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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