Hi All,
I'd like to build a package for the community that replicates the output
produced by SAS "proc varclus". According to the SAS documentation, the
first few steps are:
1. Find the first two principal components.
2. Perform an orthoblique rotation (quartimax rotation) on eigenvectors.
3. Assign each variable to the rotated component with which it has the
higher
squared correlation.
The cartoon example below attempt to do this, but I found my results differ
from SAS in "pc3" (i.e, the standardize component scores).
I'd appreciate your help in whether you see anything wrong in "pc2" or
"pc3"?
set.seed(1)
x1=rnorm(200); x2=0.9*x1; x3=0.7*x1; x4=x1*x1; x5=x1*x1*x1;
x6=rnorm(200); x7=0.9*x6; x8=0.7*x6; x9=x6*x6; x10=x6*x6*x6;
x <- cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10)
require(GPArotation)
pc1 <- princomp(x, cor = TRUE, scores = TRUE)
pc2 <- quartimax(pc1$loadings[,1:2],normalize=TRUE)$loadings
pc3 <- scale(x%*% pc2)
pc4 <- apply(x, 2, function(x) cor(x, pc3)^2)
Thanks in advance for any help!
Axel.
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