sphpca {psy} | R Documentation |
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.
sphpca(datafile, h=0, v=0, f=0, cx=0.75, nbsphere=2, back=FALSE)
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 |
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).
A plot
Bruno Falissard
Falissard B, A spherical representation of a correlation matrix, Journal of Classification (1996), 13:2, 267-280.
data(sleep) sphpca(sleep[,c(2:5,7:11)]) ##spherical representation of ecological and constitutional correlates in mammals