sphpca {psy}R Documentation

Spherical Representation of a Correlation Matrix

Description

Graphical representation of a correlation matrix, similar to principal component analysis (PCA) but the mapping is on a sphere. The information is close to a 3d PCA, the picture is however easier to interpret since the variables are in fact on a 2d map.

Usage

sphpca(datafile, h=0, v=0, f=0, cx=0.75, nbsphere=2, back=FALSE)

Arguments

datafile name of datafile
h rotation of the sphere on a horizontal plane (in degres)
v rotation of the sphere on a vertical plane (in degres)
f rotation of the sphere on a frontal plane (in degres)
cx size of the lettering (0.75 by default, 1 for bigger letters, 0.5 for smaller)
nbsphere two by default: front and back
back "FALSE" by default: the back sphere is not seen through

Details

The sphere may be rotated to help in visualising most of variables on a same side (front for example). By default, the back of the sphere (right plot) is not seen showing through. Computations are based on a principal components approximation (see reference for details).

Value

A plot

Author(s)

Bruno Falissard

References

Falissard B, A spherical representation of a correlation matrix, Journal of Classification (1996), 13:2, 267-280.

Examples

data(sleep)
sphpca(sleep[,c(2:5,7:11)])
##spherical representation of ecological and constitutional correlates in mammals

[Package psy version 0.6 Index]