pamr.test.errors.surv.compute {pamr} | R Documentation

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

### 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)

[Package pamr version 1.42.0 Index]