gendata<-function(n=100, p=2000){
tim <- 3*abs(rnorm(n))
u<-runif(n,min(tim),max(tim))
y<-pmin(tim,u)
ic<-1*(timm] <- x[1:100, tim>m]+3
return(list(x=x,y=y,ic=ic))
}
# generate training data; 2000 genes, 100 samples
junk<-gendata(n=100)
y<-junk$y
ic<-junk$ic
x<-junk$x
d <- list(x=x,survival.time=y, censoring.status=ic,
geneid=as.character(1:nrow(x)), genenames=paste("g",as.character(1:nrow(x)),sep=
""))
# train model
a3<- pamr.train(d, ngroup.survival=2)
#make class predictions
yhat <- pamr.predict(a3,d$x, threshold=1.0)
pamr.plotsurvival(yhat, d$survival.time, d$censoring.status)
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