I have this function and want to run it parallel with different sets of data.
Using SNOW and clusterApplyLB.
system.time(out <- mclapply(cData, plotGraph)) #each cData contains 100X6000
doubles
system.time(out <- mclapply(cData2, plotGraph))
system.time(out <- mclapply(cData3, plotGraph))
system.time(out <- mclapply(cData4, plotGraph))
system.time(out <- mclapply(cData5, plotGraph))
system.time(out <- mclapply(cData6, plotGraph))
plotGraph() <- function(cData)
{
cl = unname(cor(cData))
result = cbind(as.vector(row(cl)),as.vector(col(cl)),as.vector(cl))
result = result[result[,1] != result[,2],]
corm = result
corm =corm[abs(corm[,3]) >= CORRELATION, ]
# remove low cor pairs
library(network); library(sna)
net <- network(corm, directed = F)
# the network
cd <- component.dist(net)
# component analysis
delete.vertices(net, which(cd$csize[cd$membership] == 1))
# delete genes not connected with others
plot(net)
}
--
View this message in context:
http://r.789695.n4.nabble.com/using-SNOW-and-clusterApplyLB-to-run-jobs-parallel-tp3253060p3253060.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
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.
