[R-sig-hpc] getting multiple results back from foreach using MPI

[Maas James Dr (MED)]

I'm relatively new to this.  I have a toy script (listed below) that runs on a SGE cluster using doMPI.  When I ask for the results ("stuff"), all I'm getting is the final variable calculated ("bsiaicORSD") as shown here.  I need the function to return both "bsiaicOR" and "bsiaic".  What do I need to do to get foreach to give me both back? I've tried using a "return" statement within the foreach loop but got a "multi argument returns are not permitted" error message.

Thanks

J

> stuff
[[1]]
           [,1]       [,2]       [,3]
[1,] 0.00000000 0.05813405 0.03851296
[2,] 0.09045419 0.00000000 0.06218322
[3,] 0.11871065 0.12007783 0.00000000

[[2]]
           [,1]       [,2]       [,3]
[1,] 0.00000000 0.05813405 0.03851296
[2,] 0.09045419 0.00000000 0.06218322
[3,] 0.11871065 0.12007783 0.00000000

[[3]]
           [,1]       [,2]       [,3]
[1,] 0.00000000 0.05813405 0.03851296
[2,] 0.09045419 0.00000000 0.06218322
[3,] 0.11871065 0.12007783 0.00000000

======================================================

library(doMPI)
cl <- startMPIcluster()
registerDoMPI(cl)

  results.overall <- array (dim = c(0,6))
  bsiaic <- read.table(file="extern.bsiaic.dat",header=TRUE)
  bsiaictritot <- nrow(bsiaic)
  bsiaictreatnum=max(bsiaic[,4])

  bsiaic.data <- list(Ntrials =bsiaictritot,ntreat=bsiaictreatnum,
                      treat0=bsiaic[,3],treat1=bsiaic[,4],
                      total.patientnums.trt1=bsiaic[ ,2],
                      total.patientnums.trt2=bsiaic[ ,2],
                      num.countstrt1=bsiaic[ ,5],
                      num.countstrt2=bsiaic[ ,6])

  bsiaic.inits <- list( list (tau.eta=1, tau.theta=1,
                              thetamean0=0, eta.mean=c(rep(0,bsiaictreatnum))))

stuff <- foreach (i=1:3, .packages="rjags") %dopar% {

  bsiaicjags <- jags.model("aicmodel.txt", data=bsiaic.data, inits=bsiaic.inits,
                           n.chain=1, n.adapt=1000 )

  results <- jags.samples(bsiaicjags, variable.names =c("logOR", "OR",
                                        "eta.mean", "tau.eta","tau.theta"), n.iter=1000)

  bsiaicOR <-  apply(results$OR, c(1,2), mean)
  bsiaicORSD <- apply(results$OR, c(1,2), sd)

}

stuff

closeCluster(cl)
mpi.quit()

rm(list=ls())


===============================
Dr. Jim Maas
University of East Anglia