lbfit.nonpar: Fits a Vardi corrected survival curve, a nonparametric...

In lbiassurv: Length-biased correction to survival curve 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

See Also

lbfit.par, lbsample

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")

Example output

Loading required package: actuar

Attaching package: 'actuar'

The following object is masked from 'package:grDevices':

    cm

Warning message:
In lbfit.nonpar(time = mydata$time, censor = mydata$censor) :
  The boot option is FALSE, no confidence bounds are produced
bootstrapping...

lbiassurv documentation built on May 2, 2019, 9:35 a.m.