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. james ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.