Dear All,
I have come upon an R-mode PCA protocol that uses the following arguments,
where "mydata.txt" is an nxm matrix of n objects and m variables:
> a <- read.table("mydata.txt")
> b <- t(a)
> c <- prcomp(b)
> c$rotation
The user then plots the coordinates given by c$rotation (PC1 and PC2) as the
"scores" of their PCA plot.
This doesn't make sense to me as the user transposed the matrix prior to
rotating the data, so they have solved for the eigenvectors of the objects
and by plotting the values of c$rotation the user is in effect plotting the
loading matrix and not the scores. If anything, this looks like a Q-mode PCA
where the rotation matrix should be multiplied by the original data matrix
to give scores for the variables.
Am I missing something or does this procedure look incorrect?
Thank you for your time,
Chris
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