mcmcglmm and parallel chains

Hans Ekbrand <hans at ...> writes:

I am learning mcmcglmm in order to use it on a beowulf cluster.

In https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q3/006558.html

Jarrod Hadfield writes:

"You can merge MCMC chains from multiple runs, although you should make
sure you start them from different initial values"

Is it sufficient to provide differents random seeds for each run,
or does this refer to the start parameter of mcmcglmm()?

   start: optional list having 4 possible elements: ?R? (R-structure)
          ?G? (G-structure) and ?liab? (latent variables or
          liabilities) should contain the starting values where ?G?
          itself is also a list with as many elements as random effect
          components. The fourth element ?QUASI? should be logical: if
          ?TRUE? starting latent variables are obtained heuristically,
          if ?FALSE? then they are sampled from a Z-distribution

It depends a bit on what your computational issues are.  It would
probably be _better_ to use multiple starting points, but if you are
sure you have no problem with burn-in then you can start all the chains
at the same points and rely on the different random-number seeds to allow
the chains to explore parameter space independently.  (Using multiple
starting points would would also allow you to use the Gelman-Rubin
diagnostic to assess convergence.)
   I would do some experiments with MCMCglmm to ensure that you know
how random seeds work with it (i.e. that you get identical answers
if and only if random seeds are set the same).  You may also want/need
to look at some of the comments in the high performance task view about
random number streams for parallel computation.

  
to look into