You can just use the parallel versions of lapply
or sapply
, instead of saying to replicate this expression n
times you do the apply on 1:n
and instead of giving an expression, you wrap that expression in a function that ignores the argument sent to it.
possibly something like:
#create cluster
library(parallel)
cl <- makeCluster(detectCores()-1)
# get library support needed to run the code
clusterEvalQ(cl,library(MASS))
# put objects in place that might be needed for the code
myData <- data.frame(x=1:10, y=rnorm(10))
clusterExport(cl,c("myData"))
# Set a different seed on each member of the cluster (just in case)
clusterSetRNGStream(cl)
#... then parallel replicate...
parSapply(cl, 1:10000, function(i,...) { x <- rnorm(10); mean(x)/sd(x) } )
#stop the cluster
stopCluster(cl)
as the parallel equivalent of:
replicate(10000, {x <- rnorm(10); mean(x)/sd(x) } )
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