A function giving a table of true versus predicted values, from a nearest shrunken centroid fit from survival data.
A function giving a table of true versus predicted values, from a nearest shrunken centroid fit from survival data.
pamr.test.errors.surv.compute(proby, yhat)
proby |
Survival class probabilities, from pamr.surv.to.class2 |
yhat |
Estimated class labels, from pamr.predict |
pamr.test.errors.surv.compute computes the erros between the
true 'soft" class labels proby and the estimated ones "yhat"
Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu
gendata<-function(n=100, p=2000){
tim <- 3*abs(rnorm(n))
u<-runif(n,min(tim),max(tim))
y<-pmin(tim,u)
ic<-1*(tim<u)
m <- median(tim)
x<-matrix(rnorm(p*n),ncol=n)
x[1:100, tim>m] <- 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$x
d <- list(x=x,survival.time=y, censoring.status=ic,
geneid=as.character(1:nrow(x)), genenames=paste("g",as.character(1:nrow(x)),sep=
""))
# train model
a3<- pamr.train(d, ngroup.survival=2)
# generate test data
junkk<- gendata(n=500)
dd <- list(x=junkk$x, survival.time=junkk$y, censoring.status=junkk$ic)
# compute soft labels
proby <- pamr.surv.to.class2(dd$survival.time, dd$censoring.status,
n.class=a3$ngroup.survival)$prob
# make class predictions for test data
yhat <- pamr.predict(a3,dd$x, threshold=1.0)
# compute test errors
pamr.test.errors.surv.compute(proby, yhat)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.