diff --git a/DESCRIPTION b/DESCRIPTION index eb7595f..f8a61b4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,30 +1,30 @@ -Package: VIM -Version: 4.1.0 -Date: 2014-10-24 -Title: Visualization and Imputation of Missing Values -Author: Matthias Templ, Andreas Alfons, Alexander Kowarik, Bernd Prantner -Maintainer: Matthias Templ -Depends: - R (>= 3.1.0),colorspace,grid,data.table(>= 1.9.4) -Imports: - car, - grDevices, - robustbase, - stats, - sp, - vcd,MASS,nnet,e1071,methods,Rcpp -Description: This package introduces new tools for the visualization of missing - and/or imputed values, which can be used for exploring the data and the - structure of the missing and/or imputed values. Depending on this structure - of the missing values, the corresponding methods may help to identify the - mechanism generating the missing values and allows to explore the data - including missing values. In addition, the quality of imputation can be - visually explored using various univariate, bivariate, multiple and - multivariate plot methods. A graphical user interface available in the - separate package VIMGUI allows an easy handling of the implemented plot - methods. -LazyData: TRUE -License: GPL (>= 2) -URL: https://github.com/alexkowa/VIM -Repository: CRAN -LinkingTo: Rcpp +Package: VIM +Version: 4.1.0 +Date: 2014-10-24 +Title: Visualization and Imputation of Missing Values +Author: Matthias Templ, Andreas Alfons, Alexander Kowarik, Bernd Prantner +Maintainer: Matthias Templ +Depends: + R (>= 3.1.0),colorspace,grid,data.table(>= 1.9.4) +Imports: + car, + grDevices, + robustbase, + stats, + sp, + vcd,MASS,nnet,e1071,methods,Rcpp +Description: This package introduces new tools for the visualization of missing + and/or imputed values, which can be used for exploring the data and the + structure of the missing and/or imputed values. Depending on this structure + of the missing values, the corresponding methods may help to identify the + mechanism generating the missing values and allows to explore the data + including missing values. In addition, the quality of imputation can be + visually explored using various univariate, bivariate, multiple and + multivariate plot methods. A graphical user interface available in the + separate package VIMGUI allows an easy handling of the implemented plot + methods. +LazyData: TRUE +License: GPL (>= 2) +URL: https://github.com/alexkowa/VIM +Repository: CRAN +LinkingTo: Rcpp diff --git a/NAMESPACE b/NAMESPACE index 312f78e..3aacfeb 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,116 +1,116 @@ -# Generated by roxygen2 (4.0.2): do not edit by hand - -S3method(aggr,data.frame) -S3method(aggr,default) -S3method(aggr,survey.design) -S3method(barMiss,data.frame) -S3method(barMiss,default) -S3method(barMiss,survey.design) -S3method(colormapMiss,data.frame) -S3method(colormapMiss,default) -S3method(colormapMiss,survey.design) -S3method(growdotMiss,data.frame) -S3method(growdotMiss,default) -S3method(growdotMiss,survey.design) -S3method(histMiss,data.frame) -S3method(histMiss,default) -S3method(histMiss,survey.design) -S3method(hotdeck,data.frame) -S3method(hotdeck,default) -S3method(hotdeck,survey.design) -S3method(irmi,data.frame) -S3method(irmi,default) -S3method(irmi,survey.design) -S3method(kNN,data.frame) -S3method(kNN,default) -S3method(kNN,survey.design) -S3method(mapMiss,data.frame) -S3method(mapMiss,default) -S3method(mapMiss,survey.design) -S3method(marginmatrix,data.frame) -S3method(marginmatrix,default) -S3method(marginmatrix,survey.design) -S3method(matrixplot,data.frame) -S3method(matrixplot,default) -S3method(matrixplot,survey.design) -S3method(mosaicMiss,data.frame) -S3method(mosaicMiss,default) -S3method(mosaicMiss,survey.design) -S3method(parcoordMiss,data.frame) -S3method(parcoordMiss,default) -S3method(parcoordMiss,survey.design) -S3method(pbox,data.frame) -S3method(pbox,default) -S3method(pbox,survey.design) -S3method(plot,aggr) -S3method(prepare,data.frame) -S3method(prepare,default) -S3method(prepare,survey.design) -S3method(print,aggr) -S3method(print,summary.aggr) -S3method(regressionImp,data.frame) -S3method(regressionImp,default) -S3method(regressionImp,survey.design) -S3method(scattmatrixMiss,data.frame) -S3method(scattmatrixMiss,default) -S3method(scattmatrixMiss,survey.design) -S3method(summary,aggr) -export(aggr) -export(alphablend) -export(barMiss) -export(bgmap) -export(colSequence) -export(colSequenceHCL) -export(colSequenceRGB) -export(colormapMiss) -export(colormapMissLegend) -export(countInf) -export(countNA) -export(existsVm) -export(getVm) -export(gowerD) -export(growdotMiss) -export(histMiss) -export(hotdeck) -export(initialise) -export(irmi) -export(kNN) -export(mapMiss) -export(marginmatrix) -export(marginplot) -export(matrixplot) -export(maxCat) -export(mosaicMiss) -export(pairsVIM) -export(parcoordMiss) -export(pbox) -export(plot.aggr) -export(prepare) -export(putVm) -export(regressionImp) -export(rmVm) -export(rugNA) -export(sampleCat) -export(scattJitt) -export(scattMiss) -export(scattmatrixMiss) -export(spineMiss) -export(vmGUIenvir) -export(which.minN) -import(MASS) -import(Rcpp) -import(colorspace) -import(data.table) -import(e1071) -import(grDevices) -import(grid) -import(methods) -import(nnet) -import(robustbase) -import(sp) -import(stats) -importFrom(car,bcPower) -importFrom(car,powerTransform) -importFrom(vcd,labeling_border) -importFrom(vcd,mosaic) -useDynLib(VIM) +# Generated by roxygen2 (4.0.2): do not edit by hand + +S3method(aggr,data.frame) +S3method(aggr,default) +S3method(aggr,survey.design) +S3method(barMiss,data.frame) +S3method(barMiss,default) +S3method(barMiss,survey.design) +S3method(colormapMiss,data.frame) +S3method(colormapMiss,default) +S3method(colormapMiss,survey.design) +S3method(growdotMiss,data.frame) +S3method(growdotMiss,default) +S3method(growdotMiss,survey.design) +S3method(histMiss,data.frame) +S3method(histMiss,default) +S3method(histMiss,survey.design) +S3method(hotdeck,data.frame) +S3method(hotdeck,default) +S3method(hotdeck,survey.design) +S3method(irmi,data.frame) +S3method(irmi,default) +S3method(irmi,survey.design) +S3method(kNN,data.frame) +S3method(kNN,default) +S3method(kNN,survey.design) +S3method(mapMiss,data.frame) +S3method(mapMiss,default) +S3method(mapMiss,survey.design) +S3method(marginmatrix,data.frame) +S3method(marginmatrix,default) +S3method(marginmatrix,survey.design) +S3method(matrixplot,data.frame) +S3method(matrixplot,default) +S3method(matrixplot,survey.design) +S3method(mosaicMiss,data.frame) +S3method(mosaicMiss,default) +S3method(mosaicMiss,survey.design) +S3method(parcoordMiss,data.frame) +S3method(parcoordMiss,default) +S3method(parcoordMiss,survey.design) +S3method(pbox,data.frame) +S3method(pbox,default) +S3method(pbox,survey.design) +S3method(plot,aggr) +S3method(prepare,data.frame) +S3method(prepare,default) +S3method(prepare,survey.design) +S3method(print,aggr) +S3method(print,summary.aggr) +S3method