i did find this for you, down towards the end, they discuss the anova method.
i am on my way to a bayesian analysis/lmer is a step towards that- so i won't be doing anova. i can't be of much specific help with that question, but here you go. https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/015591.html On Mar 27, 2013, at 10:13 PM, Nicole Ford wrote: > i literally just ran one. > > when i ran one of mine and the did summary(mod) i get the following: > >> mod <- lmer(dem ~ xbar + cpi + (1 | country), data=wvsAB) >> summary(mod) > Linear mixed model fit by REML > Formula: dem ~ xbar + cpi + (1 | country) > Data: wvsAB > AIC BIC logLik deviance REMLdev > 34383 34418 -17187 34355 34373 > Random effects: > Groups Name Variance Std.Dev. > > with a bunch more stuff below. > > > On Mar 27, 2013, at 10:00 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 ...) >> >>> 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. > > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. [[alternative HTML version deleted]] ______________________________________________ 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.
