  0           175 #> 176 10  36.0    0    NA  0  22.0   6    2  0           176 #> 177 10  48.0    0    NA  0   6.0   5    1  0           177 #> 178 10  48.0    0    NA  0  17.0   6    2  0           178 #> 179 10  72.0    0    NA  0   4.4   5    1  0           179 #> 180 10  72.0    0    NA  0  17.0   6    2  0           180 #> 181 10  96.0    0    NA  0   3.6   5    1  0           181 #> 182 10  96.0    0    NA  0  22.0   6    2  0           182 #> 183 10 120.0    0    NA  0   2.8   5    1  0           183 #> 184 10 120.0    0    NA  0  25.0   6    2  0           184 #> 185 10 144.0    0    NA  0  33.0   6    2  0           185 #> 186 11   0.0    1 123.0  0    NA   1    0  0           186 #> 187 11   0.0    0    NA  0 100.0   6    2  0           187 #> 188 11   1.5    0    NA  0  11.4   5    1  0           188 #> 189 11   3.0    0    NA  0  15.4   5    1  0           189 #> 190 11   6.0    0    NA  0  17.5   5    1  0           190 #> 191 11  12.0    0    NA  0  14.0   5    1  0           191 #> 192 11  24.0    0    NA  0   9.0   5    1  0           192 #> 193 11  24.0    0    NA  0  37.0   6    2  0           193 #> 194 11  36.0    0    NA  0   8.9   5    1  0           194 #> 195 11  36.0    0    NA  0  24.0   6    2  0           195 #> 196 11  48.0    0    NA  0   6.6   5    1  0           196 #> 197 11  48.0    0    NA  0  14.0   6    2  0           197 #> 198 11  72.0    0    NA  0   4.2   5    1  0           198 #> 199 11  72.0    0    NA  0  11.0   6    2  0           199 #> 200 11  96.0    0    NA  0   3.6   5    1  0           200 #> 201 11  96.0    0    NA  0  14.0   6    2  0           201 #> 202 11 120.0    0    NA  0   2.6   5    1  0           202 #> 203 11 120.0    0    NA  0  23.0   6    2  0           203 #> 204 11 144.0    0    NA  0  33.0   6    2  0           204 #> 205 12   0.0    1 113.0  0    NA   1    0  0           205 #> 206 12   0.0    0    NA  0  85.0   6    2  0           206 #> 207 12   1.5    0    NA  0   0.6   5    1  0           207 #> 208 12   3.0    0    NA  0   2.8   5    1  0           208 #> 209 12   6.0    0    NA  0  13.8   5    1  0           209 #> 210 12   9.0    0    NA  0  15.0   5    1  0           210 #> 211 12  24.0    0    NA  0  10.5   5    1  0           211 #> 212 12  24.0    0    NA  0  25.0   6    2  0           212 #> 213 12  36.0    0    NA  0   9.1   5    1  0           213 #> 214 12  36.0    0    NA  0  15.0   6    2  0           214 #> 215 12  48.0    0    NA  0   6.6   5    1  0           215 #> 216 12  48.0    0    NA  0  11.0   6    2  0           216 #> 217 12  72.0    0    NA  0   4.9   5    1  0           217 #> 218 12  96.0    0    NA  0   2.4   5    1  0           218 #> 219 12 120.0    0    NA  0   1.9   5    1  0           219 #> 220 13   0.0    1 113.0  0    NA   1    0  0           220 #> 221 13   0.0    0    NA  0  88.0   6    2  0           221 #> 222 13   1.5    0    NA  0   3.6   5    1  0           222 #> 223 13   3.0    0    NA  0  12.9   5    1  0           223 #> 224 13   6.0    0    NA  0  12.9   5    1  0           224 #> 225 13   9.0    0    NA  0  10.2   5    1  0           225 #> 226 13  24.0    0    NA  0   6.4   5    1  0           226 #> 227 13  24.0    0    NA  0  41.0   6    2  0           227 #> 228 13  36.0    0    NA  0   6.9   5    1  0           228 #> 229 13  36.0    0    NA  0  23.0   6    2  0           229 #> 230 13  48.0    0    NA  0   4.5   5    1  0           230 #> 231 13  48.0    0    NA  0  16.0   6    2  0           231 #> 232 13  72.0    0    NA  0   3.2   5    1  0           232 #> 233 13  72.0    0    NA  0  14.0   6    2  0           233 #> 234 13  96.0    0    NA  0   2.4   5    1  0           234 #> 235 13  96.0    0    NA  0  18.0   6    2  0           235 #> 236 13 120.0    0    NA  0   1.3   5    1  0           236 #> 237 13 120.0    0    NA  0  22.0   6    2  0           237 #> 238 13 144.0    0    NA  0  35.0   6    2  0           238 #> 239 14   0.0    1  75.0  0    NA   1    0  0           239 #> 240 14   0.0    0    NA  0  85.0   6    2  0           240 #> 241 14   0.5    0    NA  0   0.0   5    1  0           241 #> 242 14   1.0    0    NA  0   2.7   5    1  0           242 #> 243 14   2.0    0    NA  0  11.6   5    1  0           243 #> 244 14   3.0    0    NA  0  11.6   5    1  0           244 #> 245 14   6.0    0    NA  0  11.3   5    1  0           245 #> 246 14   9.0    0    NA  0   9.7   5    1  0           246 #> 247 14  24.0    0    NA  0   6.5   5    1  0           247 #> 248 14  24.0    0    NA  0  32.0   6    2  0           248 #> 249 14  36.0    0    NA  0   5.2   5    1  0           249 #> 250 14  36.0    0    NA  0  22.0   6    2  0           250 #> 251 14  48.0    0    NA  0   3.6   5    1  0           251 #> 252 14  48.0    0    NA  0  21.0   6    2  0           252 #> 253 14  72.0    0    NA  0   2.4   5    1  0           253 #> 254 14  72.0    0    NA  0  28.0   6    2  0           254 #> 255 14  96.0    0    NA  0   0.9   5    1  0           255 #> 256 14  96.0    0    NA  0  38.0   6    2  0           256 #> 257 14 120.0    0    NA  0  46.0   6    2  0           257 #> 258 14 144.0    0    NA  0  65.0   6    2  0           258 #> 259 15   0.0    1  85.0  0    NA   1    0  0           259 #> 260 15   0.0    0    NA  0 100.0   6    2  0           260 #> 261 15   1.0    0    NA  0   6.6   5    1  0           261 #> 262 15   3.0    0    NA  0  11.9   5    1  0           262 #> 263 15   6.0    0    NA  0  11.7   5    1  0           263 #> 264 15   9.0    0    NA  0  12.2   5    1  0           264 #> 265 15  24.0    0    NA  0   8.1   5    1  0           265 #> 266 15  24.0    0    NA  0  43.0   6    2  0           266 #> 267 15  36.0    0    NA  0   7.4   5    1  0           267 #> 268 15  36.0    0    NA  0  26.0   6    2  0           268 #> 269 15  48.0    0    NA  0   6.8   5    1  0           269 #> 270 15  48.0    0    NA  0  15.0   6    2  0           270 #> 271 15  72.0    0    NA  0   5.3   5    1  0           271 #> 272 15  72.0    0    NA  0  13.0   6    2  0           272 #> 273 15  96.0    0    NA  0   3.0   5    1  0           273 #> 274 15  96.0    0    NA  0  21.0   6    2  0           274 #> 275 15 120.0    0    NA  0   2.0   5    1  0           275 #> 276 15 120.0    0    NA  0  28.0   6    2  0           276 #> 277 15 144.0    0    NA  0  39.0   6    2  0           277 #> 278 16   0.0    1  87.0  0    NA   1    0  0           278 #> 279 16   0.0    0    NA  0 100.0   6    2  0           279 #> 280 16  24.0    0    NA  0  10.4   5    1  0           280 #> 281 16  24.0    0    NA  0  42.0   6    2  0           281 #> 282 16  36.0    0    NA  0   8.9   5    1  0           282 #> 283 16  36.0    0    NA  0  32.0   6    2  0           283 #> 284 16  48.0    0    NA  0   7.0   5    1  0           284 #> 285 16  48.0    0    NA  0  26.0   6    2  0           285 #> 286 16  72.0    0    NA  0   4.4   5    1  0           286 #> 287 16  72.0    0    NA  0  31.0   6    2  0           287 #> 288 16  96.0    0    NA  0   3.2   5    1  0           288 #> 289 16  96.0    0    NA  0  33.0   6    2  0           289 #> 290 16 120.0    0    NA  0   2.4   5    1  0           290 #> 291 16 120.0    0    NA  0  54.0   6    2  0           291 #> 292 17   0.0    1 117.0  0    NA   1    0  0           292 #> 293 17   0.0    0    NA  0 100.0   6    2  0           293 #> 294 17  24.0    0    NA  0   7.6   5    1  0           294 #> 295 17  24.0    0    NA  0  35.0   6    2  0           295 #> 296 17  36.0    0    NA  0   6.4   5    1  0           296 #> 297 17  36.0    0    NA  0  23.0   6    2  0           297 #> 298 17  48.0    0    NA  0   6.0   5    1  0           298 #> 299 17  48.0    0    NA  0  17.0   6    2  0           299 #> 300 17  72.0    0    NA  0   4.0   5    1  0           300 #> 301 17  72.0    0    NA  0  18.0   6    2  0           301 #> 302 17  96.0    0    NA  0   3.1   5    1  0           302 #> 303 17  96.0    0    NA  0  18.0   6    2  0           303 #> 304 17 120.0    0    NA  0   2.0   5    1  0           304 #> 305 17 120.0    0    NA  0  21.0   6    2  0           305 #> 306 18   0.0    1 112.0  0    NA   1    0  0           306 #> 307 18   0.0    0    NA  0 100.0   6    2  0           307 #> 308 18  24.0    0    NA  0   7.6   5    1  0           308 #> 309 18  24.0    0    NA  0  32.0   6    2  0           309 #> 310 18  36.0    0    NA  0   6.6   5    1  0           310 #> 311 18  36.0    0    NA  0  20.0   6    2  0           311 #> 312 18  48.0    0    NA  0   5.4   5    1  0           312 #> 313 18  48.0    0    NA  0  18.0   6    2  0           313 #> 314 18  72.0    0    NA  0   3.4   5    1  0           314 #> 315 18  72.0    0    NA  0  18.0   6    2  0           315 #> 316 18  96.0    0    NA  0   1.2   5    1  0           316 #> 317 18  96.0    0    NA  0  19.0   6    2  0           317 #> 318 18 120.0    0    NA  0   0.9   5    1  0           318 #> 319 18 120.0    0    NA  0  29.0   6    2  0           319 #> 320 19   0.0    1  95.5  0    NA   1    0  0           320 #> 321 19   0.0    0    NA  0 100.0   6    2  0           321 #> 322 19  24.0    0    NA  0   6.6   5    1  0           322 #> 323 19  24.0    0    NA  0  33.0   6    2  0           323 #> 324 19  36.0    0    NA  0   5.3   5    1  0           324 #> 325 19  36.0    0    NA  0  28.0   6    2  0           325 #> 326 19  48.0    0    NA  0   3.6   5    1  0           326 #> 327 19  48.0    0    NA  0  18.0   6    2  0           327 #> 328 19  72.0    0    NA  0   2.7   5    1  0           328 #> 329 19  72.0    0    NA  0  18.0   6    2  0           329 #> 330 19  96.0    