On Mon, Sep 7, 2009 at 10:34 AM, Alain Zuur<highs...@highstat.com> wrote: > > > > rapton wrote: >> >> 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.
And, unless I'm missing something, also by the (misspecified) test. A large p-value indicates you have no evidence that the additional 19 parameters in f2 improve fit, which matches what the other methods suggested. However, as has been pointed out, the lack of nesting makes this a faulty LRT. This is made apparent by the fact that you get a test statistic outside the support of the chi-squared distribution (positive reals) > (lambda <- (-2)*(-22715 - (-23633))) [1] -1836 and since the test is uses right-tail probability, anova is not changing anything by moving the statistic to 0. > pchisq(lambda, 19, lower = FALSE) [1] 1 > pchisq(0, 19, lower = FALSE) [1] 1 To do the test properly the restricted (null) model must be a special case of the general (alternative) model (e.g., with the additional 19 parameters set to zero) which will result in the null model having a smaller likelihood, leading to a positive tests statistic. When that statistic is small you get a large p-value indicating a lack of evidence that the additional parameters improve fit... hth, Kingsford 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 >> > > > ** NOT ** nested ....sorry....the brain is going faster than the > fingers. > > > > > > ----- > -------------------------------------------------------------------- > Dr. Alain F. Zuur > First author of: > > 1. Analysing Ecological Data (2007). > Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p. > > 2. Mixed effects models and extensions in ecology with R. (2009). > Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer. > > 3. A Beginner's Guide to R (2009). > Zuur, AF, Ieno, EN, Meesters, EHWG. Springer > > > Statistical consultancy, courses, data analysis and software > Highland Statistics Ltd. > 6 Laverock road > UK - AB41 6FN Newburgh > Email: highs...@highstat.com > URL: www.highstat.com > > > > -- > View this message in context: > http://www.nabble.com/Using-anova%28f1%2C-f2%29-to-compare-lmer-models-yields-seemingly-erroneous-Chisq-%3D-0%2C-p-%3D-1-tp25297254p25333148.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. > ______________________________________________ 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.