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Last data update: 2014.03.03

R: Fits a Vardi corrected survival curve, a nonparametric...

lbfit.nonpar

R Documentation

Fits a Vardi corrected survival curve, a nonparametric survival cuve estimation.

Description

An extended version of the Kaplan-Miere estimator.

Usage

lbfit.nonpar(time, censor, boot = FALSE, 
boot.control = list(quantile = TRUE, use.median = FALSE, 
confidence.level = 0.95, iter = 1000), 
fit.control = list(iter = 500, tol = 1e-06))

Arguments

`time`	The observed time as a vector.
`censor`	The survival indicator, 1 if censored, 0 otherwise.
`boot`	logical, for calculating bootstrap confidence bounds.
`boot.control`	Bootstrap control parameters, as a list.
`fit.control`	Nonparametric fit control parameters.

Details

A detailed description, or refer the reader to a work.

Value

The result of the function is an lbsurvfit object, including step functions for the nonparametric survival curve fit and its bootstrap confidence bounds.

Note

Bootstrapping is required if confidence bounds are needed.

Author(s)

V. P. Nia and P. J. Bergeron

References

Vardi Paper

Examples

mydata=lbsample(20,family="exponential",par=list(rate=1))
noboot=lbfit.nonpar(time=mydata$time,censor=mydata$censor)
plot(noboot$survfun)
withboot=lbfit.nonpar(time=mydata$time,censor=mydata$censor,boot=TRUE)
x=seq(0,max(mydata$time)+1,length=500)
plot(x,withboot$survfun(x),type="l",col="blue",ylim=c(0,1))
points(x,withboot$lowerfun(x),type="l",col="red")
points(x,withboot$upperfun(x),type="l",col="red")