0    NA  0   1.4   5    1  0           330 #> 331 19  96.0    0    NA  0  17.0   6    2  0           331 #> 332 19 120.0    0    NA  0   1.1   5    1  0           332 #> 333 19 120.0    0    NA  0  26.0   6    2  0           333 #> 334 20   0.0    1  88.5  0    NA   1    0  0           334 #> 335 20   0.0    0    NA  0 100.0   6    2  0           335 #> 336 20  24.0    0    NA  0   9.6   5    1  0           336 #> 337 20  24.0    0    NA  0  41.0   6    2  0           337 #> 338 20  36.0    0    NA  0   8.0   5    1  0           338 #> 339 20  36.0    0    NA  0  30.0   6    2  0           339 #> 340 20  48.0    0    NA  0   6.6   5    1  0           340 #> 341 20  48.0    0    NA  0  22.0   6    2  0           341 #> 342 20  72.0    0    NA  0   5.6   5    1  0           342 #> 343 20  72.0    0    NA  0  23.0   6    2  0           343 #> 344 20  96.0    0    NA  0   3.5   5    1  0           344 #> 345 20  96.0    0    NA  0  23.0   6    2  0           345 #> 346 20 120.0    0    NA  0   2.3   5    1  0           346 #> 347 20 120.0    0    NA  0  35.0   6    2  0           347 #> 348 21   0.0    1  93.0  0    NA   1    0  0           348 #> 349 21   0.0    0    NA  0 100.0   6    2  0           349 #> 350 21  24.0    0    NA  0   7.3   5    1  0           350 #> 351 21  24.0    0    NA  0  46.0   6    2  0           351 #> 352 21  36.0    0    NA  0   6.1   5    1  0           352 #> 353 21  36.0    0    NA  0  27.0   6    2  0           353 #> 354 21  48.0    0    NA  0   4.3   5    1  0           354 #> 355 21  48.0    0    NA  0  22.0   6    2  0           355 #> 356 21  72.0    0    NA  0   3.2   5    1  0           356 #> 357 21  72.0    0    NA  0  36.0   6    2  0           357 #> 358 21  96.0    0    NA  0   2.3   5    1  0           358 #> 359 21  96.0    0    NA  0  40.0   6    2  0           359 #> 360 21 120.0    0    NA  0   1.9   5    1  0           360 #> 361 21 120.0    0    NA  0  44.0   6    2  0           361 #> 362 22   0.0    1  87.0  0    NA   1    0  0           362 #> 363 22   0.0    0    NA  0 100.0   6    2  0           363 #> 364 22  24.0    0    NA  0   8.9   5    1  0           364 #> 365 22  24.0    0    NA  0  35.0   6    2  0           365 #> 366 22  36.0    0    NA  0   8.4   5    1  0           366 #> 367 22  36.0    0    NA  0  27.0   6    2  0           367 #> 368 22  48.0    0    NA  0   8.0   5    1  0           368 #> 369 22  48.0    0    NA  0  23.0   6    2  0           369 #> 370 22  72.0    0    NA  0   4.4   5    1  0           370 #> 371 22  72.0    0    NA  0  27.0   6    2  0           371 #> 372 22  96.0    0    NA  0   3.2   5    1  0           372 #> 373 22  96.0    0    NA  0  43.0   6    2  0           373 #> 374 22 120.0    0    NA  0   1.7   5    1  0           374 #> 375 22 120.0    0    NA  0  43.0   6    2  0           375 #> 376 23   0.0    1 110.0  0    NA   1    0  0           376 #> 377 23   0.0    0    NA  0 100.0   6    2  0           377 #> 378 23  24.0    0    NA  0   9.8   5    1  0           378 #> 379 23  24.0    0    NA  0  34.0   6    2  0           379 #> 380 23  36.0    0    NA  0   8.4   5    1  0           380 #> 381 23  36.0    0    NA  0  24.0   6    2  0           381 #> 382 23  48.0    0    NA  0   6.6   5    1  0           382 #> 383 23  48.0    0    NA  0  15.0   6    2  0           383 #> 384 23  72.0    0    NA  0   4.8   5    1  0           384 #> 385 23  72.0    0    NA  0  15.0   6    2  0           385 #> 386 23  96.0    0    NA  0   3.2   5    1  0           386 #> 387 23  96.0    0    NA  0  19.0   6    2  0           387 #> 388 23 120.0    0    NA  0   2.4   5    1  0           388 #> 389 23 120.0    0    NA  0  19.0   6    2  0           389 #> 390 24   0.0    1 115.0  0    NA   1    0  0           390 #> 391 24   0.0    0    NA  0  88.0   6    2  0           391 #> 392 24  24.0    0    NA  0   8.2   5    1  0           392 #> 393 24  24.0    0    NA  0  37.0   6    2  0           393 #> 394 24  36.0    0    NA  0   7.5   5    1  0           394 #> 395 24  36.0    0    NA  0  20.0   6    2  0           395 #> 396 24  48.0    0    NA  0   6.8   5    1  0           396 #> 397 24  48.0    0    NA  0  20.0   6    2  0           397 #> 398 24  72.0    0    NA  0   5.5   5    1  0           398 #> 399 24  72.0    0    NA  0  26.0   6    2  0           399 #> 400 24  96.0    0    NA  0   4.5   5    1  0           400 #> 401 24  96.0    0    NA  0  28.0   6    2  0           401 #> 402 24 120.0    0    NA  0   3.7   5    1  0           402 #> 403 24 120.0    0    NA  0  50.0   6    2  0           403 #> 404 25   0.0    1 112.0  0    NA   1    0  0           404 #> 405 25   0.0    0    NA  0 100.0   6    2  0           405 #> 406 25  24.0    0    NA  0  11.0   5    1  0           406 #> 407 25  24.0    0    NA  0  32.0   6    2  0           407 #> 408 25  36.0    0    NA  0  10.0   5    1  0           408 #> 409 25  36.0    0    NA  0  20.0   6    2  0           409 #> 410 25  48.0    0    NA  0   8.2   5    1  0           410 #> 411 25  48.0    0    NA  0  17.0   6    2  0           411 #> 412 25  72.0    0    NA  0   6.0   5    1  0           412 #> 413 25  72.0    0    NA  0  19.0   6    2  0           413 #> 414 25  96.0    0    NA  0   3.7   5    1  0           414 #> 415 25  96.0    0    NA  0  21.0   6    2  0           415 #> 416 25 120.0    0    NA  0   2.6   5    1  0           416 #> 417 25 120.0    0    NA  0  30.0   6    2  0           417 #> 418 26   0.0    1 120.0  0    NA   1    0  0           418 #> 419 26   0.0    0    NA  0 100.0   6    2  0           419 #> 420 26  24.0    0    NA  0  10.0   5    1  0           420 #> 421 26  24.0    0    NA  0  41.0   6    2  0           421 #> 422 26  36.0    0    NA  0   9.0   5    1  0           422 #> 423 26  36.0    0    NA  0  28.0   6    2  0           423 #> 424 26  48.0    0    NA  0   7.3   5    1  0           424 #> 425 26  48.0    0    NA  0  19.0   6    2  0           425 #> 426 26  72.0    0    NA  0   5.2   5    1  0           426 #> 427 26  72.0    0    NA  0  17.0   6    2  0           427 #> 428 26  96.0    0    NA  0   3.7   5    1  0           428 #> 429 26  96.0    0    NA  0  17.0   6    2  0           429 #> 430 26 120.0    0    NA  0   2.7   5    1  0           430 #> 431 26 120.0    0    NA  0  24.0   6    2  0           431 #> 432 27   0.0    1 120.0  0    NA   1    0  0           432 #> 433 27   0.0    0    NA  0 100.0   6    2  0           433 #> 434 27  24.0    0    NA  0  11.8   5    1  0           434 #> 435 27  24.0    0    NA  0  32.0   6    2  0           435 #> 436 27  36.0    0    NA  0   9.2   5    1  0           436 #> 437 27  36.0    0    NA  0  21.0   6    2  0           437 #> 438 27  48.0    0    NA  0   7.7   5    1  0           438 #> 439 27  48.0    0    NA  0  19.0   6    2  0           439 #> 440 27  72.0    0    NA  0   4.9   5    1  0           440 #> 441 27  72.0    0    NA  0  22.0   6    2  0           441 #> 442 27  96.0    0    NA  0   3.4   5    1  0           442 #> 443 27  96.0    0    NA  0  33.0   6    2  0           443 #> 444 27 120.0    0    NA  0   2.7   5    1  0           444 #> 445 27 120.0    0    NA  0  46.0   6    2  0           445 #> 446 28   0.0    1 120.0  0    NA   1    0  0           446 #> 447 28   0.0    0    NA  0 100.0   6    2  0           447 #> 448 28  24.0    0    NA  0  10.1   5    1  0           448 #> 449 28  24.0    0    NA  0  39.0   6    2  0           449 #> 450 28  36.0    0    NA  0   8.0   5    1  0           450 #> 451 28  36.0    0    NA  0  25.0   6    2  0           451 #> 452 28  48.0    0    NA  0   6.0   5    1  0           452 #> 453 28  48.0    0    NA  0  16.0   6    2  0           453 #> 454 28  72.0    0    NA  0   4.9   5    1  0           454 #> 455 28  72.0    0    NA  0  14.0   6    2  0           455 #> 456 28  96.0    0    NA  0   3.4   5    1  0           456 #> 457 28  96.0    0    NA  0  15.0   6    2  0           457 #> 458 28 120.0    0    NA  0   2.0   5    1  0           458 #> 459 28 120.0    0    NA  0  20.0   6    2  0           459 #> 460 29   0.0    1 153.0  0    NA   1    0  0           460 #> 461 29   0.0    0    NA  0  86.0   6    2  0           461 #> 462 29  24.0    0    NA  0   8.3   5    1  0           462 #> 463 29  24.0    0    NA  0  35.0   6    2  0           463 #> 464 29  36.0    0    NA  0   7.0   5    1  0           464 #> 465 29  36.0    0    NA  0  21.0   6    2  0           465 #> 466 29  48.0    0    NA  0   5.6   5    1  0           466 #> 467 29  48.0    0    NA  0  18.0   6    2  0           467 #> 468 29  72.0    0    NA  0   4.1   5    1  0           468 #> 469 29  72.0    0    NA  0  20.0   6    2  0           469 #> 470 29  96.0    0    NA  0   3.1   5    1  0           470 #> 471 29  96.0    0    NA  0  29.0   6    2  0           471 #> 472 29 120.0    0    NA  0   2.2   5    1  0           472 #> 473 29 120.0    0    NA  0  41.0   6    2  0           473 #> 474 30   0.0    1 105.0  0    NA   1    0  0           474 #> 475 30   0.0    0    NA  0 100.0   6    2  0           475 #> 476 30  24.0    0    NA  0   9.9   5    1  0           476 #> 477 30  24.0    0    NA  0  45.0   6    2  0           477 #> 478 30  36.0    0    NA  0   7.5   5    1  0           478 #> 479 30  36.0    0    NA  0  24.0   6    2  0           479 #> 480 30  48.0    0    NA  0   6.5   5    1  0           480 #> 481 30  48.0    0    NA  0  23.0   6    2  0           481 #> 482 30  72.0    0    NA  0   4.1   5    1  0           482 #> 483 30  72.0    0    NA  0  26.0   6    2  0           483 #> 484 30  96.0    0    NA  0   2.9   5    1  0           