[R-sig-ME] mcmcglmm and parallel chains

[Dale Steele]

Just now hearing from Andrew Redd about his 'harvestr' package which
seems tailor made for this problem...

https://github.com/halpo/harvestr

On Wed, Jun 13, 2012 at 3:16 AM, Ben Bolker <bbolker at gmail.com> wrote:
> 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
>
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