It would be easier to run multiple copies of R. Ken
On 08/03/2011, at 7:14 PM, 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..... > 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 > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-mac > _______________________________________________ R-SIG-Mac mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-mac
