On 01.02.2011 23:07, kparamas wrote:
I have this function and want to run it parallel with different sets of data.
Using SNOW and clusterApplyLB.
Nice, but what is your question?
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Uwe Ligges
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)
}
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
