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Model selection GLM vs. GLMMs
2 messages · Nelida Villasenor, Ben Bolker
1 day later
Nelida Villasenor <nelida.villasenor at ...> writes:
Dear all,
I'm performing model selection based on AICc on a set of GLMMs that only vary in their fixed effects. The data comes from 12 transects (5 measures along each transect), then each transect is modelled as a random effect "+(1|transect)". As the response variable was proportions (presences/n), I fitted the models using binomial family and the total number of points (n) as weights.
Given that some models had boundary problems
meaning singular fits (estimated zero variances and/or +/- 1 correlations and/or values of estimated theta=0) ?
I ran the model selection on a set of GLMs instead of GLMMs. The results were almost identical in terms of the list of models with the highest support (for 7 response variables where model selection was performed independently).
I'm wondering which approach is correct? Or, as my results show, it does not really matter, because the random effect does not change in my GLMMs?
I think you would find a bit of disagreement among experts about the best procedure -- whether it would be to drop random effects until you got a sensible non-singular fit, or to keep them in even though they're singular. Keep in mind that you should get the same estimates with a GLM or a GLMM if the variance estimates are all zero ... Since it doesn't sound as though it affects your einference/model selection on the fixed effects, I would say you could choose either approach (and explain clearly what you did). Ben Bolker