484 #> 485 30  96.0    0    NA  0  28.0   6    2  0           485 #> 486 30 120.0    0    NA  0   2.3   5    1  0           486 #> 487 30 120.0    0    NA  0  39.0   6    2  0           487 #> 488 31   0.0    1 125.0  0    NA   1    0  0           488 #> 489 31   0.0    0    NA  0 100.0   6    2  0           489 #> 490 31  24.0    0    NA  0   9.5   5    1  0           490 #> 491 31  24.0    0    NA  0  45.0   6    2  0           491 #> 492 31  36.0    0    NA  0   7.8   5    1  0           492 #> 493 31  36.0    0    NA  0  30.0   6    2  0           493 #> 494 31  48.0    0    NA  0   6.4   5    1  0           494 #> 495 31  48.0    0    NA  0  24.0   6    2  0           495 #> 496 31  72.0    0    NA  0   4.5   5    1  0           496 #> 497 31  72.0    0    NA  0  22.0   6    2  0           497 #> 498 31  96.0    0    NA  0   3.4   5    1  0           498 #> 499 31  96.0    0    NA  0  28.0   6    2  0           499 #> 500 31 120.0    0    NA  0   2.5   5    1  0           500 #> 501 31 120.0    0    NA  0  42.0   6    2  0           501 #> 502 32   0.0    1  93.0  0    NA   1    0  0           502 #> 503 32   0.0    0    NA  0 100.0   6    2  0           503 #> 504 32  24.0    0    NA  0   8.9   5    1  0           504 #> 505 32  24.0    0    NA  0  36.0   6    2  0           505 #> 506 32  36.0    0    NA  0   7.7   5    1  0           506 #> 507 32  36.0    0    NA  0  27.0   6    2  0           507 #> 508 32  48.0    0    NA  0   6.9   5    1  0           508 #> 509 32  48.0    0    NA  0  24.0   6    2  0           509 #> 510 32  72.0    0    NA  0   4.4   5    1  0           510 #> 511 32  72.0    0    NA  0  23.0   6    2  0           511 #> 512 32  96.0    0    NA  0   3.5   5    1  0           512 #> 513 32  96.0    0    NA  0  20.0   6    2  0           513 #> 514 32 120.0    0    NA  0   2.5   5    1  0           514 #> 515 32 120.0    0    NA  0  22.0   6    2  0           515  bblDatToRxode(pk.turnover.emax3, nlmixr2data::warfarin) #>   #>   #>     ID  TIME EVID   AMT II    DV CMT DVID SS nlmixrRowNums #> 1    1   0.0    1 100.0  0    NA   1    0  0             1 #> 2    1   0.5    0    NA  0   0.0   5    1  0             2 #> 3    1   1.0    0    NA  0   1.9   5    1  0             3 #> 4    1   2.0    0    NA  0   3.3   5    1  0             4 #> 5    1   3.0    0    NA  0   6.6   5    1  0             5 #> 6    1   6.0    0    NA  0   9.1   5    1  0             6 #> 7    1   9.0    0    NA  0  10.8   5    1  0             7 #> 8    1  12.0    0    NA  0   8.6   5    1  0             8 #> 9    1  24.0    0    NA  0   5.6   5    1  0             9 #> 10   1  24.0    0    NA  0  44.0   6    2  0            10 #> 11   1  36.0    0    NA  0   4.0   5    1  0            11 #> 12   1  36.0    0    NA  0  27.0   6    2  0            12 #> 13   1  48.0    0    NA  0   2.7   5    1  0            13 #> 14   1  48.0    0    NA  0  28.0   6    2  0            14 #> 15   1  72.0    0    NA  0   0.8   5    1  0            15 #> 16   1  72.0    0    NA  0  31.0   6    2  0            16 #> 17   1  96.0    0    NA  0  60.0   6    2  0            17 #> 18   1 120.0    0    NA  0  65.0   6    2  0            18 #> 19   1 144.0    0    NA  0  71.0   6    2  0            19 #> 20   2   0.0    1 100.0  0    NA   1    0  0            20 #> 21   2   0.0    0    NA  0 100.0   6    2  0            21 #> 22   2  24.0    0    NA  0   9.2   5    1  0            22 #> 23   2  24.0    0    NA  0  49.0   6    2  0            23 #> 24   2  36.0    0    NA  0   8.5   5    1  0            24 #> 25   2  36.0    0    NA  0  32.0   6    2  0            25 #> 26   2  48.0    0    NA  0   6.4   5    1  0            26 #> 27   2  48.0    0    NA  0  26.0   6    2  0            27 #> 28   2  72.0    0    NA  0   4.8   5    1  0            28 #> 29   2  72.0    0    NA  0  22.0   6    2  0            29 #> 30   2  96.0    0    NA  0   3.1   5    1  0            30 #> 31   2  96.0    0    NA  0  28.0   6    2  0            31 #> 32   2 120.0    0    NA  0   2.5   5    1  0            32 #> 33   2 120.0    0    NA  0  33.0   6    2  0            33 #> 34   3   0.0    1 100.0  0    NA   1    0  0            34 #> 35   3   0.0    0    NA  0 100.0   6    2  0            35 #> 36   3   0.5    0    NA  0   0.0   5    1  0            36 #> 37   3   2.0    0    NA  0   8.4   5    1  0            37 #> 38   3   3.0    0    NA  0   9.7   5    1  0            38 #> 39   3   6.0    0    NA  0   9.8   5    1  0            39 #> 40   3  12.0    0    NA  0  11.0   5    1  0            40 #> 41   3  24.0    0    NA  0   8.3   5    1  0            41 #> 42   3  24.0    0    NA  0  46.0   6    2  0            42 #> 43   3  36.0    0    NA  0   7.7   5    1  0            43 #> 44   3  36.0    0    NA  0  22.0   6    2  0            44 #> 45   3  48.0    0    NA  0   6.3   5    1  0            45 #> 46   3  48.0    0    NA  0  19.0   6    2  0            46 #> 47   3  72.0    0    NA  0   4.1   5    1  0            47 #> 48   3  72.0    0    NA  0  20.0   6    2  0            48 #> 49   3  96.0    0    NA  0   3.0   5    1  0            49 #> 50   3  96.0    0    NA  0  42.0   6    2  0            50 #> 51   3 120.0    0    NA  0   1.4   5    1  0            51 #> 52   3 120.0    0    NA  0  49.0   6    2  0            52 #> 53   3 144.0    0    NA  0  54.0   6    2  0            53 #> 54   4   0.0    1 120.0  0    NA   1    0  0            54 #> 55   4   0.0    0    NA  0 100.0   6    2  0            55 #> 56   4   3.0    0    NA  0  12.0   5    1  0            56 #> 57   4   6.0    0    NA  0  13.2   5    1  0            57 #> 58   4   9.0    0    NA  0  14.4   5    1  0            58 #> 59   4  24.0    0    NA  0   9.6   5    1  0            59 #> 60   4  24.0    0    NA  0  30.0   6    2  0            60 #> 61   4  36.0    0    NA  0   8.2   5    1  0            61 #> 62   4  36.0    0    NA  0  24.0   6    2  0            62 #> 63   4  48.0    0    NA  0   7.8   5    1  0            63 #> 64   4  48.0    0    NA  0  13.0   6    2  0            64 #> 65   4  72.0    0    NA  0   5.8   5    1  0            65 #> 66   4  72.0    0    NA  0   9.0   6    2  0            66 #> 67   4  96.0    0    NA  0   4.3   5    1  0            67 #> 68   4  96.0    0    NA  0   9.0   6    2  0            68 #> 69   4 120.0    0    NA  0   3.0   5    1  0            69 #> 70   4 120.0    0    NA  0  11.0   6    2  0            70 #> 71   4 144.0    0    NA  0  12.0   6    2  0            71 #> 72   5   0.0    1  60.0  0    NA   1    0  0            72 #> 73   5   0.0    0    NA  0  82.0   6    2  0            73 #> 74   5   3.0    0    NA  0  11.1   5    1  0            74 #> 75   5   6.0    0    NA  0  11.9   5    1  0            75 #> 76   5   9.0    0    NA  0   9.8   5    1  0            76 #> 77   5  12.0    0    NA  0  11.0   5    1  0            77 #> 78   5  24.0    0    NA  0   8.5   5    1  0            78 #> 79   5  24.0    0    NA  0  43.0   6    2  0            79 #> 80   5  36.0    0    NA  0   7.6   5    1  0            80 #> 81   5  36.0    0    NA  0  25.0   6    2  0            81 #> 82   5  48.0    0    NA  0   5.4   5    1  0            82 #> 83   5  48.0    0    NA  0  18.0   6    2  0            83 #> 84   5  72.0    0    NA  0   4.5   5    1  0            84 #> 85   5  72.0    0    NA  0  17.0   6    2  0            85 #> 86   5  96.0    0    NA  0   3.3   5    1  0            86 #> 87   5  96.0    0    NA  0  23.0   6    2  0            87 #> 88   5 120.0    0    NA  0   2.3   5    1  0            88 #> 89   5 120.0    0    NA  0  29.0   6    2  0            89 #> 90   5 144.0    0    NA  0  41.0   6    2  0            90 #> 91   6   0.0    1 113.0  0    NA   1    0  0            91 #> 92   6   0.0    0    NA  0 100.0   6    2  0            92 #> 93   6   6.0    0    NA  0   8.6   5    1  0            93 #> 94   6  12.0    0    NA  0   8.6   5    1  0            94 #> 95   6  24.0    0    NA  0   7.0   5    1  0            95 #> 96   6  24.0    0    NA  0  34.0   6    2  0            96 #> 97   6  36.0    0    NA  0   5.7   5    1  0            97 #> 98   6  36.0    0    NA  0  23.0   6    2  0            98 #> 99   6  48.0    0    NA  0   4.7   5    1  0            99 #> 100  6  48.0    0    NA  0  20.0   6    2  0           100 #> 101  6  72.0    0    NA  0   3.3   5    1  0           101 #> 102  6  72.0    0    NA  0  16.0   6    2  0           102 #> 103  6  96.0    0    NA  0   2.3   5    1  0           103 #> 104  6  96.0    0    NA  0  17.0   6    2  0           104 #> 105  6 120.0    0    NA  0   1.7   5    1  0           105 #> 106  6 120.0    0    NA  0  18.0   6    2  0           106 #> 107  6 144.0    0    NA  0  25.0   6    2  0           107 #> 108  7   0.0    1  90.0  0    NA   1    0  0           108 #> 109  7   3.0    0    NA  0  13.4   5    1  0           109 #> 110  7   6.0    0    NA  0  12.4   5    1  0           110 #> 111  7   9.0    0    NA  0  12.7   5    1  0           111 #> 112  7  12.0    0    NA  0   8.8   5    1  0           112 #> 113  7  24.0    0    NA  0   6.1   5    1  0           113 #> 114  7  24.0    0    NA  0  36.0   6    2  0           114 #> 115  7  36.0    0    NA  0   3.5   5    1  0           115 #> 116  7  36.0    0    NA  0  33.0   6    2  0           116 #> 117  7  48.0    0    NA  0   1.8   5    1  0           117 #> 118  7  48.0    0    NA  0  28.0   6    2  0           118 #> 119  7  72.0    0    NA  0   1.5   5    1  0           119 #> 120  7  72.0    0    NA  0  52.0   6    2  0           120 #> 121  7  96.0    0    NA  0   1.0   5    1  0           121 #> 122  7  96.0    0    NA  