[R-sig-ME] inference for random effects
Jeff Evans
evansj18 at msu.edu
Thu Feb 5 19:48:08 CET 2009
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
More information about the R-sig-mixed-models
mailing list