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, r-help-request at stat.math.ethz.ch wrote:
> From: Roel de Jong <dejongroel at gmail.com>
> Date: 29 September 2005 11:19:38 PM
> To: r-help at 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: john.maindonald at anu.edu.au
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.
