Pre-model Variable Reduction

Hi All,

I beg to differ with Ravi Varadhan's perspective. While it is true that
principal component analysis does not itself do variable selection, it is an
important method for pointing the way to what to select. This is what the
methods in the subselect package rely on. (One of its authors was I believe
a student of Jolliffe's). For a modern perspective on this, see the
following paper:

Debashis Paul, Eric Bair, Trevor Hastie and Robert Tibshirani:
"Preconditioning" for feature selection and regression in high-dimensional
problems We show that supervised principal components followed by a variable
selection procedure is an effective approach for variable selection in very
high dimension. Annals of Statistics 36(4), 2008, 1595-1618.

http://www-stat.stanford.edu/~hastie/Papers/Preconditioning_Annals.pdf

Regards, Mark.

Ravi Varadhan wrote:

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:

Hello All,
I am trying to carry out variable reduction. I do not have information 
about the dependent variable, and have only the X variables as it 
were.
In selecting variables I wish to keep, I have considered the following

criteria.

1) Percentage of missing value in each column/variable
2) Variance of each variable, with a cut-off value.

I recently came across Weka and found that there is an RWeka package 
which would allow me to make use of Weka through R.
Weka provides a "Genetic search" variable reduction method, but I 
could not find its R code implementation in the RWeka Pdf file on 
CRAN.

I looked for other R packages that allow me to do variable reduction 
without considering a dependent variable. I came across 'dprep'
package but it does not have a Windows implementation.

Moreover, I have a dataset that contains continuous and categorical 
variables, some categorical variables having 3 levels, 10 levels and 
so on, till a max 50 levels (E.g. States in the USA).

Any suggestions in this regard will be much appreciated.

Thank you

Harsh Singhal
Decision Systems,
Mu Sigma, Inc.