TKRparcoordMiss: Parallel coordinate plot with information about...

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

Description

Parallel coordinate plot with adjustments for missing/imputed values. Missing values in the plotted variables may be represented by a point above the corresponding coordinate axis to prevent disconnected lines. In addition, observations with missing/imputed values in selected variables may be highlighted.

Usage

TKRparcoordMiss(x, delimiter = NULL, highlight = NULL,
  selection = c("any", "all"), plotvars = NULL, plotNA = TRUE,
  col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"),
  alpha = NULL, hscale = NULL, vscale = 1, 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).
highlight	a vector giving the variables to be used for highlighting. If NULL (the default), all variables are used for highlighting.
selection	the selection method for highlighting missing/imputed values in multiple highlight variables. Possible values are "any" (highlighting of missing/imputed values in any of the highlight variables) and "all" (highlighting of missing/imputed values in all of the highlight variables).
plotvars	a vector giving the variables to be plotted. If NULL (the default), all variables are plotted.
plotNA	a logical indicating whether missing values in the plot variables should be represented by a point above the corresponding coordinate axis to prevent disconnected lines.
col	if plotNA is TRUE, a vector of length six giving the colors to be used for observations with different combinations of observed and missing/imputed values in the plot variables and highlight variables (vectors of length one or two are recycled). Otherwise, a vector of length two giving the colors for non-highlighted and highlighted observations (if a single color is supplied, it is used for both).
alpha	a numeric value between 0 and 1 giving the level of transparency of the colors, or NULL. This can be used to prevent overplotting.
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’).
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 parcoordMiss, further graphical parameters to be passed down (see par). For TKRparcoordMiss, further arguments to be passed to parcoordMiss.

Details

In parallel coordinate plots, the variables are represented by parallel axes. Each observation of the scaled data is shown as a line. Observations with missing/imputed values in selected variables may thereby be highlighted. However, plotting variables with missing values results in disconnected lines, making it impossible to trace the respective observations across the graph. As a remedy, missing values may be represented by a point above the corresponding coordinate axis, which is separated from the main plot by a small gap and a horizontal line, as determined by plotNA. Connected lines can then be drawn for all observations. Nevertheless, a caveat of this display is that it may draw attention away from the main relationships between the variables.

If interactive is TRUE, it is possible switch between this display and the standard display without the separate level for missing values by clicking in the top margin of the plot. In addition, the variables to be used for highlighting can be selected interactively. Observations with missing/imputed values in any or in all of the selected variables are highlighted (as determined by selection). A variable can be added to the selection by clicking on a coordinate axis. If a variable is already selected, clicking on its coordinate axis removes it from the selection. Clicking anywhere outside the plot region (except the top margin, if missing/imputed values exist) quits the interactive session.

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

Note

Some of the argument names and positions have changed with versions 1.3 and 1.4 due to extended functionality and for more consistency with other plot functions in VIM. For back compatibility, the arguments colcomb and xaxlabels can still be supplied to ...{} and are handled correctly. Nevertheless, they are deprecated and no longer documented. Use highlight and labels instead.

Author(s)

Andreas Alfons, Matthias Templ, modifications by Bernd Prantner

References

Wegman, E. J. (1990) Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association 85 (411), 664–675.

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

See Also

pbox

Examples

data(chorizonDL, package = "VIM")
## for missing values
parcoordMiss(chorizonDL[,c(15,101:110)], 
    plotvars=2:11, interactive = FALSE)
legend("top", col = c("skyblue", "red"), lwd = c(1,1), 
    legend = c("observed in Bi", "missing in Bi"))

## for imputed values
parcoordMiss(kNN(chorizonDL[,c(15,101:110)]), delimiter = "_imp" ,
    plotvars=2:11, interactive = FALSE)
legend("top", col = c("skyblue", "orange"), lwd = c(1,1), 
    legend = c("observed in Bi", "imputed in Bi"))

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