[R] mixed-effects models with (g)lmer in R and model selection
fanjianling at gmail.com
Sat Feb 20 00:30:28 CET 2016
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
On 19 February 2016 at 07:01, Wilbert Heeringa <wjheeringa at 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
> 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
> 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
> 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.
> 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.
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