This function estimates the bootstrap standard errors for the finite-dimensional model parameters and for the non-parametric cumulative hazard function. Parallel computing using foreach has been used to speed up the estimation of standard errors.
boot.fun(
init,
resData,
X,
W,
lhat,
cumL,
dist,
k,
lb,
ub,
Obs.time,
cop,
n.boot,
n.iter,
ncore,
eps
)Bootstrap standard errors for parameter estimates and for estimated cumulative hazard function.
Initial values for the finite dimensional parameters obtained from the fit of fitDepCens
Data matrix with three columns; Z = the observed survival time, d1 = the censoring indicator of T and d2 = the censoring indicator of C.
Data matrix with covariates related to T
Data matrix with covariates related to C. First column of W should be a vector of ones
Initial values for the hazard function obtained from the fit of fitDepCens based on the original data.
Initial values for the cumulative hazard function obtained from the fit of fitDepCens based on the original data.
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.
Dimension of X
lower boundary for finite dimensional parameters
Upper boundary for finite dimensional parameters
Observed survival time, which is the minimum of T, C and A, where A is the administrative censoring time.
Which copula should be computed to account for dependency between T and C. This argument can take
one of the values from c("Gumbel", "Frank", "Normal").
Number of bootstraps to use in the estimation of bootstrap standard errors.
Number of iterations; the default is n.iter = 20. The larger the number of iterations, the longer the computational time.
The number of cores to use for parallel computation is configurable, with the default ncore = 7.
Convergence error. This is set by the user in such away that the desired convergence is met; the default is eps = 1e-3