util_hypergeometric_aic {TidyDensity}

Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution

Description

This function estimates the parameters m, n, and k of a hypergeometric distribution from the provided data and then calculates the AIC value based on the fitted distribution.

Usage

util_hypergeometric_aic(.x)

Arguments

.x

A numeric vector containing the data to be fitted to a hypergeometric distribution.

Details

This function calculates the Akaike Information Criterion (AIC) for a hypergeometric distribution fitted to the provided data.

This function fits a hypergeometric distribution to the provided data. It estimates the parameters m, n, and k of the hypergeometric distribution from the data. Then, it calculates the AIC value based on the fitted distribution.

Initial parameter estimates: The function does not estimate parameters; they are directly calculated from the data.

Optimization method: Since the parameters are directly calculated from the data, no optimization is needed.

Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.

Value

The AIC value calculated based on the fitted hypergeometric distribution to the provided data.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Utility: check_duplicate_rows(), convert_to_ts(), quantile_normalize(), tidy_mcmc_sampling(), util_beta_aic(), util_binomial_aic(), util_cauchy_aic(), util_chisq_aic(), util_exponential_aic(), util_gamma_aic(), util_geometric_aic(), util_logistic_aic(), util_lognormal_aic(), util_normal_aic(), util_pareto_aic(), util_poisson_aic(), util_uniform_aic(), util_weibull_aic()

Examples

# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- rhyper(100, m = 10, n = 10, k = 5)
util_hypergeometric_aic(x)