confint.margstd_boot: Bootstrap confidence intervals

In tgerke/risks: Estimate risk ratios and risk differences using regression

Bootstrap confidence intervals

Description

Confidence intervals for models fit using marginal standardization based on parametric bootstrapping.

Usage

## S3 method for class 'margstd_boot'
confint(
  object,
  parm = NULL,
  level = 0.95,
  bootrepeats = 1000,
  bootci = c("bca", "normal", "nonpar"),
  jacksd = FALSE,
  ...
)

Arguments

object	Model fitted through marginal standardization
parm	Not used, for compatibility
level	Confidence level, defaults to 0.95.
bootrepeats	Bootstrap repeats. Defaults to 1000. Consider increasing.
bootci	Type of bootstrap confidence interval: "bca" Default. Parametric BCa (bias-corrected accelerated) confidence intervals. "normal" Parametric normality-based confidence intervals, which require lower repeat numbers but are less accurate and may result in invalid results for ratios. "nonpar" Non-parametric BCa confidence intervals, which should be used with caution because of the risk of sparse-data bias with non-parametric bootstrapping.
jacksd	Return jackknife Monte-Carlo error for the confidence limits? Only functional with BCa confidence intervals. Defaults to FALSE.
...	Not used

Value

Matrix: First column, lower bound; second column, upper bound.

tgerke/risks documentation built on Jan. 28, 2024, 1:08 a.m.