0  80.0   6    2  0           122 #> 123  7 120.0    0    NA  0  90.0   6    2  0           123 #> 124  7 144.0    0    NA  0 100.0   6    2  0           124 #> 125  8   0.0    1 135.0  0    NA   1    0  0           125 #> 126  8   0.0    0    NA  0  88.0   6    2  0           126 #> 127  8   2.0    0    NA  0  17.6   5    1  0           127 #> 128  8   3.0    0    NA  0  17.3   5    1  0           128 #> 129  8   6.0    0    NA  0  15.0   5    1  0           129 #> 130  8   9.0    0    NA  0  15.0   5    1  0           130 #> 131  8  12.0    0    NA  0  12.4   5    1  0           131 #> 132  8  24.0    0    NA  0   7.9   5    1  0           132 #> 133  8  24.0    0    NA  0  35.0   6    2  0           133 #> 134  8  36.0    0    NA  0   7.9   5    1  0           134 #> 135  8  36.0    0    NA  0  20.0   6    2  0           135 #> 136  8  48.0    0    NA  0   5.1   5    1  0           136 #> 137  8  48.0    0    NA  0  12.0   6    2  0           137 #> 138  8  72.0    0    NA  0   3.6   5    1  0           138 #> 139  8  72.0    0    NA  0  16.0   6    2  0           139 #> 140  8  96.0    0    NA  0   2.4   5    1  0           140 #> 141  8  96.0    0    NA  0  23.0   6    2  0           141 #> 142  8 120.0    0    NA  0   2.0   5    1  0           142 #> 143  8 120.0    0    NA  0  36.0   6    2  0           143 #> 144  8 144.0    0    NA  0  48.0   6    2  0           144 #> 145  9   0.0    1  75.0  0    NA   1    0  0           145 #> 146  9   0.0    0    NA  0  92.0   6    2  0           146 #> 147  9   0.5    0    NA  0   0.0   5    1  0           147 #> 148  9   1.0    0    NA  0   1.0   5    1  0           148 #> 149  9   2.0    0    NA  0   4.6   5    1  0           149 #> 150  9   3.0    0    NA  0  12.7   5    1  0           150 #> 151  9   3.0    0    NA  0   8.0   5    1  0           151 #> 152  9   6.0    0    NA  0  12.7   5    1  0           152 #> 153  9   6.0    0    NA  0  11.5   5    1  0           153 #> 154  9   9.0    0    NA  0  12.9   5    1  0           154 #> 155  9   9.0    0    NA  0  11.4   5    1  0           155 #> 156  9  12.0    0    NA  0  11.4   5    1  0           156 #> 157  9  12.0    0    NA  0  11.0   5    1  0           157 #> 158  9  24.0    0    NA  0   9.1   5    1  0           158 #> 159  9  24.0    0    NA  0  33.0   6    2  0           159 #> 160  9  36.0    0    NA  0   8.2   5    1  0           160 #> 161  9  36.0    0    NA  0  22.0   6    2  0           161 #> 162  9  48.0    0    NA  0   5.9   5    1  0           162 #> 163  9  48.0    0    NA  0  16.0   6    2  0           163 #> 164  9  72.0    0    NA  0   3.6   5    1  0           164 #> 165  9  72.0    0    NA  0  18.0   6    2  0           165 #> 166  9  96.0    0    NA  0   1.7   5    1  0           166 #> 167  9  96.0    0    NA  0  32.0   6    2  0           167 #> 168  9 120.0    0    NA  0   1.1   5    1  0           168 #> 169  9 120.0    0    NA  0  30.0   6    2  0           169 #> 170  9 144.0    0    NA  0  45.0   6    2  0           170 #> 171 10   0.0    1 105.0  0    NA   1    0  0           171 #> 172 10   0.0    0    NA  0  90.0   6    2  0           172 #> 173 10  24.0    0    NA  0   8.6   5    1  0           173 #> 174 10  24.0    0    NA  0  39.0   6    2  0           174 #> 175 10  36.0    0    NA  0   8.0   5    1  0           175 #> 176 10  36.0    0    NA  0  22.0   6    2  0           176 #> 177 10  48.0    0    NA  0   6.0   5    1  0           177 #> 178 10  48.0    0    NA  0  17.0   6    2  0           178 #> 179 10  72.0    0    NA  0   4.4   5    1  0           179 #> 180 10  72.0    0    NA  0  17.0   6    2  0           180 #> 181 10  96.0    0    NA  0   3.6   5    1  0           181 #> 182 10  96.0    0    NA  0  22.0   6    2  0           182 #> 183 10 120.0    0    NA  0   2.8   5    1  0           183 #> 184 10 120.0    0    NA  0  25.0   6    2  0           184 #> 185 10 144.0    0    NA  0  33.0   6    2  0           185 #> 186 11   0.0    1 123.0  0    NA   1    0  0           186 #> 187 11   0.0    0    NA  0 100.0   6    2  0           187 #> 188 11   1.5    0    NA  0  11.4   5    1  0           188 #> 189 11   3.0    0    NA  0  15.4   5    1  0           189 #> 190 11   6.0    0    NA  0  17.5   5    1  0           190 #> 191 11  12.0    0    NA  0  14.0   5    1  0           191 #> 192 11  24.0    0    NA  0   9.0   5    1  0           192 #> 193 11  24.0    0    NA  0  37.0   6    2  0           193 #> 194 11  36.0    0    NA  0   8.9   5    1  0           194 #> 195 11  36.0    0    NA  0  24.0   6    2  0           195 #> 196 11  48.0    0    NA  0   6.6   5    1  0           196 #> 197 11  48.0    0    NA  0  14.0   6    2  0           197 #> 198 11  72.0    0    NA  0   4.2   5    1  0           198 #> 199 11  72.0    0    NA  0  11.0   6    2  0           199 #> 200 11  96.0    0    NA  0   3.6   5    1  0           200 #> 201 11  96.0    0    NA  0  14.0   6    2  0           201 #> 202 11 120.0    0    NA  0   2.6   5    1  0           202 #> 203 11 120.0    0    NA  0  23.0   6    2  0           203 #> 204 11 144.0    0    NA  0  33.0   6    2  0           204 #> 205 12   0.0    1 113.0  0    NA   1    0  0           205 #> 206 12   0.0    0    NA  0  85.0   6    2  0           206 #> 207 12   1.5    0    NA  0   0.6   5    1  0           207 #> 208 12   3.0    0    NA  0   2.8   5    1  0           208 #> 209 12   6.0    0    NA  0  13.8   5    1  0           209 #> 210 12   9.0    0    NA  0  15.0   5    1  0           210 #> 211 12  24.0    0    NA  0  10.5   5    1  0           211 #> 212 12  24.0    0    NA  0  25.0   6    2  0           212 #> 213 12  36.0    0    NA  0   9.1   5    1  0           213 #> 214 12  36.0    0    NA  0  15.0   6    2  0           214 #> 215 12  48.0    0    NA  0   6.6   5    1  0           215 #> 216 12  48.0    0    NA  0  11.0   6    2  0           216 #> 217 12  72.0    0    NA  0   4.9   5    1  0           217 #> 218 12  96.0    0    NA  0   2.4   5    1  0           218 #> 219 12 120.0    0    NA  0   1.9   5    1  0           219 #> 220 13   0.0    1 113.0  0    NA   1    0  0           220 #> 221 13   0.0    0    NA  0  88.0   6    2  0           221 #> 222 13   1.5    0    NA  0   3.6   5    1  0           222 #> 223 13   3.0    0    NA  0  12.9   5    1  0           223 #> 224 13   6.0    0    NA  0  12.9   5    1  0           224 #> 225 13   9.0    0    NA  0  10.2   5    1  0           225 #> 226 13  24.0    0    NA  0   6.4   5    1  0           226 #> 227 13  24.0    0    NA  0  41.0   6    2  0           227 #> 228 13  36.0    0    NA  0   6.9   5    1  0           228 #> 229 13  36.0    0    NA  0  23.0   6    2  0           229 #> 230 13  48.0    0    NA  0   4.5   5    1  0           230 #> 231 13  48.0    0    NA  0  16.0   6    2  0           231 #> 232 13  72.0    0    NA  0   3.2   5    1  0           232 #> 233 13  72.0    0    NA  0  14.0   6    2  0           233 #> 234 13  96.0    0    NA  0   2.4   5    1  0           234 #> 235 13  96.0    0    NA  0  18.0   6    2  0           235 #> 236 13 120.0    0    NA  0   1.3   5    1  0           236 #> 237 13 120.0    0    NA  0  22.0   6    2  0           237 #> 238 13 144.0    0    NA  0  35.0   6    2  0           238 #> 239 14   0.0    1  75.0  0    NA   1    0  0           239 #> 240 14   0.0    0    NA  0  85.0   6    2  0           240 #> 241 14   0.5    0    NA  0   0.0   5    1  0           241 #> 242 14   1.0    0    NA  0   2.7   5    1  0           242 #> 243 14   2.0    0    NA  0  11.6   5    1  0           243 #> 244 14   3.0    0    NA  0  11.6   5    1  0           244 #> 245 14   6.0    0    NA  0  11.3   5    1  0           245 #> 246 14   9.0    0    NA  0   9.7   5    1  0           246 #> 247 14  24.0    0    NA  0   6.5   5    1  0           247 #> 248 14  24.0    0    NA  0  32.0   6    2  0           248 #> 249 14  36.0    0    NA  0   5.2   5    1  0           249 #> 250 14  36.0    0    NA  0  22.0   6    2  0           250 #> 251 14  48.0    0    NA  0   3.6   5    1  0           251 #> 252 14  48.0    0    NA  0  21.0   6    2  0           252 #> 253 14  72.0    0    NA  0   2.4   5    1  0           253 #> 254 14  72.0    0    NA  0  28.0   6    2  0           254 #> 255 14  96.0    0    NA  0   0.9   5    1  0           255 #> 256 14  96.0    0    NA  0  38.0   6    2  0           256 #> 257 14 120.0    0    NA  0  46.0   6    2  0           257 #> 258 14 144.0    0    NA  0  65.0   6    2  0           258 #> 259 15   0.0    1  85.0  0    NA   1    0  0           259 #> 260 15   0.0    0    NA  0 100.0   6    2  0           260 #> 261 15   1.0    0    NA  0   6.6   5    1  0           261 #> 262 15   3.0    0    NA  0  11.9   5    1  0           262 #> 263 15   6.0    0    NA  0  11.7   5    1  0           263 #> 264 15   9.0    0    NA  0  12.2   5    1  0           264 #> 265 15  24.0    0    NA  0   8.1   5    1  0           265 #> 266 15  24.0    0    NA  0  43.0   6    2  0           266 #> 267 15  36.0    0    NA  0   7.4   5    1  0           267 #> 268 15  36.0    0    NA  0  26.0   6    2  0           268 #> 269 15  48.0    0    NA  0   6.8   5    1  0           269 #> 270 15  48.0    0    NA  0  15.0   6    2  0           270 #> 271 15  72.0    0    NA  0   5.3   5    1  0           271 #> 272 15  72.0    0    NA  0  13.0   6    2  0           272 #> 273 15  96.0    0    NA  0   3.0   5    1  0           273 #> 274 15  96.0    0    NA  0  21.0   6    2  0           274 #> 275 15 120.0    0    NA  0   2.0   5    1  0           275 #> 276 15 120.0    0    NA  0  28.0   6    2  0           276 #> 