[R-sig-ME] inference for random effects

Jeff Evans evansj18 at msu.edu
Thu Feb 5 20:44:23 CET 2009

Thanks Juan,

I would have done this, but lmer and glmer won't run without a random
effects term. So I thought that maybe I could trick it.

-----Original Message-----
From: Juan Pedro Steibel [mailto:steibelj at msu.edu] 
Sent: Thursday, February 05, 2009 2:38 PM
To: Jeff Evans
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] inference for random effects

Why not use the model without the random effect as the null model?

Jeff Evans wrote:
> I'm sure this must have been discussed before, but in searching the
> I haven't found an answer yet. 
> Simple question:
> In lme4 can I evaluate the significance of a random effect in a model by
> substituting an uninformative dummy variable for it and comparing it to
> model with the "real" random effect using anova? 
> M1 = lmer(cbind(successes, total-successes) ~ A * B + (1|C), data=dat,
> family="binomial")
> M2 = lmer(cbind(successes, total-successes) ~ A * B + (1|Cdummy) ,
> family="binomial")
> anova(M1,M2)
> Where A, B, and C are factors, and Cdummy is a column with the word
> in every row.
> Then compare the AIC, subtracting 2 from the M2 AIC score since it
> estimated a parameter for the random effect. When I do this, I get delta
> of about 600 favoring the more informative M1. Is this approach
> fundamentally wrong? 
> Thanks,
> Jeff Evans
> Michigan State University
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Juan Pedro Steibel

Assistant Professor
Statistical Genetics and Genomics

Department of Animal Science & 
Department of Fisheries and Wildlife

Michigan State University
1205-I Anthony Hall
East Lansing, MI
48824 USA 

Phone: 1-517-353-5102
E-mail: steibelj at msu.edu

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