boot.funI {SemiPar.depCens}

Nonparametric bootstrap approach for the independent censoring model

Description

This function estimates the bootstrap standard errors for the finite-dimensional model parameters and for the non-parametric cumulative hazard function under the assumption of independent censoring. Parallel computing using foreach has been used to speed up the computation.

Usage

boot.funI(
  init,
  resData,
  X,
  W,
  lhat,
  cumL,
  dist,
  k,
  lb,
  ub,
  Obs.time,
  n.boot,
  n.iter,
  eps
)

Arguments

init

Initial values for the finite dimensional parameters obtained from the fit of fitIndepCens

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. First column of W should be a vector of ones

lhat

Initial values for the hazard function obtained from the fit of fitIndepCens based on the original data

cumL

Initial values for the cumulative hazard function obtained from the fit of fitIndepCens based on the original data

dist

The distribution to be used for the dependent censoring time C. Only two distributions are allowed, i.e, Weibull and lognormal distributions. With the value "Weibull" as the default

k

Dimension of X

lb

lower boundary for finite dimensional parameters

ub

Upper boundary for finite dimensional parameters

Obs.time

Observed survival time, which is the minimum of T, C and A, where A is the administrative censoring time.

n.boot

Number of bootstraps to use in the estimation of bootstrap standard errors.

n.iter

Number of iterations; the default is n.iter = 20. 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.