On 09/21/2010 09:02 PM, Douglas Bates wrote: > On Tue, Sep 21, 2010 at 1:39 PM, Douglas Bates <ba...@stat.wisc.edu> wrote: >> I haven't had the time to keep up with this discussion, or many of the >> other discussions on the R-SIG-Mixed-Models email list. I swamped >> with other duties at present.
It's not like I don't know how you feel.... >> >> It is important to remember that the nlme and lme4 packages take a >> model specification and provide code to evaluate the deviance. Other >> code is used to optimize the deviance. In the case of nlme it is the >> nlminb function and in the case of lme4 it is the minqa function. > > Shouldn't send email when I'm tired. The optimizer function is bobyqa > in the minqa package. (Sort of makes you nostalgic for the punched > cards and line printers when you see the Fortran names jammed into six > characters.) > >> There are no guarantees for numerical optimization routines. If the >> problem is ill-conditioned then they can converge to "optima" that are >> not the global optimum. Right. However, what we have here is a case where I'm pretty damn sure that the likelihood function is unimodal (it's a linear reparametrization of three independent chi-square terms) and has an optimum in the interior of the feasible region. In any case, I'm thoroughly confused about the lme4/lme4a/lme4b subtrees. Which algorithm is used by the current CRAN lme4?? (I have > sessionInfo() R version 2.11.1 (2010-05-31) i386-redhat-linux-gnu locale: [1] LC_CTYPE=en_US.utf8 LC_NUMERIC=C [3] LC_TIME=en_US.utf8 LC_COLLATE=en_US.utf8 [5] LC_MONETARY=C LC_MESSAGES=en_US.utf8 [7] LC_PAPER=en_US.utf8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.utf8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] lme4_0.999375-35 Matrix_0.999375-39 lattice_0.18-8 loaded via a namespace (and not attached): [1] grid_2.11.1 nlme_3.1-96 stats4_2.11.1 tcltk_2.11.1 tools_2. ) >> >> Whoever suggested using the verbose option to see the progress of the >> iterations has the right idea. >> >> >> On Mon, Sep 20, 2010 at 2:50 PM, array chip <arrayprof...@yahoo.com> wrote: >>> 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. >>> >> -- 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.