[R] Question about PCA with prcomp
James R. Graham
jamesrgraham at mac.com
Mon Jul 2 17:24:00 CEST 2007
The basic premise of what I want to do is the following:
I have 20 "entities" for which I have ~500 measurements each. So, I
have a matrix of 20 rows by ~500 columns.
The 20 entities fall into two classes: "good" and "bad."
I eventually would like to derive a model that would then be able to
classify new entities as being in "good territory" or "bad territory"
based upon my existing data set.
I know that not all ~500 measurements are meaningful, so I thought
the best place to begin would be to do a PCA in order to reduce the
amount of data with which I have to work.
I did this using the prcomp function and found that nearly 90% of the
variance in the data is explained by PC1 and 2.
So far, so good.
I would now like to find out which of the original ~500 measurements
contribute to PC1 and 2 and by how much.
Any tips would be greatly appreciated! And apologies in advance if
this turns out to be an idiotic question.
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