confint.risks: Confidence Intervals for Model Parameters

In stopsack/risks: Estimate Risk Ratios and Risk Differences using Regression

Confidence Intervals for Model Parameters

Description

Estimates confidence intervals for the fitted risks model. For binomial models, large-sample confidence intervals, not profile likelihood, are used.

Usage

## S3 method for class 'risks'
confint(
  object,
  parm,
  level = 0.95,
  bootrepeats = 1000,
  bootci = "bca",
  jacksd = FALSE,
  ...
)

Arguments

object	A fitted risks model
parm	Not used, for compatibility
level	Optional. Confidence level. Defaults to 0.95.
bootrepeats	Optional and only applicable to approach = "margstd_boot": bootstrap repeats. Defaults to 1000. Consider increasing.
bootci	Optional and only applicable to approach = "margstd_boot": type of bootstrap confidence interval. Available methods: "bca" Default. Parametric BCa (bias-corrected accelerated) bootstrap 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	Optional and only applicable to approach = "margstd_boot": Also return jackknife estimate of Monte-Carlo error for the confidence limits? Only functional with BCa confidence intervals. Defaults to FALSE.
...	Passed on.

Value

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

Examples

confint(
  riskratio(
    formula = death ~ stage + receptor,
    data = breastcancer))

stopsack/risks documentation built on April 5, 2025, 6:02 p.m.