[R] Question about PCA with prcomp
mark_difford at yahoo.co.uk
Mon Jul 2 19:55:15 CEST 2007
Have a look at Cadima et al.'s subselect package [Cadima worked with/was a
student of Prof Jolliffe, one of _the_ experts on PCA; Jolliffe devotes part
of a Chapter to this question in his text (Principal Component Analysis,
pub. Springer)]. Then you should look at psychometric stuff: a good place
to start would be Professor Revelle's psych package.
James R. Graham wrote:
> 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.
> R-help at stat.math.ethz.ch mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
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