[R] prcomp(X,center=F) ??

I do not understand, from a PCA point of view, the option center=F of prcomp() According to the help page, the calculation in prcomp() "is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix" (as it's done by p

[R] prcomp vs. princomp vs fast.prcomp

Hi R People: When performing PCA, should I use prcomp, princomp or fast.prcomp, please? thanks. Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: [EMAIL PROTECTED] __ R

Re: [R] prcomp(X,center=F) ??

Hi Agus, >> But the rotation made with the eigenvectors of prcomp(X,center=F) yields >> axes that are correlated. Therefore, prcomp(X,center=F) is not really a >> PCA. cor() is not an appropriate test of whether two vectors are orthogonal. The definition that two vectors (in an inner product sp

Re: [R] prcomp(X,center=F) ??

Dear Agustin & the Listers, Noncentred PCA is an old and establishes method. It is rarely used, but still (methinks) it is used more often than it should be used. There is nothing wrong in having noncentred PCA in R, and it is a real PCA. Details will follow. On 08/03/2009, at 11:07 AM, A

Re: [R] prcomp vs. princomp vs fast.prcomp

On 2/10/08, Erin Hodgess <[EMAIL PROTECTED]> wrote: > When performing PCA, should I use prcomp, princomp or fast.prcomp, please? You can take a look here [1] and here [2] for some short references. >From the first page: "Principal Components Analysis (PCA) is available in prcomp() (preferred) and