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.ber...@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-boun...@r-project.org [mailto:r-help-boun...@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. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.