[R] Question about PCA with prcomp

Ravi Varadhan rvaradhan at jhmi.edu
Mon Jul 2 22:29:13 CEST 2007

The PCs that are associated with the smaller eigenvalues. 


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



-----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
|> 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
|> 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
|> 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"?

   ___    Patrick Connolly   
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