With lme4, use of mcmcsamp can be insightful. (Douglas Bates drew my attention to this function in a private exchange of emails.) The distributions of random effects are simulated on a log scale, where the distributions are much closer to symmetry than on the scale of the random effects themselves. As far as I can see, this is a straightforward use of MCMC to estimate model parameters; it is not clear to me the results from the lmer() fit are used. John Maindonald.
On 30 Sep 2005, at 8:00 PM, [EMAIL PROTECTED] wrote: > From: Roel de Jong <[EMAIL PROTECTED]> > Date: 29 September 2005 11:19:38 PM > To: r-help@stat.math.ethz.ch > Subject: [R] standard error of variances and covariances of the > random effects with LME > > > Hello, > > how do I obtain standard errors of variances and covariances of the > random effects with LME comparable to those of for example MlWin? I > know you shouldn't use them because the distribution of the > estimator isn't symmetric blablabla, but I need a measure of the > variance of those estimates for pooling my multiple imputation > results. > > Regards, > Roel. John Maindonald email: [EMAIL PROTECTED] phone : +61 2 (6125)3473 fax : +61 2(6125)5549 Centre for Bioinformation Science, Room 1194, John Dedman Mathematical Sciences Building (Building 27) Australian National University, Canberra ACT 0200. ______________________________________________ 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