Dear all,

I am a phd candidate working on aging in some bird species. To achieve this,
I have started to use MCMCglmm a few month ago. To detect senescence, I need
using large data sets and hence I have to wait about two weeks or often more
for the runs to complete. My major problem is that the capricious
electricity network often shut down my computers, so I am looking for ideas
to reduce computation time (and finish runs).

I have read on the internet that it is too complicated (or impossible) to
gain time on the algorithm. So, I have decided to use the package "doMC" in
order to divide the markov chain between the cores of my computers. With
this, instead of running a model with 4300000 iterations (with 300000
burning iterations), I would run in parallel four models (one by core) with
1300000 iterations (300000 burning iterations and the same prior in the 4
models).

My question : Is it possible to concatenate results after burning (Sol, VCV,
deviance) and use it like a single big model or is it conceptually wrong? I
know that I loose computation time by multiplying burning but is it wrong to
do this alternative strategy?

Thanks a lot in advance for your input,

Best


 Stephane

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Stephane Chantepie
CNRS Phd  candidate
Muséum national d'Histoire naturelle
55 rue Buffon
75005 paris
Tel : 01 40 79 32 03
E-mail : chantepie@mnhn.fr
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