I am experimenting with parallel processing using foreach and seem to be missing something fundamental. Cool stuff. I've gone through the list and seen a couple of closely related issues, but nothing I've tried seems to work.
I know that the results from foreach are combined, but what if there is more than one variable within the loop? Below is a snippet (non-functioning) of code that I hope provides enough insight into what I am trying to do. The commented out lines are what I would be doing (successfully) if I wasn't trying to implement the %dopar% . The goal is to do statistics on the sequence of lambda vectors that were originally accumulated in the matrix lambdas using cbind. Thanks in advance for any suggestions, Dave ---------------snip update_N <- function(sets, indexes, lam) { n <- length(indexes)-1 # count of events N <- rep(0, K) # count of failures per node for (i in 1:n) { nodes <- as.numeric(sets[indexes[i]:(indexes[i+1]-1)]) node <- resample(nodes, 1, prob=lam[nodes]/sum(lam[nodes])) N[node] = N[node] + 1 } N } lambdas<- foreach(j=1:(2*burn_in), .combine=cbind) %dopar% { N <- update_N(min_sets, min_sets_indexes, lambda) lambda <- rgamma(K, shape=a+N, rate=bT) lambda if (j%%100==0) { print(j); print(lambda); print(N)} # if (j > burn_in) { # lambdas <- cbind(lambdas, lambda) # } } ---------------snip ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.