lmer, p-values and all that

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 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

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