scattmatrixMiss: Scatterplot matrix with information about missing/imputed values

Description

Scatterplot matrix in which observations with missing/imputed values in certain variables are highlighted.

Usage

scattmatrixMiss(x, delimiter = NULL, highlight = NULL, 
    selection = c("any","all"), plotvars = NULL, col = c("skyblue",
    "red","orange"), alpha = NULL, pch = c(1,3), lty = par("lty"),
    diagonal = c("density","none"), interactive = TRUE, ...)
TKRscattmatrixMiss(x, delimiter = NULL, highlight = NULL,
    selection = c("any","all"), plotvars = NULL, col = c("skyblue",
    "red","orange"), alpha = NULL, ..., hscale = NULL,
    vscale = NULL, TKRpar = list())

Arguments

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 correspo

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

plotvars

a vector giving the variables to be plotted. If NULL (the default), all variables are plotted.

col

a vector of length three giving the colors to be used in the plot. The second/third color will be used for highlighting missing/imputed values.

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.

pch

a vector of length two giving the plot characters. The second plot character will be used for the highlighted observations.

lty

a vector of length two giving the line types for the density plots in the diagonal panels (if diagonal="density"). The second line type is used for the highlighted observations. If a single value is supplied, it i

diagonal

a character string specifying the plot to be drawn in the diagonal panels. Possible values are "density" (density plots for non-highlighted and highlighted observations) and "none".

interactive

a logical indicating whether the variables to be used for highlighting can be selected interactively (see Details).

...

for scattmatrixMiss, further arguments and graphical parameters to be passed to pairsVIM. par("oma") will be set appropriately unless supplied (see

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 variables.

TKRpar

a list of graphical parameters to be set for the plot to be embedded in a Tcl/Tk window (see Details and par).

Details

scattmatrixMiss uses pairsVIM with a panel function that allows highlighting of missing/imputed values. If interactive=TRUE, 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 in a diagonal panel. If a variable is already selected, clicking on the corresponding diagonal panel removes it from the selection. Clicking anywhere else quits the interactive session. The graphical parameter oma will be set unless supplied as an argument. TKRscattmatrixMiss behaves like scattmatrixMiss, 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.

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.

Examples

Run this code

data(sleep, package = "VIM")
## for missing values
x <- sleep[, 1:5]
x[,c(1,2,4)] <- log10(x[,c(1,2,4)])
scattmatrixMiss(x, highlight = "Dream")

## for imputed values
x_imp <- kNN(sleep[, 1:5])
x_imp[,c(1,2,4)] <- log10(x_imp[,c(1,2,4)])
scattmatrixMiss(x_imp, delimiter = "_imp", highlight = "Dream")