277 15 144.0    0    NA  0  39.0   6    2  0           277 #> 278 16   0.0    1  87.0  0    NA   1    0  0           278 #> 279 16   0.0    0    NA  0 100.0   6    2  0           279 #> 280 16  24.0    0    NA  0  10.4   5    1  0           280 #> 281 16  24.0    0    NA  0  42.0   6    2  0           281 #> 282 16  36.0    0    NA  0   8.9   5    1  0           282 #> 283 16  36.0    0    NA  0  32.0   6    2  0           283 #> 284 16  48.0    0    NA  0   7.0   5    1  0           284 #> 285 16  48.0    0    NA  0  26.0   6    2  0           285 #> 286 16  72.0    0    NA  0   4.4   5    1  0           286 #> 287 16  72.0    0    NA  0  31.0   6    2  0           287 #> 288 16  96.0    0    NA  0   3.2   5    1  0           288 #> 289 16  96.0    0    NA  0  33.0   6    2  0           289 #> 290 16 120.0    0    NA  0   2.4   5    1  0           290 #> 291 16 120.0    0    NA  0  54.0   6    2  0           291 #> 292 17   0.0    1 117.0  0    NA   1    0  0           292 #> 293 17   0.0    0    NA  0 100.0   6    2  0           293 #> 294 17  24.0    0    NA  0   7.6   5    1  0           294 #> 295 17  24.0    0    NA  0  35.0   6    2  0           295 #> 296 17  36.0    0    NA  0   6.4   5    1  0           296 #> 297 17  36.0    0    NA  0  23.0   6    2  0           297 #> 298 17  48.0    0    NA  0   6.0   5    1  0           298 #> 299 17  48.0    0    NA  0  17.0   6    2  0           299 #> 300 17  72.0    0    NA  0   4.0   5    1  0           300 #> 301 17  72.0    0    NA  0  18.0   6    2  0           301 #> 302 17  96.0    0    NA  0   3.1   5    1  0           302 #> 303 17  96.0    0    NA  0  18.0   6    2  0           303 #> 304 17 120.0    0    NA  0   2.0   5    1  0           304 #> 305 17 120.0    0    NA  0  21.0   6    2  0           305 #> 306 18   0.0    1 112.0  0    NA   1    0  0           306 #> 307 18   0.0    0    NA  0 100.0   6    2  0           307 #> 308 18  24.0    0    NA  0   7.6   5    1  0           308 #> 309 18  24.0    0    NA  0  32.0   6    2  0           309 #> 310 18  36.0    0    NA  0   6.6   5    1  0           310 #> 311 18  36.0    0    NA  0  20.0   6    2  0           311 #> 312 18  48.0    0    NA  0   5.4   5    1  0           312 #> 313 18  48.0    0    NA  0  18.0   6    2  0           313 #> 314 18  72.0    0    NA  0   3.4   5    1  0           314 #> 315 18  72.0    0    NA  0  18.0   6    2  0           315 #> 316 18  96.0    0    NA  0   1.2   5    1  0           316 #> 317 18  96.0    0    NA  0  19.0   6    2  0           317 #> 318 18 120.0    0    NA  0   0.9   5    1  0           318 #> 319 18 120.0    0    NA  0  29.0   6    2  0           319 #> 320 19   0.0    1  95.5  0    NA   1    0  0           320 #> 321 19   0.0    0    NA  0 100.0   6    2  0           321 #> 322 19  24.0    0    NA  0   6.6   5    1  0           322 #> 323 19  24.0    0    NA  0  33.0   6    2  0           323 #> 324 19  36.0    0    NA  0   5.3   5    1  0           324 #> 325 19  36.0    0    NA  0  28.0   6    2  0           325 #> 326 19  48.0    0    NA  0   3.6   5    1  0           326 #> 327 19  48.0    0    NA  0  18.0   6    2  0           327 #> 328 19  72.0    0    NA  0   2.7   5    1  0           328 #> 329 19  72.0    0    NA  0  18.0   6    2  0           329 #> 330 19  96.0    0    NA  0   1.4   5    1  0           330 #> 331 19  96.0    0    NA  0  17.0   6    2  0           331 #> 332 19 120.0    0    NA  0   1.1   5    1  0           332 #> 333 19 120.0    0    NA  0  26.0   6    2  0           333 #> 334 20   0.0    1  88.5  0    NA   1    0  0           334 #> 335 20   0.0    0    NA  0 100.0   6    2  0           335 #> 336 20  24.0    0    NA  0   9.6   5    1  0           336 #> 337 20  24.0    0    NA  0  41.0   6    2  0           337 #> 338 20  36.0    0    NA  0   8.0   5    1  0           338 #> 339 20  36.0    0    NA  0  30.0   6    2  0           339 #> 340 20  48.0    0    NA  0   6.6   5    1  0           340 #> 341 20  48.0    0    NA  0  22.0   6    2  0           341 #> 342 20  72.0    0    NA  0   5.6   5    1  0           342 #> 343 20  72.0    0    NA  0  23.0   6    2  0           343 #> 344 20  96.0    0    NA  0   3.5   5    1  0           344 #> 345 20  96.0    0    NA  0  23.0   6    2  0           345 #> 346 20 120.0    0    NA  0   2.3   5    1  0           346 #> 347 20 120.0    0    NA  0  35.0   6    2  0           347 #> 348 21   0.0    1  93.0  0    NA   1    0  0           348 #> 349 21   0.0    0    NA  0 100.0   6    2  0           349 #> 350 21  24.0    0    NA  0   7.3   5    1  0           350 #> 351 21  24.0    0    NA  0  46.0   6    2  0           351 #> 352 21  36.0    0    NA  0   6.1   5    1  0           352 #> 353 21  36.0    0    NA  0  27.0   6    2  0           353 #> 354 21  48.0    0    NA  0   4.3   5    1  0           354 #> 355 21  48.0    0    NA  0  22.0   6    2  0           355 #> 356 21  72.0    0    NA  0   3.2   5    1  0           356 #> 357 21  72.0    0    NA  0  36.0   6    2  0           357 #> 358 21  96.0    0    NA  0   2.3   5    1  0           358 #> 359 21  96.0    0    NA  0  40.0   6    2  0           359 #> 360 21 120.0    0    NA  0   1.9   5    1  0           360 #> 361 21 120.0    0    NA  0  44.0   6    2  0           361 #> 362 22   0.0    1  87.0  0    NA   1    0  0           362 #> 363 22   0.0    0    NA  0 100.0   6    2  0           363 #> 364 22  24.0    0    NA  0   8.9   5    1  0           364 #> 365 22  24.0    0    NA  0  35.0   6    2  0           365 #> 366 22  36.0    0    NA  0   8.4   5    1  0           366 #> 367 22  36.0    0    NA  0  27.0   6    2  0           367 #> 368 22  48.0    0    NA  0   8.0   5    1  0           368 #> 369 22  48.0    0    NA  0  23.0   6    2  0           369 #> 370 22  72.0    0    NA  0   4.4   5    1  0           370 #> 371 22  72.0    0    NA  0  27.0   6    2  0           371 #> 372 22  96.0    0    NA  0   3.2   5    1  0           372 #> 373 22  96.0    0    NA  0  43.0   6    2  0           373 #> 374 22 120.0    0    NA  0   1.7   5    1  0           374 #> 375 22 120.0    0    NA  0  43.0   6    2  0           375 #> 376 23   0.0    1 110.0  0    NA   1    0  0           376 #> 377 23   0.0    0    NA  0 100.0   6    2  0           377 #> 378 23  24.0    0    NA  0   9.8   5    1  0           378 #> 379 23  24.0    0    NA  0  34.0   6    2  0           379 #> 380 23  36.0    0    NA  0   8.4   5    1  0           380 #> 381 23  36.0    0    NA  0  24.0   6    2  0           381 #> 382 23  48.0    0    NA  0   6.6   5    1  0           382 #> 383 23  48.0    0    NA  0  15.0   6    2  0           383 #> 384 23  72.0    0    NA  0   4.8   5    1  0           384 #> 385 23  72.0    0    NA  0  15.0   6    2  0           385 #> 386 23  96.0    0    NA  0   3.2   5    1  0           386 #> 387 23  96.0    0    NA  0  19.0   6    2  0           387 #> 388 23 120.0    0    NA  0   2.4   5    1  0           388 #> 389 23 120.0    0    NA  0  19.0   6    2  0           389 #> 390 24   0.0    1 115.0  0    NA   1    0  0           390 #> 391 24   0.0    0    NA  0  88.0   6    2  0           391 #> 392 24  24.0    0    NA  0   8.2   5    1  0           392 #> 393 24  24.0    0    NA  0  37.0   6    2  0           393 #> 394 24  36.0    0    NA  0   7.5   5    1  0           394 #> 395 24  36.0    0    NA  0  20.0   6    2  0           395 #> 396 24  48.0    0    NA  0   6.8   5    1  0           396 #> 397 24  48.0    0    NA  0  20.0   6    2  0           397 #> 398 24  72.0    0    NA  0   5.5   5    1  0           398 #> 399 24  72.0    0    NA  0  26.0   6    2  0           399 #> 400 24  96.0    0    NA  0   4.5   5    1  0           400 #> 401 24  96.0    0    NA  0  28.0   6    2  0           401 #> 402 24 120.0    0    NA  0   3.7   5    1  0           402 #> 403 24 120.0    0    NA  0  50.0   6    2  0           403 #> 404 25   0.0    1 112.0  0    NA   1    0  0           404 #> 405 25   0.0    0    NA  0 100.0   6    2  0           405 #> 406 25  24.0    0    NA  0  11.0   5    1  0           406 #> 407 25  24.0    0    NA  0  32.0   6    2  0           407 #> 408 25  36.0    0    NA  0  10.0   5    1  0           408 #> 409 25  36.0    0    NA  0  20.0   6    2  0           409 #> 410 25  48.0    0    NA  0   8.2   5    1  0           410 #> 411 25  48.0    0    NA  0  17.0   6    2  0           411 #> 412 25  72.0    0    NA  0   6.0   5    1  0           412 #> 413 25  72.0    0    NA  0  19.0   6    2  0           413 #> 414 25  96.0    0    NA  0   3.7   5    1  0           414 #> 415 25  96.0    0    NA  0  21.0   6    2  0           415 #> 416 25 120.0    0    NA  0   2.6   5    1  0           416 #> 417 25 120.0    0    NA  0  30.0   6    2  0           417 #> 418 26   0.0    1 120.0  0    NA   1    0  0           418 #> 419 26   0.0    0    NA  0 100.0   6    2  0           419 #> 420 26  24.0    0    NA  0  10.0   5    1  0           420 #> 421 26  24.0    0    NA  0  41.0   6    2  0           421 #> 422 26  36.0    0    NA  0   9.0   5    1  0           422 #> 423 26  36.0    0    NA  0  28.0   6    2  0           423 #> 424 26  48.0    0    NA  0   7.3   5    1  0           424 #> 425 26  48.0    0    NA  0  19.0   6    2  0           425 #> 426 26  72.0    0    NA  0   5.2   5    1  0           426 #> 427 26  72.0    0    NA  0  17.0   6    2  0           427 #> 428 26  96.0    0    NA  0   3.7   5    1  0           428 #> 429 26  96.0    0    NA  0  17.0   6    2  0           429 #> 430 26 120.0    0    NA  0   2.7   5    1  0           430 #> 431 26 120.0    0    NA  0  24.0   6    