calc_coverage: Calculate Coverage Probability and its Monte Carlo Standard...

In mcstatsim: Monte Carlo Statistical Simulation Tools Using a Functional Approach

Calculate Coverage Probability and its Monte Carlo Standard Error

Description

Computes the coverage probability of a confidence interval, defined as the proportion of times the true parameter value falls within the calculated lower and upper bounds across a set of simulations. Additionally, calculates the Monte Carlo Standard Error (MCSE) of the coverage probability to assess the uncertainty associated with this coverage estimate. This function is useful for evaluating the accuracy and reliability of confidence intervals generated by statistical models or estimation procedures.

Usage

calc_coverage(lower_bound, upper_bound, true_param)

Arguments

lower_bound	A numeric vector of lower bounds of confidence intervals.
upper_bound	A numeric vector of upper bounds of confidence intervals, corresponding to 'lower_bound'.
true_param	The true parameter value that the confidence intervals are intended to estimate.

Value

A list with two components: 'coverage', the calculated coverage probability of the confidence intervals, and 'coverage_mcse', the Monte Carlo Standard Error of the coverage. This MCSE provides a measure of the precision of the coverage probability estimate.

Examples

set.seed(123) # For reproducibility
estimates <- rnorm(100, mean = 50, sd = 10)
ci_lower <- estimates - 1.96 * 10
ci_upper <- estimates + 1.96 * 10
true_param <- 50
coverage_info <- calc_coverage(ci_lower, ci_upper, true_param)
print(coverage_info)

mcstatsim documentation built on Sept. 11, 2024, 5:35 p.m.