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: [EMAIL PROTECTED] > www.lukasindermaur.ch > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
