Beran: Estimation of the conditional distribution function of the...

In survidm: Inference and Prediction in an Illness-Death Model

Description

Computes the conditional survival probability P(T > y|Z = z)

Usage

 Beran(time, status, covariate, delta, x, y, kernel = "gaussian", bw, lower.tail = FALSE)

Arguments

time	The survival time of the process.
status	Censoring indicator of the total time of the process; 0 if the total time is censored and 1 otherwise.
covariate	Covariate values for obtaining estimates for the conditional probabilities.
delta	Censoring indicator of the covariate.
x	The first time (or covariate value) for obtaining estimates for the conditional probabilities. If missing, 0 will be used.
y	The total time for obtaining estimates for the conditional probabilities.
kernel	A character string specifying the desired kernel. See details below for possible options. Defaults to "gaussian" where the gaussian density kernel will be used.
bw	A single numeric value to compute a kernel density bandwidth.
lower.tail	logical; if FALSE (default), probabilities are P(T > y|Z = z) otherwise, P(T <= y|Z = z).

Details

Possible options for argument window are "gaussian", "epanechnikov", "tricube", "boxcar", "triangular", "quartic" or "cosine".

Author(s)

Luis Meira-Machado, Marta Sestelo and Gustavo Soutinho

References

R. Beran. Nonparametric regression with randomly censored survival data. Technical report, University of California, Berkeley, 1981.

Examples

obj <- with(colonIDM, survIDM(time1, event1, Stime, event))
obj0 <- obj

#P(T>y|age=45)
library(KernSmooth)
h <- dpik(colonIDM$age)
Beran(time = obj0$Stime, status = obj0$event, covariate = colonIDM$age,
x = 45, y = 730, bw = h)

#P(T<=y|age=45)
Beran(time = obj0$Stime, status = obj0$event, covariate = colonIDM$age,
x = 45, y = 730, bw = h, lower.tail = TRUE)

Example output

KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
[1] 0.7963267
[1] 0.2036733

survidm documentation built on June 25, 2021, 1:07 a.m.