[R-sig-ME] inference for random effects

[Juan Pedro Steibel]

Jeff,
Why not use the model without the random effect as the null model?
JP

Jeff Evans wrote:
> I'm sure this must have been discussed before, but in searching the archives
> 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 the
> 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) , data=dat,
> family="binomial")
>
> anova(M1,M2)
>
> Where A, B, and C are factors, and Cdummy is a column with the word "dummy"
> in every row.
>
> Then compare the AIC, subtracting 2 from the M2 AIC score since it "falsely"
> estimated a parameter for the random effect. When I do this, I get delta AIC
> of about 600 favoring the more informative M1. Is this approach
> fundamentally wrong? 
>
>
> Thanks,
>
> Jeff Evans
> Michigan State University
>
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>
>   


-- 
=============================
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