model selection in lme4
Some caution on this advice: you seem to be quoting the general advice on AIC/BIC/AICc 1. The AIC/BIC distinction is between "best prediction" and "consistent estimation of true model" dimension, e.g. Yang, Yuhong. 2005. Can the strengths of AIC and BIC be shared? A conflict between model identification and regression estimation. Biometrika 92, no. 4 (December 1): 937-950. doi:10.1093/biomet/92.4.937. I favor AIC on these grounds, but you can decide for yourself. 2. For models with different random effects, AIC and BIC share a "degrees of freedom counting" problem with all model selection approaches -- there are two aspects here, (1) whether you are focused on individual-level prediction or population-level prediction (Vaida and Blanchard 2005, Spiegelhalter et al 2002) and (2) whether AIC/BIC share the boundary problems that also apply to likelihood ratio tests (Greven, Sonja. 2008. Non-Standard Problems in Inference for Additive and Linear Mixed Models. G?ttingen, Germany: Cuvillier Verlag. http://www.cuvillier.de/flycms/en/html/30/-UickI3zKPS,3cEY=/Buchdetails.html?SID=wVZnpL8f0fbc. ) 3. AIC and BIC are asymptotic tests (which can be especially problematic with random effects problems, when there are not large number of random blocks -- this makes likelihood ratio tests NOT OK for fixed-effect comparisons with small numbers of blocks (Pinheiro and Bates 2000)). If you want to use AICc then you are back to counting residual degrees of freedom ... as far as I know there isn't much guidance available on this issue. My bottom line: I would go ahead and use (Q)AIC with caution for data sets with large (?) numbers of blocks. With smaller numbers of blocks I would probably try to find some kind of randomization/permutation approach to get a sense of the relevant size of delta-AIC values ... ... or damn the torpedoes and see if you can get away with straight AIC. Ben Bolker
Christopher David Desjardins wrote:
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 https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
----------------- Christopher David Desjardins Ph.D. Student Quantitative Methods in Education Department of Educational Psychology University of Minnesota http://blog.lib.umn.edu/desja004/educationalpsychology/
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
Ben Bolker Associate professor, Biology Dep't, Univ. of Florida bolker at ufl.edu / www.zoology.ufl.edu/bolker GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc