Hello R gurus,
I have the code below for which i need help and pointers to make it run in
parallel on a dual core win7 computer with R 2.13.x, using foreach,
iterators,doMC.
library(scatterplot3d) # Loads 3D library.
library(fields)
library(MASS)
library(ROCR)
library(verification)
library(caret)
library(gregmisc)
##simulated data
d=replicate(9, rnorm(40)+10)
colnames(d)<-c("LEPTIN","SAA","PTH","sEGFR","IGFBP6","MMP2","OPG","IGFBP3","PDGFAABB")
mols=c("LEPTIN","SAA","PTH","sEGFR","IGFBP6","MMP2","OPG","IGFBP3","PDGFAABB")
####Name of the results output
file1="AUCvalues_3plex.csv"
temp1=c('protein comb', 'AUC')
pdf('ROC Charts-3plex.pdf')
#generate combinations
pc3 = combinations(n=length(mols),r=3)
#runing the combinations
for (len in 1:dim(pc3)[1])
{
prs = pc3[len,]
## new data mat
samples <- mols[prs]
mat <-data [,c(samples,'Self_T1D')]
mat<-mat[complete.cases(mat),]
##### LDA #########
rows<- c(1:nrow(mat))
scores <- c()
labels <-c()
for (itr in 1:1000)
{
train <- sample(rows, length(rows)-1)
label =0 ; if (mat$Self_T1D[-train] == "N") label = 1 #need the value for
this line, should it be 'N' or 'Y'
z <- lda(Self_T1D ~ ., mat, subset = train)
score = predict(z, mat[-train, ])$pos[1]
scores <- c(scores, score)
labels<- c(labels, label)
}
########## ROC #########
pred <- prediction(scores, labels)
perf <- performance(pred,"tpr", "fpr")
plot(perf,colorize = F)
# plot a ROC curve for a single prediction run
# with CI by bootstrapping and fitted curve
#roc.plot(labels,scores, xlab = "False positive rate",
#ylab = "True positive rate", main = NULL, CI = T, n.boot = 100, plot =
"both", binormal = TRUE)
auc <- as.numeric(performance(pred, measure = "auc", x.measure =
"cutoff")@y.values)
auc = round(auc,3)
text(.9,0,paste(" AUC=", auc, sep=" "), cex=1)
names1 = paste (samples, collapse="+")
text(.8,.05,names1, cex=0.75)
temp = c(names1, auc)
temp1 = rbind(temp1 , temp)
print(paste(3,' ','done len=',len," ",names1,' ',date(),sep=''))
}
dev.off()
write.csv(temp1 , file=file1)
Thanks
sharad
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