On Mon, 3 Nov 2008, EVANS David-William wrote:
Hello fellow Rers,
I have a no-doubt simple question which is turning into a headache so
would be grateful for any help.
I want to do a principal components analysis directly on a correlation
matrix object rather than inputting the raw data (and specifying cor =
TRUE or the like). The reason behind this is I need to use polychoric
correlation coefficients calculated with John Fox's hetcor function. Is
there a way to do this with princomp or prcomp, or any other principal
components function in other R packages?
See ?princomp, and
covmat: a covariance matrix, or a covariance list as returned by
'cov.wt' (and 'cov.mve' or 'cov.mcd' from package 'MASS'). If
supplied, this is used rather than the covariance matrix of
'x'.
A correlation matrix *is* a covariance matrix (of scaled data, and scaling
does matter for PCA, of course).
That this is possible is the main advantage of princomp over prcomp.
Compare
princomp(covmat=cor(USArrests))
Call:
princomp(covmat = cor(USArrests))
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.5748783 0.9948694 0.5971291 0.4164494
4 variables and NA observations.
with the help-page examples
Again, very grateful for any help.
David Evans.
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