pamr.surv.to.class2 {pamr}

A function to assign observations to categories, based on their survival times.

Description

A function to assign observations to categories, based on their survival times.

Usage

pamr.surv.to.class2(
  y,
  icens,
  cutoffs = NULL,
  n.class = NULL,
  class.names = NULL,
  newy = y,
  newic = icens
)

Arguments

y

vector of survival times

icens

Vector of censorng status values: 1=died, 0=censored

cutoffs

Survival time cutoffs for categories. Default NULL

n.class

Number of classes to create: if cutoffs is NULL, n.class equal classes are created.

class.names

Character names for classes

newy

New set of survival times, for which probabilities are computed (see below). Default is y

newic

New set of censoring statuses, for which probabilities are computed (see below). Default is icens

Details

pamr.pamr.surv.to.class2 splits observations into categories based on their survival times and the Kaplan-Meier estimates. For example if n.class=2, it makes two categories, one below the median survival, the other above. For each observation (newy, ic), it then computes the probability of that observation falling in each category. For an uncensored observation that probability is just 1 or 0 depending on when the death occurred. For a censored observation, the probabilities are based on the Kaplan Meier and are typically between 0 and 1.

Value

class

The category labels

prob

The estimates class probabilities

cutoffs

The cutoffs used

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