[R] Question about PCA with prcomp

James R. Graham jamesrgraham at mac.com
Mon Jul 2 17:24:00 CEST 2007

Hello All,

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.


More information about the R-help mailing list