TKRmatrixplot: Matrix plot

In VIMGUI: Visualization and Imputation of Missing Values - Graphical User Interface

Description

Create a matrix plot, in which all cells of a data matrix are visualized by rectangles. Available data is coded according to a continuous color scheme, while missing/imputed data is visualized by a clearly distinguishable color.

Usage

TKRmatrixplot(x, delimiter = NULL, hscale = NULL, vscale = NULL,
  TKRpar = list(), ...)

Arguments

x	a matrix or data.frame.
delimiter	a character-vector to distinguish between variables and imputation-indices for imputed variables (therefore, x needs to have colnames). If given, it is used to determine the corresponding imputation-index for any imputed variable (a logical-vector indicating which values of the variable have been imputed). If such imputation-indices are found, they are used for highlighting and the colors are adjusted according to the given colors for imputed variables (see col).
hscale	horizontal scale factor for plot to be embedded in a Tcl/Tk window (see ‘Details’). The default value depends on the number of variables.
vscale	vertical scale factor for the plot to be embedded in a Tcl/Tk window (see ‘Details’). The default value depends on the number of observations.
TKRpar	a list of graphical parameters to be set for the plot to be embedded in a Tcl/Tk window (see ‘Details’ and par).
...	for matrixplot and iimagMiss, further graphical parameters to be passed to plot.window, title and axis. For TKRmatrixplot, further arguments to be passed to matrixplot.

Details

In a matrix plot, all cells of a data matrix are visualized by rectangles. Available data is coded according to a continuous color scheme. To compute the colors via interpolation, the variables are first scaled to the interval [0,1]. Missing/imputed values can then be visualized by a clearly distinguishable color. It is thereby possible to use colors in the HCL or RGB color space. A simple way of visualizing the magnitude of the available data is to apply a greyscale, which has the advantage that missing/imputed values can easily be distinguished by using a color such as red/orange. Note that -Inf and Inf are always assigned the begin and end color, respectively, of the continuous color scheme.

Additionally, the observations can be sorted by the magnitude of a selected variable. If interactive is TRUE, clicking in a column redraws the plot with observations sorted by the corresponding variable. Clicking anywhere outside the plot region quits the interactive session.

TKRmatrixplot behaves like matrixplot, but uses tkrplot to embed the plot in a Tcl/Tk window. This is useful if the number of observations and/or variables is large, because scrollbars allow to move from one part of the plot to another.

Note

This is a much more powerful extension to the function imagmiss in the former CRAN package dprep.

iimagMiss is deprecated and may be omitted in future versions of VIM. Use matrixplot instead.

Author(s)

Andreas Alfons, Matthias Templ, modifications by Bernd Prantner

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.

A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16

Examples

data(sleep, package = "VIM")
## for missing values
x <- sleep[, -(8:10)]
x[,c(1,2,4,6,7)] <- log10(x[,c(1,2,4,6,7)])
matrixplot(x, sortby = "BrainWgt")

## for imputed values
x_imp <- kNN(sleep[, -(8:10)])
x_imp[,c(1,2,4,6,7)] <- log10(x_imp[,c(1,2,4,6,7)])
matrixplot(x_imp, delimiter = "_imp", sortby = "BrainWgt")

VIMGUI documentation built on May 2, 2019, 10 a.m.