On 13-03-27 10:10 PM, David Winsemius wrote: > > On Mar 27, 2013, at 7:00 PM, Ben Bolker wrote: > >> Michael Grant <michael.grant <at> colorado.edu> writes: >>> Dear Help: >> >>> I am trying to follow Professor Bates' recommendation, quoted by >>> Professor Crawley in The R Book, p629, to determine whether I should >>> model data using the 'plain old' lm function or the mixed model >>> function lmer by using the syntax anova(lmModel,lmerModel). >>> Apparently I've not understood the recommendation or the proper >>> likelihood ratio test in question (or both) for I get this error >>> message: Error: $ operator not defined for this S4 class. >> >> I don't have the R Book handy (some more context would be extremely >> useful! I would think it would count as "fair use" to quote the >> passage you're referring to ...) > > This is the quoted Rhelp entry: > > http://tolstoy.newcastle.edu.au/R/help/05/01/10006.html > > (I'm unable to determine whether it applies to the question at hand.)
OK, I misspoke -- sorry. I think the lmer()/lme() likelihoods are actually comparable; it's GLMMs (glmer(), with no analogue in lme()-land except for MASS::glmmPQL, which doesn't give you log-likelihoods at all) where the problem arises. You can (1) use lme(), (2) look at http://glmm.wikidot.com/faq for suggestions about testing random-effects terms (including "perhaps don't do it at all"), or (3) construct the likelihood ratio test yourself as follows: library("nlme") data(Orthodont) fm1 <- lme(distance~age,random=~1|Subject,data=Orthodont) fm0 <- lm(distance~age,data=Orthodont) anova(fm1,fm0)[["p-value"]] detach("package:nlme",unload=TRUE) library("lme4.0") ## stable version of lme4 fm2 <- lmer(distance~age+(1|Subject),data=Orthodont,REML=FALSE) anova(fm2,fm0) ## fails ddiff <- -2*c(logLik(fm0)-logLik(fm2)) pchisq(ddiff,1,lower.tail=FALSE) ## not identical to above but close enough > >> >>> Would someone be kind enough to point out my blunder? >> >> You should probably repost this to the r-sig-mixed-mod...@r-project.org >> mailing list. >> >> My short answer would be: (1) I don't think you can actually >> use anova() to compare likelihoods between lm() and lme()/lmer() >> fits in the way that you want: *maybe* for lme() [don't recall], >> but almost certainly not for lmer(). See http://glmm.wikidot.com/faq >> for methods for testing significance/inclusion of random factors >> (short answer: you should *generally* try to make the decision >> whether to include random factors or not on _a priori_ grounds, >> not on the basis of statistical tests ...) >> >> Ben Bolker >> > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