(regressionImp,data.frame) +S3method(regressionImp,default) +S3method(regressionImp,survey.design) +S3method(scattmatrixMiss,data.frame) +S3method(scattmatrixMiss,default) +S3method(scattmatrixMiss,survey.design) +S3method(summary,aggr) +export(aggr) +export(alphablend) +export(barMiss) +export(bgmap) +export(colSequence) +export(colSequenceHCL) +export(colSequenceRGB) +export(colormapMiss) +export(colormapMissLegend) +export(countInf) +export(countNA) +export(existsVm) +export(getVm) +export(gowerD) +export(growdotMiss) +export(histMiss) +export(hotdeck) +export(initialise) +export(irmi) +export(kNN) +export(mapMiss) +export(marginmatrix) +export(marginplot) +export(matrixplot) +export(maxCat) +export(mosaicMiss) +export(pairsVIM) +export(parcoordMiss) +export(pbox) +export(plot.aggr) +export(prepare) +export(putVm) +export(regressionImp) +export(rmVm) +export(rugNA) +export(sampleCat) +export(scattJitt) +export(scattMiss) +export(scattmatrixMiss) +export(spineMiss) +export(vmGUIenvir) +export(which.minN) +import(MASS) +import(Rcpp) +import(colorspace) +import(data.table) +import(e1071) +import(grDevices) +import(grid) +import(methods) +import(nnet) +import(robustbase) +import(sp) +import(stats) +importFrom(car,bcPower) +importFrom(car,powerTransform) +importFrom(vcd,labeling_border) +importFrom(vcd,mosaic) +useDynLib(VIM) diff --git a/R/hotdeck.R b/R/hotdeck.R index 277256c..94be619 100644 --- a/R/hotdeck.R +++ b/R/hotdeck.R @@ -28,7 +28,7 @@ #' @param makeNA list of length equal to the number of variables, with values, that should be converted to NA for each variable #' @param NAcond list of length equal to the number of variables, with a condition for imputing a NA #' @param impNA TRUE/FALSE whether NA should be imputed -#' @param list of length equal to the number of variables, with a donorcond condition for the donors e.g. ">5" +#' @param donorcond list of length equal to the number of variables, with a donorcond condition for the donors e.g. ">5" #' @param imp_var TRUE/FALSE if a TRUE/FALSE variables for each imputed #' variable should be created show the imputation status #' @param imp_suffix suffix for the TRUE/FALSE variables showing the imputation @@ -46,7 +46,7 @@ #' nRows <- 1e3 #' # Generate a data set with nRows rows and several variables #' x<-data.frame(x=rnorm(nRows),y=rnorm(nRows),z=sample(LETTERS,nRows,replace=TRUE), -#' d1=sample(LETTERS[1:3],nRows,rep=T),d2=sample(LETTERS[1:2],nRows,replace=TRUE), +#' d1=sample(LETTERS[1:3],nRows,replace=TRUE),d2=sample(LETTERS[1:2],nRows,replace=TRUE), #' o1=rnorm(nRows),o2=rnorm(nRows),o3=rnorm(100)) #' origX <- x #' x[sample(1:nRows,nRows/10),1] <- NA diff --git a/man/hotdeck.Rd b/man/hotdeck.Rd index 236aa5a..e34a0ab 100644 --- a/man/hotdeck.Rd +++ b/man/hotdeck.Rd @@ -1,63 +1,63 @@ -% Generated by roxygen2 (4.0.2): do not edit by hand -\name{hotdeck} -\alias{hotdeck} -\title{Hot-Deck Imputation} -\usage{ -hotdeck(data, variable = NULL, ord_var = NULL, domain_var = NULL, - makeNA = NULL, NAcond = NULL, impNA = TRUE, donorcond = NULL, - imp_var = TRUE, imp_suffix = "imp") -} -\arguments{ -\item{data}{data.frame or matrix} - -\item{variable}{variables where missing values should be imputed} - -\item{ord_var}{variables for sorting the data set before imputation} - -\item{domain_var}{variables for building domains and impute within these -domains} - -\item{makeNA}{list of length equal to the number of variables, with values, that should be converted to NA for each