pamr.test.errors.surv.compute: A function giving a table of true versus predicted values,...

In pamr: Pam: Prediction Analysis for Microarrays

Description

A function giving a table of true versus predicted values, from a nearest shrunken centroid fit from survival data.

Usage

pamr.test.errors.surv.compute(proby, yhat)

Arguments

proby	Survival class probabilities, from pamr.surv.to.class2
yhat	Estimated class labels, from pamr.predict

Details

pamr.test.errors.surv.compute computes the erros between the true 'soft" class labels proby and the estimated ones "yhat"

Author(s)

Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu

Examples

 
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)

pamr documentation built on May 2, 2019, 12:35 p.m.