2  0           431 #> 432 27   0.0    1 120.0  0    NA   1    0  0           432 #> 433 27   0.0    0    NA  0 100.0   6    2  0           433 #> 434 27  24.0    0    NA  0  11.8   5    1  0           434 #> 435 27  24.0    0    NA  0  32.0   6    2  0           435 #> 436 27  36.0    0    NA  0   9.2   5    1  0           436 #> 437 27  36.0    0    NA  0  21.0   6    2  0           437 #> 438 27  48.0    0    NA  0   7.7   5    1  0           438 #> 439 27  48.0    0    NA  0  19.0   6    2  0           439 #> 440 27  72.0    0    NA  0   4.9   5    1  0           440 #> 441 27  72.0    0    NA  0  22.0   6    2  0           441 #> 442 27  96.0    0    NA  0   3.4   5    1  0           442 #> 443 27  96.0    0    NA  0  33.0   6    2  0           443 #> 444 27 120.0    0    NA  0   2.7   5    1  0           444 #> 445 27 120.0    0    NA  0  46.0   6    2  0           445 #> 446 28   0.0    1 120.0  0    NA   1    0  0           446 #> 447 28   0.0    0    NA  0 100.0   6    2  0           447 #> 448 28  24.0    0    NA  0  10.1   5    1  0           448 #> 449 28  24.0    0    NA  0  39.0   6    2  0           449 #> 450 28  36.0    0    NA  0   8.0   5    1  0           450 #> 451 28  36.0    0    NA  0  25.0   6    2  0           451 #> 452 28  48.0    0    NA  0   6.0   5    1  0           452 #> 453 28  48.0    0    NA  0  16.0   6    2  0           453 #> 454 28  72.0    0    NA  0   4.9   5    1  0           454 #> 455 28  72.0    0    NA  0  14.0   6    2  0           455 #> 456 28  96.0    0    NA  0   3.4   5    1  0           456 #> 457 28  96.0    0    NA  0  15.0   6    2  0           457 #> 458 28 120.0    0    NA  0   2.0   5    1  0           458 #> 459 28 120.0    0    NA  0  20.0   6    2  0           459 #> 460 29   0.0    1 153.0  0    NA   1    0  0           460 #> 461 29   0.0    0    NA  0  86.0   6    2  0           461 #> 462 29  24.0    0    NA  0   8.3   5    1  0           462 #> 463 29  24.0    0    NA  0  35.0   6    2  0           463 #> 464 29  36.0    0    NA  0   7.0   5    1  0           464 #> 465 29  36.0    0    NA  0  21.0   6    2  0           465 #> 466 29  48.0    0    NA  0   5.6   5    1  0           466 #> 467 29  48.0    0    NA  0  18.0   6    2  0           467 #> 468 29  72.0    0    NA  0   4.1   5    1  0           468 #> 469 29  72.0    0    NA  0  20.0   6    2  0           469 #> 470 29  96.0    0    NA  0   3.1   5    1  0           470 #> 471 29  96.0    0    NA  0  29.0   6    2  0           471 #> 472 29 120.0    0    NA  0   2.2   5    1  0           472 #> 473 29 120.0    0    NA  0  41.0   6    2  0           473 #> 474 30   0.0    1 105.0  0    NA   1    0  0           474 #> 475 30   0.0    0    NA  0 100.0   6    2  0           475 #> 476 30  24.0    0    NA  0   9.9   5    1  0           476 #> 477 30  24.0    0    NA  0  45.0   6    2  0           477 #> 478 30  36.0    0    NA  0   7.5   5    1  0           478 #> 479 30  36.0    0    NA  0  24.0   6    2  0           479 #> 480 30  48.0    0    NA  0   6.5   5    1  0           480 #> 481 30  48.0    0    NA  0  23.0   6    2  0           481 #> 482 30  72.0    0    NA  0   4.1   5    1  0           482 #> 483 30  72.0    0    NA  0  26.0   6    2  0           483 #> 484 30  96.0    0    NA  0   2.9   5    1  0           484 #> 485 30  96.0    0    NA  0  28.0   6    2  0           485 #> 486 30 120.0    0    NA  0   2.3   5    1  0           486 #> 487 30 120.0    0    NA  0  39.0   6    2  0           487 #> 488 31   0.0    1 125.0  0    NA   1    0  0           488 #> 489 31   0.0    0    NA  0 100.0   6    2  0           489 #> 490 31  24.0    0    NA  0   9.5   5    1  0           490 #> 491 31  24.0    0    NA  0  45.0   6    2  0           491 #> 492 31  36.0    0    NA  0   7.8   5    1  0           492 #> 493 31  36.0    0    NA  0  30.0   6    2  0           493 #> 494 31  48.0    0    NA  0   6.4   5    1  0           494 #> 495 31  48.0    0    NA  0  24.0   6    2  0           495 #> 496 31  72.0    0    NA  0   4.5   5    1  0           496 #> 497 31  72.0    0    NA  0  22.0   6    2  0           497 #> 498 31  96.0    0    NA  0   3.4   5    1  0           498 #> 499 31  96.0    0    NA  0  28.0   6    2  0           499 #> 500 31 120.0    0    NA  0   2.5   5    1  0           500 #> 501 31 120.0    0    NA  0  42.0   6    2  0           501 #> 502 32   0.0    1  93.0  0    NA   1    0  0           502 #> 503 32   0.0    0    NA  0 100.0   6    2  0           503 #> 504 32  24.0    0    NA  0   8.9   5    1  0           504 #> 505 32  24.0    0    NA  0  36.0   6    2  0           505 #> 506 32  36.0    0    NA  0   7.7   5    1  0           506 #> 507 32  36.0    0    NA  0  27.0   6    2  0           507 #> 508 32  48.0    0    NA  0   6.9   5    1  0           508 #> 509 32  48.0    0    NA  0  24.0   6    2  0           509 #> 510 32  72.0    0    NA  0   4.4   5    1  0           510 #> 511 32  72.0    0    NA  0  23.0   6    2  0           511 #> 512 32  96.0    0    NA  0   3.5   5    1  0           512 #> 513 32  96.0    0    NA  0  20.0   6    2  0           513 #> 514 32 120.0    0    NA  0   2.5   5    1  0           514 #> 515 32 120.0    0    NA  0  22.0   6    2  0           515"},{"path":"/reference/getStandardColNames.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine standardized rxode2 column names from data — getStandardColNames","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"Determine standardized rxode2 column names data","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data)"},{"path":"/reference/getStandardColNames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"data data.frame source column names","code":""},{"path":"/reference/getStandardColNames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"named character vector names standardized names values either name column data NA column present data.","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data.frame(ID=1, DV=2, Time=3, CmT=4)) #>     id   time    amt   rate    dur   evid    cmt     ss     ii   addl     dv  #>   \"ID\" \"Time\"     NA     NA     NA     NA  \"CmT\"     NA     NA     NA   \"DV\"  #>    mdv   dvid   cens  limit  #>     NA     NA     NA     NA"},{"path":"/reference/modelUnitConversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit conversion for pharmacokinetic models — modelUnitConversion","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"Unit conversion pharmacokinetic models","code":""},{"path":"/reference/modelUnitConversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion(   dvu = NA_character_,   amtu = NA_character_,   timeu = NA_character_,   volumeu = NA_character_ )"},{"path":"/reference/modelUnitConversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"dvu, amtu, timeu units DV, AMT, TIME columns data volumeu units volume parameters model","code":""},{"path":"/reference/modelUnitConversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"list names units associated parameter (\"amtu\", \"clearanceu\", \"volumeu\", \"timeu\", \"dvu\") numeric value multiply modeled estimate (example, cp) model consistent data units.","code":""},{"path":[]},{"path":"/reference/modelUnitConversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion(dvu = \"ng/mL\", amtu = \"mg\", timeu = \"hr\", volumeu = \"L\") #> Loading required namespace: testthat #> $amtu #> [1] \"mg\" #>  #> $clearanceu #> [1] \"L/h\" #>  #> $volumeu #> [1] \"L\" #>  #> $timeu #> [1] \"hr\" #>  #> $dvu #> [1] \"ng/mL\" #>  #> $cmtu #> [1] \"mg/L\" #>  #> $dvConversion #> [1] 1000 #>"},{"path":"/reference/monolixControl.html","id":null,"dir":"Reference","previous_headings":"","what":"Monolix Controller for nlmixr2 — monolixControl","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Monolix Controller nlmixr2","code":""},{"path":"/reference/monolixControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Monolix Controller for nlmixr2 — monolixControl","text":"","code":"monolixControl(   nbSSDoses = 7,   useLinearization = FALSE,   stiff = FALSE,   addProp = c(\"combined2\", \"combined1\"),   exploratoryAutoStop = FALSE,   smoothingAutoStop = FALSE,   burnInIterations = 5,   smoothingIterations = 200,   exploratoryIterations = 250,   simulatedAnnealingIterations = 250,   exploratoryInterval = 200,   exploratoryAlpha = 0,   omegaTau = 0.95,   errorModelTau = 0.95,   variability = c(\"none\", \"firstStage\", \"decreasing\"),   runCommand = getOption(\"babelmixr2.monolix\", \"\"),   rxControl = NULL,   sumProd = FALSE,   optExpression = TRUE,   calcTables = TRUE,   compress = TRUE,   ci = 0.95,   sigdigTable = NULL,   absolutePath = FALSE,   modelName = NULL,   muRefCovAlg = TRUE,   ... )"},{"path":"/reference/monolixControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Monolix Controller for nlmixr2 — monolixControl","text":"nbSSDoses Number steady state doses (default 7) useLinearization Use linearization log likelihood fim. stiff boolean using stiff ODE solver addProp specifies type additive plus proportional   errors, one standard deviations add (combined1)   type variances add (combined2). combined1 error type can described following equation: $$y = f + (+ b\\times f^c) \\times \\varepsilon$$ combined2 error model can described following equation: $$y = f + \\sqrt{^2 + b^2\\times f^{2\\times c}} \\times \\varepsilon$$ : - y represents observed value - f represents predicted value -  additive standard deviation - b proportional/power standard deviation - c power exponent (proportional case c=1) exploratoryAutoStop logical turn exploratory phase auto-stop SAEM (default 250) smoothingAutoStop Boolean indicating smoothing automatically stop (default FALSE) burnInIterations Number burn iterations smoothingIterations Number smoothing iterations exploratoryIterations Number iterations exploratory phase (default 250) simulatedAnnealingIterations Number simulating annealing iterations exploratoryInterval Minimum number iterations exploratory phase (default 200) exploratoryAlpha Convergence memory exploratory phase (used exploratoryAutoStop TRUE) omegaTau Proportional rate variance simulated annealing errorModelTau Proportional rate error model simulated annealing variability describes methodology parameters without variability.  : - Fixed throughout (none) - Variability first stage (firstStage) - Decreasing reaches fixed value (decreasing) runCommand shell command function run monolix; can specify default options(\"babelmixr2.monolix\"=\"runMonolix\"). empty 'lixoftConnectors' available, use lixoftConnectors run monolix. See details function usage. rxControl `rxode2` ODE solving options fitting, created `rxControl()` sumProd boolean indicating model change multiplication high precision multiplication sums high precision sums using PreciseSums package.  default FALSE. optExpression Optimize rxode2 expression speed calculation. default turned . calcTables boolean determine foceiFit calculate tables. default TRUE compress object compressed items ci Confidence level tables.  default 0.95 95% confidence. sigdigTable Significant digits final output table. specified, matches significant digits `sigdig` optimization algorithm.  `sigdig` NULL, use 3. absolutePath Boolean indicating absolute path used monolix runs modelName Model name used generate NONMEM output.  NULL try infer model name (x clear).  Otherwise use character outputs. muRefCovAlg controls algebraic expressions can   mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#`      algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old   `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model.  Therefore, default `TRUE`. ... Ignored parameters","code":""},{"path":"/reference/monolixControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Monolix Controller for nlmixr2 — monolixControl","text":"monolix control object","code":""},{"path":"/reference/monolixControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Monolix Controller for nlmixr2 — monolixControl","text":"runCommand given string, called system() command like: runCommand mlxtran. example, runCommand=\"'/path//monolix/mlxbsub2021' -p \" command line used look like following: '/path//monolix/mlxbsub2021' monolix.mlxtran runCommand given function, called FUN(mlxtran, directory, ui) run Monolix.  allows run Monolix way may need, long can write R.  babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting Monolix.","code":""},{"path":"/reference/monolixControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Matthew Fidler","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Estimate starting parameters using PKNCA","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"","code":"# S3 method for pknca nlmixr2Est(env, ...)"},{"path":"/reference/nlmixr2Est.pknca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"env Environment nlmixr2 estimation routines. needs : - rxode2 ui object `$ui` - data fit estimation routine `$data` - control estimation routine's control options `$ui` ... arguments provided `nlmixr2Est()` provided flexibility currently used inside nlmixr","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"model updated starting parameters.  model new element named \"nca\" available includes PKNCA results used calculation.","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Parameters estimated follows: ka 4 half-lives Tmax higher 3:  log(2)/(tmax/4) vc Inverse dose-normalized Cmax cl Estimated median clearance vp,vp22- 4-fold vc, respectively default, controlled vpMult vp2Mult arguments pkncaControl q,q2 0.5- 0.25-fold cl, respectively default, controlled qMult q2Mult arguments pkncaControl bounds parameter estimates set 10% first percentile 10 times 99th percentile.  (ka, lower bound set lower 10% first percentile 0.03 upper bound modified 10 times 99th percentile.) Parameter estimation methods may changed future version.","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"","code":"nmGetDistributionMonolixLines(line)  # S3 method for rxUi nmGetDistributionMonolixLines(line)  # S3 method for norm nmGetDistributionMonolixLines(line)"},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Lines estimation monolix","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Matthew Fidler","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"","code":"nmGetDistributionNonmemLines(line)  # S3 method for rxUi nmGetDistributionNonmemLines(line)  # S3 method for norm nmGetDistributionNonmemLines(line)"},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Lines estimation nonmem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Matthew Fidler","code":""},{"path":"/reference/nonmemControl.html","id":null,"dir":"Reference","previous_headings":"","what":"NONMEM estimation control — nonmemControl","title":"NONMEM estimation control — nonmemControl","text":"NONMEM estimation control","code":""},{"path":"/reference/nonmemControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl(   est = c(\"focei\", \"imp\", \"its\", \"posthoc\"),   advanOde = c(\"advan13\", \"advan8\", \"advan6\"),   cov = c(\"r,s\", \"r\", \"s\", \"\"),   maxeval = 1e+05,   tol = 6,   atol = 12,   sstol = 6,   ssatol = 12,   sigl = 12,   sigdig = 3,   print = 1,   extension = getOption(\"babelmixr2.nmModelExtension\", \".nmctl\"),   outputExtension = getOption(\"babelmixr2.nmOutputExtension\", \".lst\"),   runCommand = getOption(\"babelmixr2.nonmem\", \"\"),   iniSigDig = 5,   protectZeros = TRUE,   muRef = TRUE,   addProp = c(\"combined2\", \"combined1\"),   rxControl = NULL,   sumProd = FALSE,   optExpression = TRUE,   calcTables = TRUE,   compress = TRUE,   ci = 0.95,   sigdigTable = NULL,   readRounding = FALSE,   readBadOpt = FALSE,   niter = 100L,   isample = 1000L,   iaccept = 0.4,   iscaleMin = 0.1,   iscaleMax = 10,   df = 4,   seed = 14456,   mapiter = 1,   mapinter = 0,   noabort = TRUE,   modelName = NULL,   muRefCovAlg = TRUE,   ... )"},{"path":"/reference/nonmemControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NONMEM estimation control — nonmemControl","text":"est NONMEM estimation method advanOde ODE solving method NONMEM cov NONMEM covariance method maxeval NONMEM's maxeval (non posthoc methods) tol NONMEM tolerance ODE solving advan atol NONMEM absolute tolerance ODE solving sstol NONMEM tolerance steady state ODE solving ssatol NONMEM absolute tolerance steady state ODE solving sigl NONMEM sigl estimation option sigdig significant digits NONMEM print print number NONMEM extension NONMEM file extensions outputExtension Extension use NONMEM output listing runCommand Command run NONMEM (typically path \"nmfe75\") function.  See details information. iniSigDig many significant digits printed $THETA $OMEGA estimate zero.  Also controls zero protection numbers protectZeros Add methods protect divide zero muRef Automatically mu-reference control stream addProp, sumProd, optExpression, calcTables, compress, ci, sigdigTable Passed nlmixr2est::foceiControl rxControl Options pass rxode2::rxControl simulations readRounding Try read NONMEM output NONMEM terminated due rounding errors readBadOpt Try read NONMEM output NONMEM terminated due apparent failed optimization niter number iterations NONMEM estimation methods isample Isample argument NONMEM  estimation method iaccept Iaccept NONMEM estimation methods iscaleMin parameter IMP NONMEM method (ISCALE_MIN) iscaleMax parameter IMP NONMEM method (ISCALE_MAX) df degrees freedom IMP method seed seed NONMEM methods mapiter number map iterations IMP method mapinter MAPINTER parameter IMP method noabort Add NOABORT option $EST modelName Model name used generate NONMEM output.  NULL try infer model name (x clear).  Otherwise use character outputs. muRefCovAlg controls algebraic expressions can   mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#`      algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old   `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model.  Therefore, default `TRUE`. ... optional genRxControl argument controlling automatic rxControl generation.","code":""},{"path":"/reference/nonmemControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NONMEM estimation control — nonmemControl","text":"babelmixr2 control option generating NONMEM control stream reading back babelmixr2/nlmixr2","code":""},{"path":"/reference/nonmemControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NONMEM estimation control — nonmemControl","text":"runCommand given string, called system() command like: runCommand controlFile outputFile. example, runCommand=\"'/path//nmfe75'\" command line used look like following: '/path//nmfe75' one.cmt.nmctl one.cmt.lst runCommand given function, called FUN(ctl, directory, ui) run NONMEM.  