[R] fitting of all possible models

[Indermaur Lukas]

Hi Frank
I fitted a set of 12 candidate models and evaluated the importance of variables based on model averaged coefficients and SE (model weights >=0.9). Variables in my models were not distributed in equal numbers across all models thus I was not able to evaluate the importance of variables just by summing up the AIC-weights of models including a specific variable. Now, why so many models to fit: I was curious, if the ranking in the importance of variables is similar, when just summing up the AIC-weights over an all-possible-models set and looking at the ordered model averaged coefficients (order of CV=SE/coefficient).
 
Any hint for me?
Cheers
Lukas

 

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