Re: [R] Princomp$Scores

From: miguel manese <>
Date: Mon 11 Apr 2005 - 20:13:28 EST

The scores can be "manually" computed as

    center(your.matrix,scale=F) %*% eigen(cov(your.matrix))$vector # or cor()

i.e. a linear combination of your mean-corrected data. negative coefficients does not imply anything, because computation of the signs of eigenvectors is arbitrary. Absolute value of princomp(your.matrix)$load indicates which variable influences a principal component most (this is closer to the "correlate" thing you are thinking of, I guess).

On Apr 9, 2005 1:09 AM, Ken Termiso <> wrote:
> Hi all,
> I was hoping that someone could verify this for me-
> when I run princomp() on a matrix, it is my understanding that the scores
> slot of the output is a measure of how well each row correlates (for lack of
> a better word) with each principal component.
> i.e. say I have a 300x6 log2 scaled matrix, and I run princomp(). I would
> get back a $scores slot that is also 300x6, where each value can be negative
> or positive. I'd assume that the negative values correspond to rows that are
> negatively correlated with that particular PC, and vice-versa for positives.
> Thanks in advance for the help,
> Ken
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> mailing list PLEASE do read the posting guide! Received on Mon Apr 11 20:29:28 2005

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