allows run NONMEM way may need, long can write R.  babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting NONMEM.","code":""},{"path":"/reference/nonmemControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"NONMEM estimation control — nonmemControl","text":"Matthew L. Fidler","code":""},{"path":"/reference/nonmemControl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl() #> $est #> [1] \"focei\" #>  #> $cov #> [1] \"r,s\" #>  #> $advanOde #> [1] \"advan13\" #>  #> $maxeval #> [1] 1e+05 #>  #> $print #> [1] 1 #>  #> $noabort #> [1] TRUE #>  #> $iniSigDig #> [1] 5 #>  #> $tol #> [1] 6 #>  #> $atol #> [1] 12 #>  #> $sstol #> [1] 6 #>  #> $ssatol #> [1] 12 #>  #> $sigl #> [1] 12 #>  #> $muRef #> [1] TRUE #>  #> $sigdig #> [1] 3 #>  #> $protectZeros #> [1] TRUE #>  #> $runCommand #> [1] \"\" #>  #> $outputExtension #> [1] \".lst\" #>  #> $addProp #> [1] \"combined2\" #>  #> $rxControl #> $scale #> NULL #>  #> $method #> liblsoda  #>        2  #>  #> $atol #> [1] 1e-12 #>  #> $rtol #> [1] 1e-06 #>  #> $maxsteps #> [1] 70000 #>  #> $hmin #> [1] 0 #>  #> $hmax #> [1] NA #>  #> $hini #> [1] 0 #>  #> $maxordn #> [1] 12 #>  #> $maxords #> [1] 5 #>  #> $covsInterpolation #> nocb  #>    2  #>  #> $addCov #> [1] TRUE #>  #> $returnType #> rxSolve  #>       0  #>  #> $sigma #> NULL #>  #> $sigmaDf #> NULL #>  #> $nCoresRV #> [1] 1 #>  #> $sigmaIsChol #> [1] FALSE #>  #> $sigmaSeparation #> [1] \"auto\" #>  #> $sigmaXform #> identity  #>        4  #>  #> $nDisplayProgress #> [1] 10000 #>  #> $amountUnits #> [1] NA #>  #> $timeUnits #> [1] \"hours\" #>  #> $addDosing #> [1] FALSE #>  #> $stateTrim #> [1] Inf #>  #> $updateObject #> [1] FALSE #>  #> $omega #> NULL #>  #> $omegaDf #> NULL #>  #> $omegaIsChol #> [1] FALSE #>  #> $omegaSeparation #> [1] \"auto\" #>  #> $omegaXform #> variance  #>        6  #>  #> $nSub #> [1] 1 #>  #> $thetaMat #> NULL #>  #> $thetaDf #> NULL #>  #> $thetaIsChol #> [1] FALSE #>  #> $nStud #> [1] 1 #>  #> $dfSub #> [1] 0 #>  #> $dfObs #> [1] 0 #>  #> $seed #> NULL #>  #> $nsim #> NULL #>  #> $minSS #> [1] 10 #>  #> $maxSS #> [1] 1000 #>  #> $strictSS #> [1] 1 #>  #> $infSSstep #> [1] 12 #>  #> $istateReset #> [1] TRUE #>  #> $subsetNonmem #> [1] TRUE #>  #> $hmaxSd #> [1] 0 #>  #> $maxAtolRtolFactor #> [1] 0.1 #>  #> $from #> NULL #>  #> $to #> NULL #>  #> $by #> NULL #>  #> $length.out #> NULL #>  #> $iCov #> NULL #>  #> $keep #> NULL #>  #> $keepF #> character(0) #>  #> $drop #> NULL #>  #> $warnDrop #> [1] TRUE #>  #> $omegaLower #> [1] -Inf #>  #> $omegaUpper #> [1] Inf #>  #> $sigmaLower #> [1] -Inf #>  #> $sigmaUpper #> [1] Inf #>  #> $thetaLower #> [1] -Inf #>  #> $thetaUpper #> [1] Inf #>  #> $indLinPhiM #> [1] 0 #>  #> $indLinPhiTol #> [1] 1e-07 #>  #> $indLinMatExpType #> expokit  #>       2  #>  #> $indLinMatExpOrder #> [1] 6 #>  #> $idFactor #> [1] TRUE #>  #> $mxhnil #> [1] 0 #>  #> $hmxi #> [1] 0 #>  #> $warnIdSort #> [1] TRUE #>  #> $ssAtol #> [1] 1e-12 #>  #> $ssRtol #> [1] 1e-06 #>  #> $safeZero #> [1] 0 #>  #> $sumType #> pairwise  #>        1  #>  #> $prodType #> long double  #>           1  #>  #> $sensType #> advan  #>     4  #>  #> $linDiff #>    tlag       f    rate     dur   tlag2      f2   rate2    dur2  #> 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05  #>  #> $linDiffCentral #>  tlag     f  rate   dur tlag2    f2 rate2  dur2  #>  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  #>  #> $resample #> NULL #>  #> $resampleID #> [1] TRUE #>  #> $maxwhile #> [1] 100000 #>  #> $cores #> [1] 0 #>  #> $atolSens #> [1] 1e-08 #>  #> $rtolSens #> [1] 1e-06 #>  #> $ssAtolSens #> [1] 1e-08 #>  #> $ssRtolSens #> [1] 1e-06 #>  #> $simVariability #> [1] NA #>  #> $nLlikAlloc #> NULL #>  #> $useStdPow #> [1] 0 #>  #> $naTimeHandle #> ignore  #>      1  #>  #> $addlKeepsCov #> [1] FALSE #>  #> $addlDropSs #> [1] TRUE #>  #> $ssAtDoseTime #> [1] TRUE #>  #> $ss2cancelAllPending #> [1] FALSE #>  #> $.zeros #> NULL #>  #> attr(,\"class\") #> [1] \"rxControl\" #>  #> $sumProd #> [1] FALSE #>  #> $optExpression #> [1] TRUE #>  #> $calcTables #> [1] TRUE #>  #> $compress #> [1] TRUE #>  #> $ci #> [1] 0.95 #>  #> $sigdigTable #> NULL #>  #> $readRounding #> [1] FALSE #>  #> $readBadOpt #> [1] FALSE #>  #> $genRxControl #> [1] TRUE #>  #> $niter #> [1] 100 #>  #> $isample #> [1] 1000 #>  #> $iaccept #> [1] 0.4 #>  #> $iscaleMin #> [1] 0.1 #>  #> $iscaleMax #> [1] 10 #>  #> $df #> [1] 4 #>  #> $seed #> [1] 14456 #>  #> $mapiter #> [1] 1 #>  #> $modelName #> NULL #>  #> $muRefCovAlg #> [1] TRUE #>  #> attr(,\"class\") #> [1] \"nonmemControl\""},{"path":"/reference/pkncaControl.html","id":null,"dir":"Reference","previous_headings":"","what":"PKNCA estimation control — pkncaControl","title":"PKNCA estimation control — pkncaControl","text":"PKNCA estimation control","code":""},{"path":"/reference/pkncaControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PKNCA estimation control — pkncaControl","text":"","code":"pkncaControl(   concu = NA_character_,   doseu = NA_character_,   timeu = NA_character_,   volumeu = NA_character_,   vpMult = 2,   qMult = 1/2,   vp2Mult = 4,   q2Mult = 1/4,   dvParam = \"cp\",   groups = character(),   sparse = FALSE,   ncaData = NULL,   ncaResults = NULL,   rxControl = rxode2::rxControl() )"},{"path":"/reference/pkncaControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PKNCA estimation control — pkncaControl","text":"concu, doseu, timeu concentration, dose, time units source data (passed PKNCA::pknca_units_table()). volumeu compartment volume model (NULL, simplified units source data used) vpMult, qMult, vp2Mult, q2Mult Multipliers vc cl provide initial estimates vp, q, vp2, q2 dvParam parameter name model modified concentration unit conversions.  must assigned line , separate residual error model line. groups Grouping columns NCA summaries group (required sparse = TRUE) sparse concentration-time data sparse PK (commonly used small nonclinical species terminal difficult sampling) dense PK (commonly used clinical studies larger nonclinical species)? ncaData Data use calculating NCA parameters.  Typical use subset original data informative NCA. ncaResults Already computed NCA results (PKNCAresults object) bypass automatic calculations.  least following parameters must calculated NCA: tmax, cmax.dn, cl.last rxControl Control options sent rxode2::rxControl()","code":""},{"path":"/reference/pkncaControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PKNCA estimation control — pkncaControl","text":"list parameters","code":""},{"path":"/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. nlmixr2est getValidNlmixrCtl, nlmixr2Est, nmObjGetControl, nmObjGetFoceiControl, nmObjHandleControlObject nonmem2rx .nonmem2rx, nonmem2rx rxode2 .minfo, rxModelVars, rxUiGet","code":""},{"path":"/reference/rxToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to monolix syntax — rxToMonolix","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Convert RxODE syntax monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"","code":"rxToMonolix(x, ui)"},{"path":"/reference/rxToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Matthew Fidler","code":""},{"path":"/reference/rxToNonmem.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Convert RxODE syntax NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"","code":"rxToNonmem(x, ui)"},{"path":"/reference/rxToNonmem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToNonmem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Matthew Fidler","code":""},{"path":"/reference/simplifyUnit.html","id":null,"dir":"Reference","previous_headings":"","what":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"Simplify units removing repeated units numerator denominator","code":""},{"path":"/reference/simplifyUnit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(numerator = \"\", denominator = \"\")"},{"path":"/reference/simplifyUnit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"numerator numerator units (whole unit specification) denominator denominator units (NULL numerator whole unit specification)","code":""},{"path":"/reference/simplifyUnit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"units specified units numerator denominator cancelled.","code":""},{"path":"/reference/simplifyUnit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"NA \"\" numerator denominator considered unitless.","code":""},{"path":[]},{"path":"/reference/simplifyUnit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(\"kg\", \"kg/mL\") #> [1] \"mL\" # units that don't match exactly are not cancelled simplifyUnit(\"kg\", \"g/mL\") #> [1] \"kg*mL/g\""},{"path":"/news/index.html","id":"babelmixr2-012","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.2","title":"babelmixr2 0.1.2","text":"CRAN release: 2023-12-12 Handle algebraic mu expressions PKNCA controller now contains rxControl since used translation options revision load pruned ui model query compartment properties (.e. bioavailability, lag time, etc) writing NONMEM model. fix issues PK block define variables larger calculated variable can used model instead. nonmem2rx different lst file, long nonmem2rx::nminfo(file) works, successful conversion nlmixr2 fit object occur. Fix save parameter history $parHistData accommodate changes focei’s output ($parHist now derived). Changed solving options match new steady state options rxode2 NONMEM implements . Also changed iwres model account rxerr. instead err. updated rxode2 well.","code":""},{"path":"/news/index.html","id":"babelmixr2-011","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.1","title":"babelmixr2 0.1.1","text":"CRAN release: 2023-05-27 Add new method .nlmixr2 convert nonmem2rx methods nlmixr fits Dropped pmxTools favor nonmem2rx conserve methods","code":""},{"path":"/news/index.html","id":"babelmixr2-010","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.0","title":"babelmixr2 0.1.0","text":"CRAN release: 2022-10-28 Babelmixr support “monolix”, “nonmem”, “pknca” methods release. Added NEWS.md file track changes package.","code":""}]