Likelihood ratios

Hi folks,

I have 2 lmer fits, one (fit1) nested in the other (fit2), and I'd
like to compute the likelihood ratio comparing the models so I can say
something like "there is X times more evidence for fit1 than for fit2"
(as in Glover & Dixon, 2004, www.ncbi.nlm.nih.gov/pubmed/15732688).

I know I can use anova(fit1,fit2) to obtain a null-hypothesis
significance test of the fits, and I suspect the output also contains
the information I need to make my evidentiary statement, but I'm not
confident of what I'm doing here. Is it correct that the reported
value of chi-square from anova() is simply the D of the likelihood
ratio test (http://en.wikipedia.org/wiki/Likelihood_ratio_test)? If
so, does it sound right that I can simply derive the
complexity-corrected likelihood ratio as:

LR = exp( -2 * anova( fit1 , fit2 )$Chisq[2] )

?


Mike

Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University

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~ Certainty is folly... I think. ~