Beran {survivalREC} | R Documentation |
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"
Value
Vector with the estimation of the conditional distribution function of the response, given the covariate under random censoring.
Author(s)
Gustavo Soutinho and Luis Meira-Machado
References
R. Beran. Nonparametric regression with randomly censored survival data. Technical report, University of California, Berkeley, 1981.
Examples
data("bladder4state")
b3state<-multidf(gap1=bladder4state$y1, event1=bladder4state$d1,
gap2=bladder4state$y2, status=bladder4state$d2,
size=bladder4state$size)
head(b3state[[1]])
##P(T>y|size=3)
library(KernSmooth)
obj0 <- b3state[[1]]
h <- dpik(obj0$size)
Beran(time = obj0$time, status = obj0$status, covariate =obj0$size, x = 3,
y = 50, bw = h)
##P(T<=y|size=3)
Beran(time = obj0$time, status = obj0$status, covariate =obj0$size, x = 3,
y = 50, bw = h,
lower.tail = TRUE)