# km: Kaplan-Meier Survivor Curves In event: Event History Procedures and Models

## Description

km calculates the Kaplan-Meier estimates for survival.

To plot the survivor curve, use plot(); for the empirical intensity curve, use plot.intensity(); for diagnostic curves to choose a distribution to which the data might belong, use plot.dist().

## Usage

 1 2 3 4 5 6 7 8 km(times, censor, group=1, freq=1, cdf=FALSE) ## S3 method for class 'km' plot(x, add=FALSE, xlim=NULL, ylim=c(0,1), main=NULL, xlab="Time", ylab=NULL, lty=NULL, ...) ## S3 method for class 'km' plot.intensity(x, add=FALSE, xlab="Time", ylab="Hazard", type="l", lty=NULL, ...) ## S3 method for class 'km' plot.dist(x, ...)

## Arguments

 times Vector of times to events or a list of vectors of such times for different individuals. censor Vector of censoring indicators corresponding to the vector of times or to the last time in each vector of a list. group Vector indicating to which group each individual belongs. freq Vector of frequencies for grouped data. cdf If TRUE, calculate the cdf instead of the survivor curve. x An object produced by km. add Plotting control options. main Plotting control options. type Plotting control options. ylab Plotting control options. xlab Plotting control options. xlim Plotting control options. ylim Plotting control options. lty Plotting control options. ... Plotting control options.

## Value

A matrix with class, km, containing the Kaplan-Meier estimates is returned.

J.K. Lindsey

## Examples

 1 2 3 4 5 6 7 surv <- rgamma(40,2,scale=5) cens <- rbinom(40,1,0.9) treat <- gl(2,20) plot(km(surv, cens, group=treat), main="",xlab="Months", ylab="Probability of deterioration") plot.dist(km(surv, cens, group=treat)) plot.intensity(km(surv, cens, group=treat),ylab="Risk of deterioration")