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-models@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
> 
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.


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