This is an example of 'embarrassingly parallel' computation. Simply
run each chain in a separate process in parallel. Packages such as
snow or multicore can organize that for you.
However, if you mean logistic regression (there are other binomial
regressions such as probit), first check how you are doing this in
JAGS. Using 'module glm' often makes a large difference in speed, and
my recollection is that this is still not particularly fast compared
to, say, MCMCpack. And in any case the recommended way to run JAGS
with R is rjags (recommended by the author of JAGS, amonst others).
On Tue, 8 Mar 2011, Alan Kelly wrote:
Dear all, I'm running a number of Bayesian binomial regression
models using jags (interfacing with R via R2jags) on a Mac server
with quad core processor running at 2.66 Ghz with 6 GB memory under
Snow Leopard (session info below). As the models contain around 30
predictors and between 5 to 15 thousand observations, the time
required to run a single model with 3 chains with an adequate number
of iterations to ensure convergence is around 2 hours. While I can
live with this for the occasional run, it will be a problem when I
need to run several dozen different models. Perhaps some of you have
relevant experience and can advise if this run time could be
significantly reduced using, for example, one of the parallel
computing packages? And if so, which one? I should add that I'm
not clear if jags can directly avail of multicore processing even if
available - it might be necessary to program a Gibbs or Metropolis
sampler directly in R.....
Again, if you mean logistic regression there are specialised MCMC
schemes.
Any thoughts/suggestions?
Best wishes,
Alan Kelly
sessionInfo()
R version 2.12.1 (2010-12-16)
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
locale:
[1] en_IE.UTF-8/en_IE.UTF-8/C/C/en_IE.UTF-8/en_IE.UTF-8
attached base packages:
[1] splines stats graphics grDevices utils datasets methods base
other attached packages:
[1] car_2.0-9 survival_2.36-2 nnet_7.3-1 MASS_7.3-9
foreign_0.8-41
loaded via a namespace (and not attached):
[1] tools_2.12.1
_______________________________________________
R-SIG-Mac mailing list
R-SIG-Mac@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-mac
--
Brian D. Ripley, rip...@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
_______________________________________________
R-SIG-Mac mailing list
R-SIG-Mac@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-mac
