[R] Question about PCA with prcomp

[Mark Difford]

Hi James,

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.

BestR,
Mark.


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.
> 
> 
> james
> 
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> 

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