[R] fitting of all possible models

[Bert Gunter]

... Below

-- Bert 

Bert Gunter
Genentech Nonclinical Statistics
South San Francisco, CA 94404
650-467-7374


-----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: Tuesday, February 27, 2007 5:14 AM
To: Indermaur Lukas
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] fitting of all possible models

Indermaur Lukas wrote:
> Hi,
> Fitting all possible models (GLM) with 10 predictors will result in loads
of (2^10 - 1) models. I want to do that in order to get the importance of
variables (having an unbalanced variable design) by summing the up the
AIC-weights of models including the same variable, for every variable
separately. It's time consuming and annoying to define all possible models
by hand. 
>  
> Is there a command, or easy solution to let R define the set of all
possible models itself? I defined models in the following way to process
them with a batch job:
>  
> # e.g. model 1
> preference<- formula(Y~Lwd + N + Sex + YY)

> # e.g. model 2
> preference_heterogeneity<- formula(Y~Ri + Lwd + N + Sex + YY)  
> etc.
> etc.
>  
>  
> I appreciate any hint
> Cheers
> Lukas

If you choose the model from amount 2^10 -1 having best AIC, that model 
will be badly biased.  Why look at so many?  Pre-specification of 
models, or fitting full models with penalization, 

--- ...the rub being how much to penalize. My impression from what I've read
is, for prediction, close to "the more, the better is the predictor..." .
Nature rewards parsimony.

Cheers,
Bert


Frank

>  
>  
>  
>  
>  
> °°° 
> Lukas Indermaur, PhD student 
> eawag / Swiss Federal Institute of Aquatic Science and Technology 
> ECO - Department of Aquatic Ecology
> Überlandstrasse 133
> CH-8600 Dübendorf
> Switzerland
>  
> Phone: +41 (0) 71 220 38 25
> Fax    : +41 (0) 44 823 53 15 
> Email: lukas.indermaur at eawag.ch
> www.lukasindermaur.ch
> 
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-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

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