calc_rejection_rate: Calculate Rejection Rate and its Monte Carlo Standard Error

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

Calculate Rejection Rate and its Monte Carlo Standard Error

Description

Computes the rejection rate of hypotheses tests based on a vector of p-values and a specified significance level (alpha). The rejection rate is the proportion of p-values that are lower than alpha, indicating significant results. Additionally, the function calculates the Monte Carlo Standard Error (MCSE) for the rejection rate, which quantifies the uncertainty associated with the estimated rejection rate. This function is useful for assessing the overall type I error rate or the power of a statistical test across multiple simulations or experimental replications.

Usage

calc_rejection_rate(p_values, alpha = 0.05)

Arguments

p_values	A numeric vector of p-values from multiple hypothesis tests.
alpha	The significance level used to determine if a p-value indicates a significant result. Default is 0.05.

Value

A list with two components: 'rejection_rate', the proportion of tests that resulted in rejection of the null hypothesis, and 'rejection_rate_mcse', the Monte Carlo Standard Error of the rejection rate, providing an estimate of its variability.

Examples

set.seed(123) # For reproducibility
p_values <- runif(100, min = 0, max = 1) # Simulated p-values
rejection_info <- calc_rejection_rate(p_values)
print(rejection_info)

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