Our problem here described is to calculate AIC from a GLMM negbin. Our data compose by 2 Categorical variables (Yes/Not), 3 Numerical variables and our random factor, all without any NA. We want to create a GLMM model with negative binomial family and this model calculates AIC and BIC as NA, but calculate the rest of the model values. In R:
>model<-glmmPQL(X~Categorical1*Categorical2+Numerical1+Numerical2+Numerical3,random=~1|Random1,family=negative.binomial(theta = 1234, link = "log"),data=Data)
Linear mixed-effects model fit by maximum likelihood
AIC BIC logLik
NA NA NA
We tried using in R: AIC(model), AIC(logLik(model)), BIC(model), BIC(logLik(model)) & extractAIC(model) with NA as a result (in the case of extractAIC we obtain 9 NA). How can we calculate this AIC?