[R-sig-ME] model selection in lme4

[Christopher David Desjardins]

You could use either the BIC or the AIC. My understanding is that the  
AIC tends to favor overly complex models whereas the BIC tends to  
favor parsimonious models. I am generally inclined to always use the  
BIC. If you have a small sample size you might also consider using the  
AICC which is a correction of the AIC for small sample sizes. That  
said, in my experience the AICC still selects more complex models than  
the BIC. Also if you have nested models you could use the chi-square  
tests.
Cheers,
Chris

On Feb 15, 2009, at 4:44 PM, Tahira Jamil wrote:

>
> Hi
> I have run  GLMM models in lme4 with different fixed effects and  
> random effects . But now the problem is model selction Is AIC or BIC  
> results are definitive specially for Gernalized linear mixed models  
> or what critera should I use for model selction. So I can decide  
> which explantory variable should be in the model because I have more  
> than 10 explantory variables and some are entering in the model as  
> random effect. In some cases If AIC has lower value but BIC is  
> comparatively high.
>    some suggestion for model selection would be highly appricated.
>
>    WIth best wishes
>    T Jamil
>    Ph.D student
>    Biometris
>    Wageningen University and Research centre Netherlands.
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
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-----------------
Christopher David Desjardins
Ph.D. Student
Quantitative Methods in Education
Department of Educational Psychology
University of  Minnesota
http://blog.lib.umn.edu/desja004/educationalpsychology/