Hi Bert,

Thank you for your note!  I tried changing the REML default, and it still
produces the same result (see below).  Is that what you meant for me to try?

Incidentally, I am using lmer() not lme()

 ### ORIGINAL ###
> 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

### DO NOT USE REML ###
> f1 <- (lmer(outcome ~ predictor.1 + (1 | person), data=i, REML = FALSE))
> f2 <- (lmer(outcome ~ predictor.2 + (1 | person), data=i, REML = FALSE))
> 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


On Fri, Sep 4, 2009 at 4:18 PM, Bert Gunter <gunter.berton@gene.com> wrote:

> 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@r-project.org [mailto:r-help-bounces@r-project.org]
> On
> Behalf Of rapton
> Sent: Friday, September 04, 2009 9:10 AM
> To: r-help@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
> --
> View this message in context:
>
> http://www.nabble.com/Using-anova%28f1%2C-f2%29-to-compare-lmer-models-yield
> s-seemingly-erroneous-Chisq-%3D-0%2C-p-%3D-1-tp25297254p25297254.html<http://www.nabble.com/Using-anova%28f1%2C-f2%29-to-compare-lmer-models-yield%0As-seemingly-erroneous-Chisq-%3D-0%2C-p-%3D-1-tp25297254p25297254.html>
> Sent from the R help mailing list archive at Nabble.com.
>
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