MCMC confidence intervals (strictly Bayesian credible intervals) give much
better results in some circumstances, notably for parameters that are poorly
estimated. Things are seldom a problem (In my experience) for fixed effects
unless the model is ill-specified, but the mcmc methods really score for
random effect paramters.

2008/7/10 Doran, Harold <HDoran@air.org>:

> Out of curiousity, why not just use the asymptotic standards errors of
> the fixed effects to get Cis rather than via simulation?
>
> > -----Original Message-----
> > From: r-sig-mixed-models-bounces@r-project.org
> > [mailto:r-sig-mixed-models-bounces@r-project.org] On Behalf
> > Of Jonathan Baron
> > Sent: Thursday, July 10, 2008 8:05 AM
> > To: Michael Kubovy
> > Cc: r-sig-mixed-models@r-project.org
> > Subject: Re: [R-sig-ME] State of lme4
> >
> > On 07/10/08 07:51, Michael Kubovy wrote:
> > > Dear Friends,
> > >
> > > I have become confused as to which set-up of lme4, arm,
> > gmodels, etc
> > > will produce CIs on the fixed effects by simulation.
> >
> > In the latest version of lme4, mcmcsamp and HPDinterval work.
> >  And languageR has a new version as of yesterday, which seems
> > to deal correctly with the current version of lme4.
> > Specifically, pvals.fnc() works.  I don't know about arm and
> > gmodels.  I haven't seen new versions, so I suspect they will
> > not deal with the new format of lmer.
> >
> > Jon
> > --
> > Jonathan Baron, Professor of Psychology, University of
> > Pennsylvania Home page: http://www.sas.upenn.edu/~baron
> >
> > _______________________________________________
> > R-sig-mixed-models@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



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