inference for random effects
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 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
_______________________________________________ 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