----- Original Message ----- From: "Manuel Morales" <[EMAIL PROTECTED]> To: <[EMAIL PROTECTED]> Cc: "Douglas Bates" <[EMAIL PROTECTED]>; "Manuel Morales" <[EMAIL PROTECTED]>; <r-help@stat.math.ethz.ch> Sent: Wednesday, September 13, 2006 1:04 PM Subject: Re: [R] Conservative "ANOVA tables" in lmer
> 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. > You have this effect if you're using REML, for ML I don't think there is any problem to use LRT between nested models with different fixed-effects structure. Best, Dimitris >> 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. > > ______________________________________________ > 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. > Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm ______________________________________________ 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.
