dr mike wrote:
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Wittner, Ben
Sent: 02 March 2005 11:33
To: [EMAIL PROTECTED]
Subject: [R] subset selection for logistic regression
R-packages leaps and subselect implement various methods of
selecting best or good subsets of predictor variables for
linear regression models, but they do not seem to be
applicable to logistic regression models.
Does anyone know of software for finding good subsets of
predictor variables for linear regression models?
Thanks.
-Ben
p.s., The leaps package references "Subset Selection in
Regression" by Alan Miller. On page 2 of the 2nd edition of
that text it states the following:
"All of the models which will be considered in this
monograph will be linear; that is they
will be linear in the regression coefficients.Though most
of the ideas and problems carry
over to the fitting of nonlinear models and generalized
linear models (particularly the fitting
of logistic relationships), the complexity is greatly increased."
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The LASSO method and the Least Angle Regression method are two such that
have both been implemented (efficiently IMHO - only one least squares for
all levels of shrinkage IIRC) in the lars package for R of Hastie and Efron.
There is a paper by Madigan and Ridgeway that discusses the use of the Least
Angle Regresson approach in the context of logistic regression - available
for download from Madigan's space at Ruttgers:
www.stat.rutgers.edu/~madigan/PAPERS/lars3.pdf
HTH
Mike
Yes things like lasso can help a lot.
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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