[R] Question about PCA with prcomp

[Ravi Varadhan]

The PCs that are associated with the smaller eigenvalues. 

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Ravi Varadhan, Ph.D.

Assistant Professor, The Center on Aging and Health

Division of Geriatric Medicine and Gerontology 

Johns Hopkins University

Ph: (410) 502-2619

Fax: (410) 614-9625

Email: rvaradhan at jhmi.edu

Webpage:  http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html

 

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-----Original Message-----
From: Patrick Connolly [mailto:p_connolly at ihug.co.nz] 
Sent: Monday, July 02, 2007 4:23 PM
To: Ravi Varadhan
Cc: 'Mark Difford'; r-help at stat.math.ethz.ch
Subject: Re: [R] Question about PCA with prcomp

On Mon, 02-Jul-2007 at 03:16PM -0400, Ravi Varadhan wrote:

|> Mark,
|> 
|> What you are referring to deals with the selection of covariates, since
PC
|> doesn't do dimensionality reduction in the sense of covariate selection.
|> But what Mark is asking for is to identify how much each data point
|> contributes to individual PCs.  I don't think that Mark's query makes
much
|> sense, unless he meant to ask: which individuals have high/low scores on
|> PC1/PC2.  Here are some comments that may be tangentially related to
Mark's
|> question:
|> 
|> 1.  If one is worried about a few data points contributing heavily to the
|> estimation of PCs, then one can use robust PCA, for example, using robust
|> covariance matrices.  MASS has some tools for this.
|> 2.  The "biplot" for the first 2 PCs can give some insights
|> 3. PCs, especially, the last few PCs, can be used to identify "outliers".

What is meant by "last few PCs"?

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