VIM-package: 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, VIM can be used for data
from essentially any field.
ll{ Package: VIM
Version: 3.0.3
Date: 2013-01-09
Depends: R (>= 2.10),e1071,car, colorspace, nnet,
robustbase, tcltk, tkrplot, sp, vcd, Rcpp
Imports: car, colorspace,
grDevices, robustbase, stats, tcltk, sp, utils, vcd
License: GPL (>=
2)
URL: http://cran.r-project.org/package=VIM
}References
M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incomplete
data using visualization tools. 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. Journal of
Computational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.