variable} - -\item{NAcond}{list of length equal to the number of variables, with a condition for imputing a NA} - -\item{impNA}{TRUE/FALSE whether NA should be imputed} - -\item{imp_var}{TRUE/FALSE if a TRUE/FALSE variables for each imputed -variable should be created show the imputation status} - -\item{imp_suffix}{suffix for the TRUE/FALSE variables showing the imputation -status} - -\item{list}{of length equal to the number of variables, with a donorcond condition for the donors e.g. ">5"} -} -\value{ -the imputed data set. -} -\description{ -Implementation of the popular Sequential, Random (within a domain) hot-deck -algorithm for imputation. -} -\examples{ -data(sleep) -sleepI <- hotdeck(sleep) -sleepI2 <- hotdeck(sleep,ord_var="BodyWgt",domain_var="Pred") - -set.seed(132) -nRows <- 1e3 -# Generate a data set with nRows rows and several variables -x<-data.frame(x=rnorm(nRows),y=rnorm(nRows),z=sample(LETTERS,nRows,replace=TRUE), - d1=sample(LETTERS[1:3],nRows,rep=T),d2=sample(LETTERS[1:2],nRows,replace=TRUE), - o1=rnorm(nRows),o2=rnorm(nRows),o3=rnorm(100)) -origX <- x -x[sample(1:nRows,nRows/10),1] <- NA -x[sample(1:nRows,nRows/10),2] <- NA -x[sample(1:nRows,nRows/10),3] <- NA -x[sample(1:nRows,nRows/10),4] <- NA -xImp <- hotdeck(x,ord_var = c("o1","o2","o3"),domain_var="d2") -} -\author{ -Alexander Kowarik -} -\keyword{manip} - +% Generated by roxygen2 (4.0.2): do not edit by hand +\name{hotdeck} +\alias{hotdeck} +\title{Hot-Deck Imputation} +\usage{ +hotdeck(data, variable = NULL, ord_var = NULL, domain_var = NULL, + makeNA = NULL, NAcond = NULL, impNA = TRUE, donorcond = NULL, + imp_var = TRUE, imp_suffix = "imp") +} +\arguments{ +\item{data}{data.frame or matrix} + +\item{variable}{variables where missing values should be imputed} + +\item{ord_var}{variables for sorting the data set before imputation} + +\item{domain_var}{variables for building domains and impute within these +domains} + +\item{makeNA}{list of length equal to the number of variables, with values, that should be converted to NA for each variable} + +\item{NAcond}{list of length equal to the number of variables, with a condition for imputing a NA} + +\item{impNA}{TRUE/FALSE whether NA should be imputed} + +\item{donorcond}{list of length equal to the number of variables, with a donorcond condition for the donors e.g. ">5"} + +\item{imp_var}{TRUE/FALSE if a TRUE/FALSE variables for each imputed +variable should be created show the imputation status} + +\item{imp_suffix}{suffix for the TRUE/FALSE variables showing the imputation +status} +} +\value{ +the imputed data set. +} +\description{ +Implementation of the popular Sequential, Random (within a domain) hot-deck +algorithm for imputation. +} +\examples{ +data(sleep) +sleepI <- hotdeck(sleep) +sleepI2 <- hotdeck(sleep,ord_var="BodyWgt",domain_var="Pred") + +set.seed(132) +nRows <- 1e3 +# Generate a data set with nRows rows and several variables +x<-data.frame(x=rnorm(nRows),y=rnorm(nRows),z=sample(LETTERS,nRows,replace=TRUE), + d1=sample(LETTERS[1:3],nRows,replace=TRUE),d2=sample(LETTERS[1:2],nRows,replace=TRUE), + o1=rnorm(nRows),o2=rnorm(nRows),o3=rnorm(100)) +origX <- x +x[sample(1:nRows,nRows/10),1] <- NA +x[sample(1:nRows,nRows/10),2] <- NA +x[sample(1:nRows,nRows/10),3] <- NA +x[sample(1:nRows,nRows/10),4] <- NA +xImp <- hotdeck(x,ord_var = c("o1","o2","o3"),domain_var="d2") +} +\author{ +Alexander Kowarik +} +\keyword{manip} + diff --git a/man/irmi.Rd b/man/irmi.Rd index fb15918..5c59e72 