model selection in lme4
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.
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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/