kmc.clean: Perform Data Clean for the kmc Algorithm

In yfyang86/kmc: Kaplan-Meier Estimator with Constraints for Right Censored Data -- a Recursive Computational Algorithm

Perform Data Clean for the kmc Algorithm

Description

The kmc.clean function clean the (kmc.time, delta) for the randomized censored data:

- Reorder the data according to the observed time and status;

- Clean the (right) censored data point(s) if they happen before the first uncesored data.

- If there are ties in the data. For the time points contain ties, e.g.

(T_{i_s}, d_{i_s}), i_s \in S \forall j \in S, T_{j} \equiv T

, we re-arranged the data in a manner that those with d=1 are ordered ahead of those with d=0. As d=0 indicates the data point is right censored, such procedure is trivial.

Usage

kmc.clean(kmc.time, delta)

Arguments

kmc.time	Non-negative real vector. The observed time.
delta	0/1 vector. Censoring status indictator, 0: right censored; 1 uncensored

Value

A list with the following components:

kmc.time	The cleaned observed time.
delta	The cleaned censoring status indictator, 0: right censored; 1 uncensored

Author(s)

Yifan Yang([e-mail])

References

Zhou, M. and Yang, Y. (2015). A recursive formula for the Kaplan-Meier estimator with mean constraints and its application to empirical likelihood Computational Statistics Online ISSN 1613-9658.

Examples

x <- c( 1, 1.5, 2, 3, 4.2, 5.0, 6.1, 5.3, 4.5, 0.9, 2.1, 4.3) 
d <- c( 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1) 
kmc.clean(x, d)

yfyang86/kmc documentation built on Nov. 29, 2022, 1:27 p.m.