100644 --- a/man/irmi.Rd +++ b/man/irmi.Rd @@ -1,118 +1,118 @@ -% Generated by roxygen2 (4.0.2): do not edit by hand -\name{irmi} -\alias{irmi} -\title{Iterative robust model-based imputation (IRMI)} -\usage{ -irmi(x, eps = 5, maxit = 100, mixed = NULL, mixed.constant = NULL, - count = NULL, step = FALSE, robust = FALSE, takeAll = TRUE, - noise = TRUE, noise.factor = 1, force = FALSE, robMethod = "MM", - force.mixed = TRUE, mi = 1, addMixedFactors = FALSE, trace = FALSE, - init.method = "kNN", modelFormulas = NULL, multinom.method = "multinom") -} -\arguments{ -\item{x}{data.frame or matrix} - -\item{eps}{threshold for convergency} - -\item{maxit}{maximum number of iterations} - -\item{mixed}{column index of the semi-continuous variables} - -\item{mixed.constant}{vector with length equal to the number of -semi-continuous variables specifying the point of the semi-continuous -distribution with non-zero probability} - -\item{count}{column index of count variables} - -\item{step}{a stepwise model selection is applied when the parameter is set -to TRUE} - -\item{robust}{if TRUE, robust regression methods will be applied} - -\item{takeAll}{takes information of (initialised) missings in the response -as well for regression imputation.} - -\item{noise}{irmi has the option to add a random error term to the imputed -values, this creates the possibility for multiple imputation. The error term -has mean 0 and variance corresponding to the variance of the regression -residuals.} - -\item{noise.factor}{amount of noise.} - -\item{force}{if TRUE, the algorithm tries to find a solution in any case, -possible by using different robust methods automatically.} - -\item{robMethod}{regression method when the response is continuous.} - -\item{force.mixed}{if TRUE, the algorithm tries to find a solution in any -case, possible by using different robust methods automatically.} - -\item{mi}{number of multiple imputations.} - -\item{addMixedFactors}{if TRUE add additional factor variable for each mixed variable as X variable in the regression} - -\item{trace}{Additional information about the iterations when trace equals -TRUE.} - -\item{init.method}{Method for initialization of missing values (kNN or -median)} - -\item{modelFormulas}{a named list with the name of variables for the rhs of the formulas, which must contain a rhs formula for each variable with missing values, it should look like list(y1=c("x1","x2"),y2=c("x1","x3")) - -if factor variables for the mixed variables should be created for the -regression models} - -\item{multinom.method}{Method for estimating the multinomial models -(current default and only available method is multinom)} -} -\value{ -the imputed data set. -} -\description{ -In each step of the iteration, one variable is used as a response variable -and the remaining variables serve as the regressors. -} -\details{ -The method works sequentially and iterative. The method can deal with a -mixture of continuous, semi-continuous, ordinal and nominal variables -including outliers. - -A full description of the method will be uploaded soon in form of a package -vignette. -} -\examples{ -data(sleep) -irmi(sleep) - -data(testdata) -imp_testdata1 <- irmi(testdata$wna,mixed=testdata$mixed) - -# mixed.constant != 0 (-10) -testdata$wna$m1[testdata$wna$m1==0] <- -10 -testdata$wna$m2 <- log(testdata$wna$m2+0.001) -imp_testdata2 <- irmi(testdata$wna,mixed=testdata$mixed,mixed.constant=c(-10,log(0.001))) -imp_testdata2$m2 <- exp(imp_testdata2$m2)-0.001 - -#example with fixed formulas for the variables with missing -form=list( -NonD=c("BodyWgt","BrainWgt"), -Dream=c("BodyWgt","BrainWgt"), -Sleep=c("BrainWgt"), -Span=c("BodyWgt"), -Gest=c("BodyWgt","BrainWgt") -) -irmi(sleep,modelFormulas=form,trace=TRUE) -} -\author{ -Matthias Templ, Alexander Kowarik -} -\references{ -M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwise -regression imputation using standard and robust methods. \emph{Journal of -Computational Statistics and Data Analysis}, Vol. 55, pp. 2793-2806. -} -\seealso{ -\code{\link[mi]{mi}} -} -\keyword{manip} - +% Generated by roxygen2 (4.0.2): do not edit by hand +\name{irmi} +\alias{irmi} +\title{Iterative robust model-based imputation (IRMI)} +\usage{ +irmi(x, eps = 5, maxit = 100, mixed = NULL, mixed.constant = NULL, + count = NULL, step = FALSE, robust = FALSE, takeAll = TRUE, + noise = TRUE, noise.factor = 1, force = FALSE, robMethod = "MM", + force.mixed = TRUE, mi = 1, addMixedFactors = FALSE, trace = FALSE, + init.method = "kNN", modelFormulas = NULL, multinom.method = "multinom") +} +\arguments{ +\item{x}{data.frame or matrix} + +\item{eps}{threshold for convergency} + +\item{maxit}{maximum number of iterations} + +\item{mixed}{column index of the semi-continuous variables} + +\item{mixed.constant}{vector with length equal to the number of +semi-continuous variables specifying the point of the semi-continuous +distribution with non-zero probability} + +\item{count}{column index of count variables} + +\item{step}{a stepwise model selection is applied when the parameter is set +to TRUE} + +\item{robust}{if TRUE, robust regression methods will be applied} + +\item{takeAll}{takes information of (initialised) missings in the response +as well for regression imputation.} + +\item{noise}{irmi has the option to add a random error term to the imputed +values, this creates the possibility for multiple imputation. The error term +has mean 0 and variance corresponding to the variance of the regression +residuals.} + +\item{noise.factor}{amount of noise.} + +\item{force}{if TRUE, the algorithm tries to find a solution in any case, +possible by using different robust methods automatically.} + +\item{robMethod}{regression method when the response is continuous.} + +\item{force.mixed}{if TRUE, the algorithm tries to find a solution in any +case, possible by using different robust methods automatically.} + +\item{mi}{number of multiple imputations.} + +\item{addMixedFactors}{if TRUE add additional factor variable for each mixed variable as X variable in the regression} + +\item{trace}{Additional information about the iterations when trace equals +TRUE.} + +\item{init.method}{Method for initialization of missing values (kNN or +median)} + +\item{modelFormulas}{a named list with the name of variables for the rhs of the formulas, which must contain a rhs formula for each variable with missing values, it should look like list(y1=c("x1","x2"),y2=c("x1","x3")) + +if factor variables for the mixed variables should be created for the +regression models} + +\item{multinom.method}{Method for estimating the multinomial models +(current default and only available method is multinom)} +} +\value{ +the imputed data set. +} +\description{ +In each step of the iteration, one variable is used as a response variable +and the remaining variables serve as the regressors. +} +\details{ +The method works sequentially and iterative. The method can deal with a +mixture