[R] fitting of all possible models

[Frank E Harrell Jr]

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, or using data 
reduction (masked to Y) may work better.

Frank

>  
>  
>  
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> °°° 
> 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