On Wed, Sep 13, 2006 at 07:04:17AM -0400, Manuel Morales wrote: > On Wed, 2006-09-13 at 08:04 +1000, Andrew Robinson wrote: > > On Tue, September 12, 2006 7:34 am, Manuel Morales wrote: > > > On Mon, 2006-09-11 at 11:43 -0500, Douglas Bates wrote: > > >> Having made that offer I think I will now withdraw it. Peter's > > >> example has convinced me that this is the wrong thing to do. > > >> > > >> I am encouraged by the fact that the results from mcmcsamp correspond > > >> closely to the correct theoretical results in the case that Peter > > >> described. I appreciate that some users will find it difficult to > > >> work with a MCMC sample (or to convince editors to accept results > > >> based on such a sample) but I think that these results indicate that > > >> it is better to go after the marginal distribution of the fixed > > >> effects estimates (which is what is being approximated by the MCMC > > >> sample - up to Bayesian/frequentist philosophical differences) than to > > >> use the conditional distribution and somehow try to adjust the > > >> reference distribution. > > > > > > Am I right that the MCMC sample can not be used, however, to evaluate > > > the significance of parameter groups. For example, to assess the > > > significance of a three-level factor? Are there better alternatives than > > > simply adjusting the CI for the number of factor levels > > > (1-alpha/levels). > > > > I wonder whether the likelihood ratio test would be suitable here? That > > seems to be supported. It just takes a little longer. > > > > > require(lme4) > > > data(sleepstudy) > > > fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) > > > fm2 <- lmer(Reaction ~ Days + I(Days^2) + (Days|Subject), sleepstudy) > > > anova(fm1, fm2) > > > > So, a brief overview of the popular inferential needs and solutions would > > then be: > > > > 1) Test the statistical significance of one or more fixed or random > > effects - fit a model with and a model without the terms, and use the LRT. > > I believe that the LRT is anti-conservative for fixed effects, as > described in Pinheiro and Bates companion book to NLME.
Yes, you are right. I had forgotten that. Back to square one :). Bert Gunter also kindly pointed this out to me. Cherse Andrew > > 2) Obtain confidence intervals for one or more fixed or random effects - > > use mcmcsamp > > > > Did I miss anything important? - What else would people like to do? > > > > Cheers > > > > Andrew > > > > Andrew Robinson > > Senior Lecturer in Statistics Tel: +61-3-8344-9763 > > Department of Mathematics and Statistics Fax: +61-3-8344 4599 > > University of Melbourne, VIC 3010 Australia > > Email: [EMAIL PROTECTED] Website: http://www.ms.unimelb.edu.au > > > > ______________________________________________ > > R-help@stat.math.ethz.ch 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. -- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-9763 University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599 Email: [EMAIL PROTECTED] http://www.ms.unimelb.edu.au ______________________________________________ R-help@stat.math.ethz.ch 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.
