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

In condSURV: Estimation of the Conditional Survival Function for Ordered Multivariate Failure Time Data

Estimation of the conditional distribution function of the response, given the covariate under random censoring.

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 and Marta Sestelo

References

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

Examples

obj <- with(colonCS, survCS(time1, event1, Stime, event))

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

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

condSURV documentation built on March 7, 2023, 5:19 p.m.