plotcorr: Plot correlation matrix ellipses

Description

This function plots a correlation matrix using ellipse-shaped glyphs for each entry. The ellipse represents a level curve of the density of a bivariate normal with the matching correlation.

Usage

plotcorr(corr, outline = TRUE, col = 'grey', numbers = FALSE, 
         type = c("full","lower","upper"),
	       diag = (type == "full"), bty = "n", axes = FALSE,
	       xlab = "", ylab = "", asp = 1,
	       cex.lab = par("cex.lab"), cex = 0.75*par("cex"),
	       mar = 0.1 + c(2,2,4,2), ...)

Arguments

corr

A matrix containing entries between -1 and 1 to be plotted as correlations.

outline

Whether the ellipses should be outlined in the default colour.

col

Which colour(s) to use to fill the ellipses.

numbers

Whether to plot numerical correlations in place of ellipses. If numbers is TRUE, then the correlations will be rounded to a single decimal place and placed on the plot.

type

Character. Plot "full" matrix or just "upper" or "lower" triangular part of it.

diag

Logical. Plot diagonal elements or not.

bty, axes, xlab, ylab, asp, mar, cex.lab, ...

Graphical parameters which will be passed to plot when plotting.

cex

Graphical parameter which will be passed to text when plotting.

Details

The ellipses being plotted will be tangent to a unit character square, with the shape chosen to match the required correlation. If numbers = FALSE, the col vector will be recycled to colour each of the ellipses; if TRUE, it will be ignored.

References

Murdoch, D.J. and Chow, E.D. (1996). A graphical display of large correlation matrices. The American Statistician 50, 178-180.

Examples

Run this code

# NOT RUN {
save.par <- par(ask = interactive())

# Plot the correlation matrix for the mtcars data full model fit 
data(mtcars)
fit <- lm(mpg ~ ., mtcars)
plotcorr(summary(fit, correlation = TRUE)$correlation)

# Plot a second figure with numbers in place of the
# ellipses
plotcorr(summary(fit, correlation = TRUE)$correlation, numbers = TRUE)

# Colour the ellipses to emphasize the differences.  The color range
# is based on RColorBrewer's Reds and Blues (suggested by Gregor Gorjanc)

corr.mtcars <- cor(mtcars)
ord <- order(corr.mtcars[1,])
xc <- corr.mtcars[ord, ord]
colors <- c("#A50F15","#DE2D26","#FB6A4A","#FCAE91","#FEE5D9","white",
            "#EFF3FF","#BDD7E7","#6BAED6","#3182BD","#08519C")   
plotcorr(xc, col=colors[5*xc + 6])

plotcorr(xc, col=colors[5*xc + 6], type = "upper")
plotcorr(xc, col=colors[5*xc + 6], type = "lower", diag = TRUE)
par(save.par)
# }