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Re: st: pca and predict--confusion about what it does


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: pca and predict--confusion about what it does
Date   Sat, 20 Oct 2012 19:02:39 +0100

The first predicted variable is the first PC, and so on. It's
conventional that PCs are scaled to have mean 0.

The absence of a response or outcome variable is irrelevant here. If
you're confused a good reason is because Stata is stretching the
meaning of -predict- here to include these constructed variables. (A
bad reason is not reading the documentation...)

Nick

On Sat, Oct 20, 2012 at 6:32 PM, Israel Pearce <ra.frbsf@gmail.com> wrote:
> I am confused about the principal component scores one gets from pca
> postestimation. Let's say I wanted the first principal component
> scores from a set of explanatory variables. I could do a pca on my x
> variables getting eigen values and vectors then use predict pc1 (a
> newly created var), focusing only on the first eigenvector. However,
> if one does not have a y variable what is the score actually giving us
> for individual observations? What is it a predicted value for? Also,
> in theory I do not see why the sum of these observations must add to 0
> but they are. If someone understands this I would greatly appreciate
> an explanation. Thanks!
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