[R-sig-ME] inference for random effects

[Christina Bogner]

> Message: 5
> Date: Thu, 5 Feb 2009 13:48:08 -0500
> From: "Jeff Evans" <evansj18 at msu.edu>
> Subject: [R-sig-ME] inference for random effects
> To: <r-sig-mixed-models at r-project.org>
> Message-ID: <392F001D52E34A10868571F30AB625DD at myelin>
> Content-Type: text/plain;	charset="us-ascii"
> 
> 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

Hello Jeff,

for lme and lmer models there is a simulation package RLRsim by Fabian 
Scheipl "for testing the presence of variance components" that might be 
helpful.

Christina