survivalREC (version 1.1)

IPCWdf: Inverse probability of censoring weighting estimator for the bivariate distribution function.

Description

Provides estimates for the bivariate distribution function based on the Inverse Probability of Censoring Weighting estimator (IPCW).

Usage

IPCWdf(object, x, y, covariate, cov.value, bw, window = "gaussian")

Value

Vector with the IPWC estimates for the bivariate distribution function.

Arguments

object

An object of class multidf.

x

The first time for obtaining estimates for the bivariate distribution function.

y

The second time for obtaining estimates for the bivariate distribution function.

covariate

Name of the quantitative covariate.

cov.value

The value of the quantitative covariate.

bw

A single numeric value to compute a kernel density bandwidth. Use "dpik" for the KernSmooth package based selector or "np" for the 'npudensbw' function of the np package.

window

A character string specifying the desired kernel. See details below for possible options. Defaults to "gaussian" where the gaussian density kernel will be used.

Author

Gustavo Soutinho and Luis Meira-Machado.

Gustavo Soutinho and Luis Meira-Machado

References

de Una-Alvarez, J. and Meira-Machado, L. (2008). A simple estimator of the bivariate distribution function for censored gap times, Statistics and Probability Letters 78, 2440-2445.

See Also

KMWdf, LDMdf, LINdf and WCHdf.

Examples

data("bladder4state")

b3state<-multidf(gap1=bladder4state$y1, event1=bladder4state$d1, 
                 gap2=bladder4state$y2, status=bladder4state$d2, 
                 size=bladder4state$size)         

b3size<-multidf(gap1=bladder3$t1, event1=bladder3$d1, 
                gap2=bladder3$t2-bladder3$t1,status=bladder4state$d2, 
                size=bladder3$size)  

library(KernSmooth)

IPCWdf(object=b3state, x=13, y=15, covariate="size", cov.value=3, 
       window = "gaussian")
       
IPCWdf(object=b3state, x=13, y=15, covariate="size", bw=2, cov.value=3, 
       window = "gaussian")

IPCWdf(object=b3size, x=13, y=15, covariate="size", cov.value=3, 
       window = "gaussian")
       
IPCWdf(object=b3size, x=13, y=15, covariate="size", bw=2, cov.value=3, 
       window = "gaussian")