I think one of the issues, discussed elsewhere on this list if I  
recall, is that the status of the random effects estimates is unclear  
– they are not parameters of the model in the same sense as the fixed  
effects are. Because AIC depends on counting parameters, comparing AIC  
values with different random-effects structures is suspect.

- Pete

On Jul 29, 2009, at 10:11 AM, cacabelos@uvigo.es wrote:

> Hi R-users,
>
> I was unfortunately not able to find a solution to my problem about  
> model selection. I have fixed (e.g. Height) and random (e.g.Time)  
> factors, and I created these kind of model structures:
>
> Model1<-lmer(H~Height+(1|Time)+Biomass)+(Biomass| 
> Time),data=dat,family=gaussian)
> Model2<-glm(H~Height+Biomass,data=dat,family=gaussian)
>
> I am using the criterion ?the best model is the one that has the  
> lowest AIC?, but are these AIC from different procedures (i.e. glm  
> and lmer) comparable?
>
> I wonder if anyone can help me... Thank you!
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>



---
Peter Dixon
peter.dixon@ualberta.ca




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