[R-sig-ME] Teaching Mixed Effects

[Juan Pedro Steibel]

Hello all,
This is my first post in the list as I just started to use lmer for my 
routine analyses (side by side with P.Mixed... just for now 8^D ).

The following comment caught my attention:
> I have not verified the results from the current mcmcsamp, even for
> simple Gaussian models.  They seem reasonable for these models but I
> need to look at them much more closely before I could advise trusting
> those results.
>
> The problem with designing an mcmcsamp method is that the variances of
> the random effects can legitimately be zero and often have a
> non-negligible probability of assuming the value of zero during the
> MCMC iteraions.  However, most methods of sampling from the
> distribution of a variance are based on sampling from the distribution
> of a multiplier of the current value.  If the current value is zero,
> you end up stuck there.
>   
If I understand correctly, it is claimed that once the Markov Chain hits 
a value 0 for a given VC, it stays there. Is this Correct? Should I 
interpret the statement above differently?

This is not the behavior I am observing in mcmcsamp, fitting models with 
a non-negligible posterior probability of certain VC=0, I see the chain 
hitting zero for a while, then leaving (VC>0) and back... the actual 
mixing is very good, even for a VC that is estimated as zero by REML 
(posterior mode of mcmc is ~practically~ zero). 
Thanks in advance
JP



-- 
=============================
Juan Pedro Steibel

Assistant Professor
Statistical Genetics and Genomics

Department of Animal Science & 
Department of Fisheries and Wildlife

Michigan State University
1205-I Anthony Hall
East Lansing, MI
48824 USA 

Phone: 1-517-353-5102
E-mail: steibelj at msu.edu