Yes. I know for certain that mydata.txt is ordered as an nxm matrix with n
objects and m variables.
Being that the data is transposed prior to performing the PCA, the rotation
matrix ends up being given in terms of object loadings on the principal
components, rather than variables as it's normally done.
I tried a sample data set and if you plot the values of b$rotation from the
transposed data, you end up with a similar (but, not equivalent) plot as if
you processed the data without transposing it first, i.e.,:
a - read.table(mydata.txt)
b - prcomp(a, retx = TRUE)
b$x
Even though the results are similar, conceptually I am not sure why it's
okay to transpose the data, perform PCA, and then plot the rotation matrix
as though they were scores.
Thanks again,
Chris
On Sat, Aug 20, 2011 at 12:45 PM, Carl Witthoft c...@witthoft.com wrote:
Well, did you try it with a simple test case to see what comes out?
Do you have a sample mydata.txt file so you know how it's ordered in the
first place?
Carl
quote
From: christopher stratton cfstratton_at_gmail.com
Date: Fri, 19 Aug 2011 23:10:07 -0400
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
/quote
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Tan Laboratory
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