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.
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.
|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)|
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.