intccr (version 1.0.0)

bssmle_se: Bootstrap varince-covariance estimation

Description

Bootstrap varince estimation for the estimated regression coefficients

Usage

bssmle_se(formula, data, alpha, do.par, nboot)

Arguments

formula

a formula object relating survival object Surv2(v, u, event) to a set of covariates

data

a data frame to be used

alpha

\(\alpha=(\alpha1, \alpha2)\) contains parameters that that define the link functions from class of generalized odds-rate transformation models. The components \(\alpha1\) and \(\alpha2\) should both be \(\ge 0\). If \(\alpha1 = 0\), the user assumes a proportional subdistribution hazards or Fine-Gray model for cause of failure 1. If \(\alpha2 = 1\), the user assumes a proportional odds model for cause of failure 2.

do.par

using parallel computing for bootstrap. If TRUE, parallel computing will be used during the bootstrap estimation of the variance-covariance matrix for the regression parameter estimates.

nboot

a number of bootstrap samples for estimating variances and covariances of the estimated regression coefficients. If nboot = 0, ciregic does dot perform bootstrap estimation of the variance matrix of the regression parameter estimates and returns NA in the place of the estimated variance matrix of the regression parameter estimates.

Value

The function bssmle_se returns a list of components:

numboot

a number of bootstrap converged

Sigma

an estimated bootstrap variance-covariance matrix of the estimated regression coefficients

Details

The function bssmle_se estimates bootstrap standard errors for the estimated regression coefficients from the function bssmle.

Examples

# NOT RUN {
intccr:::bssmle_se(Surv2(v, u, c) ~ z1 + z2, data = simdat,
                   alpha = c(1, 1), do.par = FALSE, nboot = 1)
# }