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Pre-model Variable Reduction

Principal components analysis does "dimensionality reduction" but NOT
"variable reduction".  However, Jolliffe's 2004 book on PCA does discuss the
problem of selecting a subset of variables, with the goal of representing
the internal variation of original multivariate vector as well as possible
(see Section 6.3 of that book).  I do not think that these methods can
handle missing data.  The most important issue is to think about the goal of
variable reduction and then choose an appropriate optimality criterion for
achieving that goal.  In most instances of variable selection, the criterion
that is optimized is never explicitly considered.

Ravi.

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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: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Gabor Grothendieck
Sent: Tuesday, December 09, 2008 8:00 AM
To: Harsh
Cc: r-help at r-project.org
Subject: Re: [R] Pre-model Variable Reduction

See:

?prcomp
?princomp
On Tue, Dec 9, 2008 at 5:34 AM, Harsh <singhalblr at gmail.com> wrote:
criteria.
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