e_vs_r {exactamente}

Compare Exact Bootstrap vs Regular Bootstrap

Description

This function runs the exact and regular bootstrap functions on a dataset, summarizes the results, and provides a comparative plot. It provides a convenient way to compare these two methods of bootstrapping.

Usage

e_vs_r(
  data,
  n_bootstraps = 10000,
  check_size = TRUE,
  anon = function(x) (mean(x)),
  lb = 0.025,
  ub = 0.975,
  density_args,
  title = "Comparison of Bootstrap Distributions"
)

Arguments

data

A numeric vector of data values to be bootstrapped.

n_bootstraps

The number of bootstrap samples to generate. Defaults to 10000.

check_size

Logical indicating if a check should be performed to ensure the dataset has less than 10 observations for the exact bootstrap. Defaults to TRUE.

anon

An anonymous function to compute the statistic of interest on each bootstrap sample. Defaults to mean.

lb

Lower bound for the confidence interval. Defaults to 0.025.

ub

Upper bound for the confidence interval. Defaults to 0.975.

density_args

Pass additional arguments to stats::density

title

Plot title

Value

A list containing two items:

• summary_table: A summary table containing the mode, median, mean, standard deviation, and confidence interval for each bootstrap method.

• comp_plot: A ggplot object comparing the bootstrap distributions.

Examples

set.seed(123)
data <- rnorm(5)
results <- e_vs_r(data)
print(results$summary_table)
print(results$comp_plot)