[R] Generating all possible models from full model

Frank E Harrell Jr f.harrell at Vanderbilt.Edu
Wed May 19 21:51:53 CEST 2010

On 05/19/2010 01:39 PM, Ben Bolker wrote:
> Frank E Harrell Jr<f.harrell<at>  Vanderbilt.Edu>  writes:
>> Please read the large number of notes in the e-mail archive about the
>> invalidity of such modeling procedures.
>> Frank
>    I'm curious: do you have an objection to multi-model averaging
> a la Burnham, Anderson, and White (as implemented in the MuMIn
> package)?  i.e., *not* just picking the
> best model, and *not* trying to interpret statistical significance
> of particular coefficients, but trying to maximize predictive
> capability by computing the AIC values of many candidate models
> and weighting predictions accordingly (and incorporating among-model variation
> when computing prediction uncertainty)?  (I would look for the
> answer in your book, but I have lost my copy by loaning it out
> &  haven't got a new one yet ...)

Hi Ben,

I think that model averaging (e.g., Bayesian model averaging) works 
extremely well.  But if you are staying within one model family, it is a 
lot more work than the equally excellent penalized maximum likelihood 
estimation of a single (big) model.  The latter uses more standard tools 
and can isolate the effect of one variable and result in ordinary model 

I haven't seen a variable selection method that works well without 
penalization (shrinkage).


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

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