[R] Using anova(f1, f2) to compare lmer models yields seemingly erroneous Chisq = 0, p = 1

[Bert Gunter]

My guess would be:

"Likelihood comparisons are not meaningful for objects fit using restricted
maximum likelihood and with different fixed effects. "

(from ?anova.lme in the nlme package).

Are you using the REML = TRUE default?

Bert Gunter
Genentech Nonclinical Statistics

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of rapton
Sent: Friday, September 04, 2009 9:10 AM
To: r-help at r-project.org
Subject: [R] Using anova(f1, f2) to compare lmer models yields seemingly
erroneous Chisq = 0, p = 1


Hello,

I am using R to analyze a large multilevel data set, using
lmer() to model my data, and using anova() to compare the fit of various
models.  When I run two models, the output of each model is generated
correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the
multilevel model output look perfectly reasonable), and in this case (see
below) predictor.1 explains vastly more variance in outcome than predictor.2
(R2 = 15% vs. 5% in OLS regression, with very large N).  What I am utterly
puzzled by is that when I run an anova comparing the two multilevel model
fits, the Chisq comes back as 0, with p = 1.  I am pretty sure that fit #1
(f1) is a much better predictor of the outcome than f2, which is reflected
in the AIC, BIC , and logLik values.  Why might anova be giving me this
curious output?  How can I fix it?  I am sure I am making a dumb error
somewhere, but I cannot figure out what it is.  Any help or suggestions
would 
be greatly appreciated!

-Matt


> f1 <- (lmer(outcome ~ predictor.1 + (1 | person), data=i))
> f2 <- (lmer(outcome ~ predictor.2 + (1 | person), data=i))
> anova(f1, f2)

Data: i
Models:
f1: outcome ~ predictor.1 + (1 | person)
f2: outcome ~ predictor.2 + (1 | person)
   Df    AIC      BIC    logLik   Chisq Chi Df Pr(>Chisq)
f1  6  45443  45489 -22715
f2 25  47317  47511 -23633     0     19          1
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