Thank you Peter and Ben for your comments. John
----- Original Message ---- From: Peter Dalgaard <pda...@gmail.com> To: array chip <arrayprof...@yahoo.com> Cc: r-help@r-project.org; r-sig-mixed-mod...@r-project.org Sent: Mon, September 20, 2010 12:28:43 PM Subject: Re: [R] lmer() vs. lme() gave different variance component estimates On 09/20/2010 08:09 PM, array chip wrote: > Thank you Peter for your explanation of relationship between aov and lme. It > makes perfect sense. > > > When you said "you might have computed the average of all 8 > measurements on each animal and computed a 1-way ANOVA" for treatment effect, > would this be the case for balanced design, or it is also true for unbalanced > data? It is only exactly true for a balanced design, although it can be a practical expedient in nearly-balanced cases, especially if there is a clearly dominant animal variation. In strongly unbalanced data, you get reduced efficiency because animals with less data should be downweighted (not proportionally if there is substantial between-animal variation, though). And of course the whole thing relies on the fact that you have individuals nested in treatment (no animals had multiple treatments) > > Another question is if 1-way ANOVA is equivalent to mixed model for testing > treatment effect, what would be reason why mixed model is used? Just to >estimate > > the variance components? If the interest is not in the estimation of variance > components, then there is no need to run mixed models to test treatment >effects? Not too far off the mark. In more complex cases, there is the advantage that the mixed model helps figure out a sensible analysis for you. > And my last question is I am glad to find that glht() from multcomp package > works well with a lmer() fit for multiple comparisons. Given Professor > Bates's > view that denominator degree's of freedom is not well defined in mixed > models, > are the results from glht() reasonable/meaningful? If not, will the suggested > 1-way ANOVA used together with glht() give us correct post-hoc multiple > comparsion results? I think Doug's view is that DFs are not _reliably_estimated_ with any of the current procedures. In the balanced cases, they are very well defined (well, give or take the issues with "negative variances"), and I would expect glht() to give meaningful results. Do check the residuals for at least approximate normality, though. > > Thank you very much! > > John > > > > > > ----- Original Message ---- > From: Peter Dalgaard <pda...@gmail.com> > To: array chip <arrayprof...@yahoo.com> > Cc: r-help@r-project.org; r-sig-mixed-mod...@r-project.org > Sent: Sat, September 18, 2010 1:35:45 AM > Subject: Re: [R] lmer() vs. lme() gave different variance component estimates > > > For a nested design, the relation is quite straightforward: The residual > MS are the variances of sample means scaled to be comparable with the > residuals (so that in the absense of random components, all > MS are equal to within the F-ratio variability). So to get the id:eye > variance component, subtract the Within MS from the id:eye MS and divide > by the number of replicates (4 in this case since you have 640 > observations on 160 eyes) (14.4 - 0.01875)/4 = 3.59, and similarly, the > id variance is the MS for id minus that for id:eye scaled by 8: > (42.482-14.4)/8 = 3.51. > > I.e. it is reproducing the lmer results above, but of course not those > from your original post. > > (Notice, by the way, that if you are only interested in the treatment > effect, you might as well have computed the average of all 8 > measurements on each animal and computed a 1-way ANOVA). > -- Peter Dalgaard Center for Statistics, Copenhagen Business School Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.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.