[R-sig-ME] inference for random effects

[Jeff Evans]

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