Hi mixed modelers,
I'm using glmm.admb and glmer functions to fit mixed models on the same data
(downloadable here
) and trying
to assess which model provides the better fit.
m1<-glmm.admb(Counts~T*Year+B*Year+P*Year, random=Site, group="Year",
data=ex1o, family="nbinom", zeroInflation=TRUE)
m2<-glmer(Counts~T*Year+B*Year+P*Year*(1|Site), data=ex1o,
family=quasipoisson)
My questions are:
1) How can I extract the AIC values from m1?
2) Are the AIC values comparable between the two models (i.e. can I compare
them for model selection)?
3) For m2, the true estimates for the fixed effects can be calculated by
exp(returned estimate). Is this true for m1 too, or does the negative
binomial distribution require a different conversion?
4) What is the difference between the 'random' and the 'group' argument in
the glmm.admb function (I've read the documentation but it's still unclear)?
Much appreciated,
Raldo
MSc student
University of Cape Town
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