R help - Mar 2013 - lmer, p-values and all that

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.

Would someone be kind enough to point out my blunder?

Thank you,
Michael Grant


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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 at 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