[R] mixed-effects models with (g)lmer in R and model selection

Jianling Fan fanjianling at gmail.com
Sat Feb 20 00:30:28 CET 2016

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



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
> 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]]
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