[R] fitting of all possible models

[Frank E Harrell Jr]

Indermaur Lukas wrote:
> 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

I have seen the literature on Bayesian model averaging which uses 
weights from Bayes factors, related to BIC, but not the approach you are 
using.

Frank

> 
>  
> 
> 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
> 
>>
>>
>>
>>
>> °°°
>> 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