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 1.28.0 Index]