DescTools (version 0.99.6)

PlotCorr: Plot a Correlation Matrix

Description

This function produces a graphical display of a correlation matrix. The cells of the matrix can be shaded or colored to show the correlation value.

Usage

PlotCorr(x, cols = colorRampPalette(c("red", "white", "blue"), space = "rgb")(20), 
         breaks = seq(-1, 1, length = length(cols) + 1), 
         args.colorlegend = NULL, xaxt = par("xaxt"), yaxt = par("yaxt"), 
         cex.axis = 0.8, las = 2, mar = c(3, 8, 8, 8), ...)

Arguments

x

x is a correlation matrix to be visualized.

cols

the colors for shading the matrix

breaks

a set of breakpoints for the colours: must give one more breakpoint than colour. These are passed to image() function. If breaks is specified then the algorithm used follows cut, so intervals are

args.colorlegend

list of arguments for the ColorLegend. Use NA if no color legend should be painted.

xaxt

parameter to define, whether to draw an x-axis, defaults to "n".

yaxt

parameter to define, whether to draw an y-axis, defaults to "n".

cex.axis

character extension for the axis labels.

las

the style of axis labels.

mar

sets the margins, defaults to mar = c(3, 8, 8, 8) as we need a bit more room on the right.

...

the dots are passed to the function image, which produces the plot.

Value

• no values returned.

See Also

image, ColorLegend, corrgram()

Examples

m <- cor(d.pizza[,WhichNumerics(d.pizza)], use="pairwise.complete.obs")

PlotCorr(m, cols=colorRampPalette(c("red", "black", "green"), space = "rgb")(20))
PlotCorr(m, cols=colorRampPalette(c("red", "black", "green"), space = "rgb")(20), 
         args.colorlegend=NA)

m <- PairApply(d.diamonds[,WhichFactors(d.diamonds)], CramerV)
PlotCorr(m, cols = colorRampPalette(c("white", "steelblue"), space = "rgb")(20),
         breaks=seq(0, 1, length=21),
         args.colorlegend = list(labels=sprintf("%.1f", seq(1, 0, length = 11)))
)
title(main="Cramer's V", line=2)
text(x=rep(1:ncol(m),ncol(m)), y=rep(1:ncol(m),each=ncol(m)), 
     label=sprintf("%0.2f", m[,ncol(m):1]), cex=0.8, xpd=TRUE)

# Spearman correlation on ordinal factors
csp <- cor(data.frame(lapply(d.diamonds[,c("carat", "clarity", "cut", "polish", 
                      "symmetry", "price")], as.numeric)), method="spearman")
PlotCorr(csp)