On Mon, 26 Aug 2013, Luca Corlatti wrote:

>...

>For the sake of curiosity, however, I tried to re-run the model selection using the function glmer, with family=poisson, adding the >observation-level as a random factor (1|obs) to account for overdispersion, as recently suggested. In this case, however, visual inspection of >residuals for the global model were not very satisfactory. After running the model selection, the results were quite different from those >obtained with glmmadmb (not dramatically different, but still...). Given I have no deep knowledge into the !

>philosophy behind the use of glmer with (1|obs), I was wondering:


A qualifier to my earlier comments is that most PQL implementations for GLMMs I have seen do not allow the user to choose which random effects do or do not enter the iterative weights via the linear predictor in addition to the variance function, so the estimated (1|obs)  REs have to remain in the linear predictor. This is not exactly what I was describing earlier in the general setting which requires that only these REs are dropped from the linear predictor and thus working response variable. This is intermediate between full PQL and full MQL (Breslow and Clayton, 1993 JASA).

Dr Steven G. Candy
Senior Applied Statistician
Wildlife Conservation and Fisheries
Australian Antarctic Division
203 Channel Hwy, Kingston, Tasmania, 7050, Australia
ph: (+61) 3 62 323135


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