LDM3df: Landmark estimator for the general case of K gap times...

In gsoutinho/survrec: Estimation of the multivariate distributions for censored gap times

Description

Provides estimates for the general case of K gap times distribution function based on landmarking.

Usage

LDM3df(object, x, y, z)

Arguments

object	An object of class multidf.
x	The first time for obtaining estimates for the general case of distribution function.
y	The second time for obtaining estimates for the general case of distribution function.
z	The third time for obtaining estimates for the general case of distribution function.

Author(s)

Gustavo Soutinho and Luis Meira-Machado

References

van Houwelingen, H.C. (2007). Dynamic prediction by landmarking in event history analysis, Scandinavian Journal of Statistics, 34, 70-85. Kaplan, E. and Meier, P. (1958). Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association 53(282), 457-481.

See Also

LDM3df, LIN3df and WCH3df.

Examples

b4state <- multidf(time1=bladder5state$y1, event1=bladder5state$d1,
time2= bladder5state$y1+bladder5state$y2, event2=bladder5state$d2,
time=bladder5state$y1+bladder5state$y2+bladder5state$y3,
status=bladder5state$d3)
head(b4state)[[1]]

LDM3df(b4state,x=13,y=20,z=40)

b4 <- multidf(time1=bladder4$t1, event1=bladder4$d1,
             time2= bladder4$t2, event2=bladder4$d2,
             time=bladder4$t3, status=bladder4$d3)
LDM3df(b4,x=13,y=20,z=40)

gsoutinho/survrec documentation built on Dec. 20, 2021, 1:46 p.m.