gclus (version 1.3.1)

cpairs: Enhanced scatterplot matrix

Description

This function draws a scatterplot matrix of data. Variables may be reordered and panels colored in the display.

Usage

cpairs(data, order = NULL, panel.colors = NULL, border.color = "grey70", show.points = TRUE, ...)

Arguments

data

a numeric matrix

order

the order of variables. Default is the order in data.

panel.colors

a matrix of panel colors. If supplied, dimensions should match those of the pairs plot. Diagonal entries are ignored.

border.color

used for panel border.

show.points

If FALSE, no points are drawn.

…

graphical parameters passed to pairs.default.

References

Hurley, Catherine B. ``Clustering Visualisations of Multidimensional Data'', to appear in JCGS.

See Also

pairs, cparcoord, dmat.color,colpairs, order.single.

Examples

# NOT RUN {
data(USJudgeRatings)
judge.cor <- cor(USJudgeRatings)
judge.color <- dmat.color(judge.cor)
# Colors variables by their correlation.
cpairs(USJudgeRatings,panel.colors=judge.color,pch=".",gap=.5)
judge.o <- order.single(judge.cor)
# Reorder variables so that those with highest correlation 
# are close to the  diagonal.
cpairs(USJudgeRatings,judge.o,judge.color,pch=".",gap=.5)

# Specify your own color scheme
judge.color <- dmat.color(judge.cor, breaks=c(-1,0,.5,.9,1), colors = 
cm.colors(4))

data(bank)
# m is a homogeneity measure of each pairwise variable plot
m <- -colpairs(scale(bank[,-1]), partition.crit,gfun=gave,groups=bank[,1])

# Color panels by level of m and reorder variables so that
# pairs with high m are near the diagonal. Panels shown
# in pink have the highest amount of group homogeneity, as measured by 
# gave.
cpairs(bank[,-1],order=order.single(m), panel.colors=dmat.color(m),
gap=.3,col=c("purple","black")[bank[,"Status"]+1],
pch=c(5,3)[bank[,"Status"]+1])

# }