subset selection for logistic regression

To clarify Frank's remark ...

A prominent theme in statistical research over at least the last 25 years
(with roots that go back 50 or more, probably) has been the superiority of
"shrinkage" methods over variable selection. I also find it distressing that
these ideas have apparently not penetrated much (at all?) into the wider
scientific community (but I suppose I shouldn't be surprised -- most
scientists still do one factor at a time experiments 80 years after Fisher).
Specific incarnations can be found in anything Bayesian, mixed effects
models for repeated measures, ridge regression, and the R packages lars and
lasso, among others.

I would speculate that aside from the usual statistics/science cultural
issues, part of the reason for this is that the estimators don't generally
come with neat, classical inference procedures: like it or not, many
scientists have been conditioned by their Stat 101 courses to expect P
values, so in some sense, we are hoisted by our own petard.

Just my $.02 -- contrary(and more knowledgeable) opinions welcome.

-- Bert Gunter
 

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch 
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Frank 
E Harrell Jr
Sent: Wednesday, March 02, 2005 5:13 AM
To: Wittner, Ben
Cc: r-help at lists.R-project.org
Subject: Re: [R] subset selection for logistic regression

Wittner, Ben wrote:

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

Why are these procedures still being used?  The performance 
is known to 
be bad in almost every sense (see r-help archives).

Frank Harrell

 
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."

-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   
Vanderbilt University

From 1 April 2005: Department of Statistical Science, UCL, London