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
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 space)
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,
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