Beran: Estimation of the conditional distribution function of the...
In sestelo/idmsurv: Inference and Prediction in an Illness-Death Model

Description Usage Arguments Details Author(s) References Examples

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

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

`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).

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

Luis Meira-Machado, Marta Sestelo and Gustavo Soutinho

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

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)