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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/VIM-package.R
\docType{package}
\name{VIM-package}
\alias{VIM-package}
\alias{VIM}
\title{Visualization and Imputation of Missing Values}
\description{
This package introduces new tools for the visualization of missing or
imputed values in , which can be used for exploring the data and the
structure of the missing or imputed values. Depending on this structure,
they may help to identify the mechanism generating the missing values or
errors, which may have happened in the imputation process. This knowledge is
necessary for selecting an appropriate imputation method in order to
reliably estimate the missing values. Thus the visualization tools should be
applied before imputation and the diagnostic tools afterwards.
}
\details{
Detecting missing values mechanisms is usually done by statistical tests or
models. Visualization of missing and imputed values can support the test
decision, but also reveals more details about the data structure. Most
notably, statistical requirements for a test can be checked graphically, and
problems like outliers or skewed data distributions can be discovered.
Furthermore, the included plot methods may also be able to detect missing
values mechanisms in the first place.
A graphical user interface available in the package VIMGUI allows an easy
handling of the plot methods. In addition, \code{VIM} can be used for data
from essentially any field.
\tabular{ll}{ Package: \tab VIM\cr Version: \tab 3.0.3\cr Date: \tab
2013-01-09\cr Depends: \tab R (>= 2.10),e1071,car, colorspace, nnet,
robustbase, tcltk, tkrplot, sp, vcd, Rcpp\cr Imports: \tab car, colorspace,
grDevices, robustbase, stats, tcltk, sp, utils, vcd\cr License: \tab GPL (>=
2)\cr URL: \tab http://cran.r-project.org/package=VIM\cr }
}
\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.
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.
}
\author{
Matthias Templ, Andreas Alfons, Alexander Kowarik, Bernd Prantner
Maintainer: Matthias Templ \href{mailto:templ@tuwien.ac.at}{templ@tuwien.ac.at}
}
\keyword{package}
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