# --- Generate data sets
n<-20 # sample size
p<-5 # number of genes
genes.name<-paste("G",seq(1,p),sep="") # genes name
data<-matrix(rnorm(p*n),n,p) # generate expression matrix
W<-abs(matrix(rnorm(p*p),p,p)) # generate weights for regulatory relationships
# --- Standardize variables to mean 0 and variance 1
data <- (apply(data, 2, function(x) { (x - mean(x)) / sd(x) } ))
# --- Run iRafNet and obtain importance score of regulatory relationships
out.iRafNet<-iRafNet(data,W,mtry=round(sqrt(p-1)),ntree=1000,genes.name)
# --- Run iRafNet for one permuted data set and obtain importance scores
out.perm<-iRafNet_permutation(data,W,mtry=round(sqrt(p-1)),ntree=1000,genes.name,perm=1)
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