A brute force solution: go to return values (type -ereturn list-),
identify what the vector of eigenvalues is (it's -e(Ev)- in Stata 10),
and then manually scale the scores:
pca <your variables here>
local k = <number of scores you want to use>
predict score1 - score`k'
mat lambda = e(Ev)
forvalues i=1/`k' {
replace score`i' = score`i'*sqrt(lambda[1,`i'])
}
I've no idea how that will go with rotation. I personally never had
much trust in rotations, anyway...
On Thu, Aug 21, 2008 at 3:32 PM, Thomas Lux <[email protected]> wrote:
> Stata 9 displays principal components in unit normalisation, but one can diplay the principal components in eigenvalue normalisation by typing: estat loadings, cnorm(eigen)
> But how can I get a rotated version of these principal components in eigenvalue normalisation?
>
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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