confint.clusterBootstrap: Confidence intervals for clusterBootstrap objects

Description

Computes confidence intervals for estimates obtained via clustered bootstrap resampling. Supported interval types are: percentile (default), normal approximation (parametric), and bias-corrected (BC).

Usage

# S3 method for clusterBootstrap
confint(
  object,
  parm = NULL,
  level = 0.95,
  type = c("percentile", "parametric", "bc"),
  ...
)

Value

A data.frame with one row per parameter and the following columns:

term: The name of the parameter.
type: The type of confidence interval used.
conf.low: The lower bound of the confidence interval.
conf.high: The upper bound of the confidence interval.

Arguments

object: An object of class "clusterBootstrap", as returned by clusterBootstrap.
parm: A character vector of parameter names to compute confidence intervals for. If NULL (default), intervals are computed for all parameters.
level: Confidence level, e.g., 0.95 for a 95% confidence interval.
type: Type of confidence interval. One of "percentile", "parametric", or "bc". The default is "percentile".
...: Currently ignored. Included for method compatibility.

Details

Percentile: uses the empirical quantiles of the bootstrap estimates.
Parametric: uses the bootstrap standard error and assumes normality.
Bias-corrected (BC): adjusts for bias in the bootstrap distribution. Note: acceleration (BCa) is not implemented.

Examples

Run this code

if (FALSE) {
set.seed(2025)
n_school  <- 30
n_class   <- 8
n_student <- 15

demo <- expand.grid(school  = paste0("S", 1:n_school),
                    class   = paste0("C", 1:n_class),
                    student = paste0("P", 1:n_student)) |>
  mutate(score1 = rnorm(n()),
  score2 = rnorm(n())) |>
  arrange(school, class, student) |>
  slice(1:(n() - 3)) # slightly unbalanced data
bootFun2 <- function(d) lm(score1 ~ score2, data = d)$coef
clusterBootstrap(df       = demo, 
                 clusters = c("school", "class", "student"),
                 replace  = c(TRUE, FALSE, TRUE),
                 stat_fun = bootFun2,
                 B        = 1000) |>
  confint()
}