generalized linear mixed models - how to compare?
On Sunday 17 April 2005 08:39, Nestor Fernandez wrote:
Dear all, I want to evaluate several generalized linear mixed models, including the null model, and select the best approximating one. I have tried glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result in similar parameter estimates but fairly different likelihood estimates. My questions: 1- Is it correct to calculate AIC for comparing my models, given that they use quasi-likelihood estimates? If not, how can I compare them? 2- Why the large differences in likelihood estimates between the two procedures?
The likelihood reported by glmmPQL is wrong, as it's the likelihood of an incorrect model (namely, an lme model that approximates the correct glmm model). GLMM uses (mostly) the same procedure to get parameter estimates, but as a final step calculates the likelihood for the correct model for those estimates (so the likelihood reported by it should be fairly reliable). Deepayan