boot.nonparTrans: Nonparametric bootstrap approach for a Semiparametric...

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpring

Description

This function estimates the bootstrap standard errors for the finite-dimensional model parameters and for the non-parametric transformation function. Parallel computing using foreach has been used to speed up the estimation of standard errors.

Usage

boot.nonparTrans(init, resData, X, W, n.boot, n.iter, eps)

Arguments

init	Initial values for the finite dimensional parameters obtained from the fit of NonParTrans
resData	Data matrix with three columns; Z = the observed survival time, d1 = the censoring indicator of T and d2 = the censoring indicator of C.
X	Data matrix with covariates related to T
W	Data matrix with covariates related to C.
n.boot	Number of bootstraps to use in the estimation of bootstrap standard errors.
n.iter	Number of iterations; the default is n.iter = 15. The larger the number of iterations, the longer the computational time.
eps	Convergence error. This is set by the user in such away that the desired convergence is met; the default is eps = 1e-3

Value

Bootstrap standard errors for parameter estimates and for estimated cumulative hazard function.

depCensoring documentation built on April 4, 2025, 1:52 a.m.