Beran | R Documentation |
Computes the conditional survival probability P(T > y|Z = z)
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 and Marta Sestelo
R. Beran. Nonparametric regression with randomly censored survival data. Technical report, University of California, Berkeley, 1981.
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)
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