Hello, Wilbert, You did give a good procedure for lme model selection! thanks! I learn some. I am also working on similar problem recently, maybe you can take a look at "glmmLasso" package, which allows model selection in generalized linear mixed effects models using the LASSO shrinkage method.
Regards, Jianling On 19 February 2016 at 07:01, Wilbert Heeringa <wjheeri...@gmail.com> wrote: > Dear all, > > Mixed-effects models are wonderful for analyzing data, but it is always a > hassle to find the best model, i.e. the model with the lowest AIC, > especially when the number of predictor variables is large. > > Presently when trying to find the right model, I perform the following > steps: > > 1. > > Start with a model containing all predictors. Assuming dependent > variable X and predictors A, B, C, D, E, I start with: X~A+B+C+D+E > 2. > > Lmer warns that is has dropped columns/coefficients. These are variables > which have a *perfect* correlation with any of the other variables or > with a combination of variables. With summary() it can be found which > columns have been dropped. Assume predictor D has been dropped, I continue > with this model: X~A+B+C+E > 3. > > Subsequently I need to check whether there are variables (or groups of > variables) which *strongly* corrrelate to each other. I included the > function vif.mer (developed by Austin F. Frank and available at: > https://raw.github.com/aufrank/R-hacks/master/mer-utils.R) in my script, > and when applying this function to my reduced model, I got vif values for > each of the variables. When vif>5 for a predictor, it probably should be > removed. In case multiple variables have a vif>5, I first remove the > predictor with the highest vif, then re-run lmer en vif.mer. I remove again > the predictor with highest vif (if one or more predictors have still a > vif>5), and I repeat this until none of the remaining predictors has a > vif>5. In case I got a warning "Model failed to converge" in the larger > model(s), this warning does not appear any longer in the 'cleaned' model. > 4. > > Assume the following predictors have survived: A, B en E. Now I want to > find the combination of predictors that gives the smallest AIC. For three > predictors it is easy to try all combinations, but if it would have been 10 > predictors, manually trying all combinations would be time-consuming. So I > used the function fitLMER.fnc from the LMERConvenienceFunctions package. > This function back fit fixed effects, forward fit random effects, and > re-back fit fixed effects. I consider the model given by fitLMER.fnc as the > right one. > > I am not an expert in mixed-effects models and have struggled with model > selection. I found the procedure which I decribed working, but I would > really be appreciate to hear whether the procedure is sound, or whether > there are better alternatives. > > Best, > > Wilbert > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.