VIM (version 3.0.2)

marginmatrix: Marginplot Matrix

Description

Create a scatterplot matrix with information about missing/imputed values in the plot margins of each panel.

Usage

marginmatrix(x, delimiter = NULL, col = c("skyblue","red","red4",
    "orange","orange4"), alpha = NULL, ...)
TKRmarginmatrix(x, delimiter = NULL, col = c("skyblue","red","red4",
    "orange","orange4"), alpha = 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 correspo

col

a vector of length five giving the colors to be used in the marginplots in the off-diagonal panels. The first color is used for the scatterplot and the boxplots for the available data, the second/fourth color for the univariate

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.

...

further arguments and graphical parameters to be passed to pairsVIM and marginplot. par("oma") will be set appropriately

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

marginmatrix uses pairsVIM with a panel function based on marginplot. The graphical parameter oma will be set unless supplied as an argument. TKRmarginmatrix behaves like marginmatrix, 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.

See Also

marginplot, pairsVIM, scattmatrixMiss

Examples

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

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