model selection in lme4
Vaida and Blanchard Biometrika [(2005), 92, 2, pp. 351?370 Conditional Akaike information for mixed-effects models] discuss using AIC for model selection in mixed-effects models, and make recommendations. There is also a follow-up not by Liang, Wu and Zhou. Biometrika (2008), 95, 3, pp. 773?778 A note on conditional AIC for linear mixed-effects models. The general message is that the "type" of AIC statistic will depend on your motivation for model selection. Is it the fixed effects part of the model that is of most interest? Or are the random effects of specific interest too? This "focus" will determine the number of "effective parameters" in the penalty term (using results from Hodges, J.S. and Sargent, D. J. (2001). Counting degrees of freedom in hierarchical and other richly parameterized models. Biometrika 88, 367?79). There is also the issue of REML v ML estimation... Cheers, Simon.
On Sun, 2009-02-15 at 20:23 -0600, Christopher David Desjardins wrote:
For a discussion of BIC, please see Raftery (1995) in Sociological Methodology. Before you commit yourself on the AIC, I do encourage you to look at your BIC. In the models I've run when there is disagreement between the BIC and the AIC, it's usually that the AIC selects the overly complex model and includes unnecessary parameters. Cheers, Chris On Sunday 15 February 2009 19:50:30 Ben Bolker wrote:
It would be better to use AICc, but I'm not sure what I would use for "number of parameters" for a random effect with n levels: any number between 0.5 and n seems plausible! Someone should send Shane Richards (who has done some very nice work testing (Q)AIC(c) in ecological settings) and see if he's willing to tackle this one, although I can imagine he's getting sick of this kind of exercise ... Ben Bolker Renwick, A. R. wrote:
Just a quickie Ben, Are you saying that you would use AIC rather than AICc even with small sample size - due to difficulty in counting residual degrees of
freedom?
Thanks Anna p.s. this forum really is fantastic
________________________________________ From: r-sig-mixed-models-bounces at r-project.org [r-sig-mixed-models-bounces at r-project.org] On Behalf Of Ben Bolker [bolker at ufl.edu] Sent: 15 February 2009 23:07 To: Christopher David Desjardins Cc: r-sig-mixed-models at r-project.org; tahirajamil at yahoo.com Subject: Re: [R-sig-ME] 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.h tml?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 _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models The University of Aberdeen is a charity registered in Scotland, No SC013683.
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Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician School of Biological Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320 Goddard Building (8) T: +61 7 3365 2506 http://www.uq.edu.au/~uqsblomb email: S.Blomberg1_at_uq.edu.au Policies: 1. I will NOT analyse your data for you. 2. Your deadline is your problem. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey.