cor.plot: Create an image plot for a correlation or factor matrix

Description

Correlation matrices may be shown graphically by using the image function to emphasize structure. This is a particularly useful tool for showing the structure of small correlation matrices with a clear structure. Meant for the pedagogical value of the graphic for teaching or discussing factor analysis and other multivariate techniques.

Usage

cor.plot(r,colors=FALSE, n=10,main=NULL,zlim=c(0,1))

Arguments

A correlation matrix or the output of factor.pa, factor.minres or omega.

colors

Defaults to FALSE (grey), but colors=TRUE will use topo.colors

The number of levels of shading to use. Defaults to 10

main

A title. Defaults to ``correlation plot"

zlim

The range of values to color -- defaults to 0 to 1

Details

When teaching about factor analysis or cluster analysis, it is useful to graphically display the structure of correlation matrices. This is a simple graphical display using the image function.

The difference of mat.plot with a regular image plot is that the primary diagonal goes from the top left to the lower right.

The zlim parameter defaults to 0 to 1. This means that negative correlations are treated as zero. This is advantageous when showing general factor structures, because it makes the 0 white.

Inspired, in part, by a paper by S. Dray (2008) on the number of components problem.

References

Dray, Stephane (2008) On the number of principal components: A test of dimensionality based on measurements of similarity between matrices. Computational Statistics & Data Analysis. 52, 4, 2228-2237.

Examples

Run this code

data(bifactor)
cor.plot(Thurstone,TRUE, main="9 cognitive variables from Thurstone")
simp <- sim.circ(24)
cor.plot(cor(simp),colors=TRUE,zlim=c(-1,1),main="24 variables in a circumplex")