of continuous, semi-continuous, ordinal and nominal variables +including outliers. + +A full description of the method will be uploaded soon in form of a package +vignette. +} +\examples{ +data(sleep) +irmi(sleep) + +data(testdata) +imp_testdata1 <- irmi(testdata$wna,mixed=testdata$mixed) + +# mixed.constant != 0 (-10) +testdata$wna$m1[testdata$wna$m1==0] <- -10 +testdata$wna$m2 <- log(testdata$wna$m2+0.001) +imp_testdata2 <- irmi(testdata$wna,mixed=testdata$mixed,mixed.constant=c(-10,log(0.001))) +imp_testdata2$m2 <- exp(imp_testdata2$m2)-0.001 + +#example with fixed formulas for the variables with missing +form=list( +NonD=c("BodyWgt","BrainWgt"), +Dream=c("BodyWgt","BrainWgt"), +Sleep=c("BrainWgt"), +Span=c("BodyWgt"), +Gest=c("BodyWgt","BrainWgt") +) +irmi(sleep,modelFormulas=form,trace=TRUE) +} +\author{ +Matthias Templ, Alexander Kowarik +} +\references{ +M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwise +regression imputation using standard and robust methods. \emph{Journal of +Computational Statistics and Data Analysis}, Vol. 55, pp. 2793-2806. +} +\seealso{ +\code{\link[mi]{mi}} +} +\keyword{manip} + diff --git a/man/vmGUIenvir.Rd b/man/vmGUIenvir.Rd index 9f8f419..b991440 100644 --- a/man/vmGUIenvir.Rd +++ b/man/vmGUIenvir.Rd @@ -1,50 +1,50 @@ -% Generated by roxygen2 (4.0.2): do not edit by hand -\docType{data} -\name{vmGUIenvir} -\alias{existsVm} -\alias{getVm} -\alias{putVm} -\alias{rmVm} -\alias{vmGUIenvir} -\title{Environment for the GUI for Visualization and Imputation of Missing Values} -\format{\preformatted{ -}} -\usage{ -vmGUIenvir - -putVm(x, value) - -getVm(x, mode = "any") - -existsVm(x, mode = "any") - -rmVm(...) -} -\arguments{ -\item{x}{object name} - -\item{value}{value to be assigned to x} - -\item{mode}{see 'exists'} - -\item{...}{see 'rm'} -} -\description{ -Location were everything from package VIM and VIMGUI is stored. -} -\details{ -Internal information regarding the VIM GUI is stored in the environment -\code{vmGUIenvir}. -} -\author{ -Andreas Alfons, based on an initial design by Matthias Templ, -modifications by Bernd Prantner -} -\references{ -M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incomplete -data using visualization tools. \emph{Journal of Advances in Data Analysis -and Classification}, Online first. DOI: 10.1007/s11634-011-0102-y. -} -\keyword{hplot} -\keyword{multivariate} - +% Generated by roxygen2 (4.0.2): do not edit by hand +\docType{data} +\name{vmGUIenvir} +\alias{existsVm} +\alias{getVm} +\alias{putVm} +\alias{rmVm} +\alias{vmGUIenvir} +\title{Environment for the GUI for Visualization and Imputation of Missing Values} +\format{\preformatted{ +}} +\usage{ +vmGUIenvir + +putVm(x, value) + +getVm(x, mode = "any") + +existsVm(x, mode = "any") + +rmVm(...) +} +\arguments{ +\item{x}{object name} + +\item{value}{value to be assigned to x} + +\item{mode}{see 'exists'} + +\item{...}{see 'rm'} +} +\description{ +Location were everything from package VIM and VIMGUI is stored. +} +\details{ +Internal information regarding the VIM GUI is stored in the environment +\code{vmGUIenvir}. +} +\author{ +Andreas Alfons, based on an initial design by Matthias Templ, +modifications by Bernd Prantner +} +\references{ +M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incomplete +data using visualization tools. \emph{Journal of Advances in Data Analysis +and Classification}, Online first. DOI: 10.1007/s11634-011-0102-y. +} +\keyword{hplot} +\keyword{multivariate} +