util_generalized_beta_aic: Calculate Akaike Information Criterion (AIC) for Generalized...

In TidyDensity: Functions for Tidy Analysis and Generation of Random Data

Calculate Akaike Information Criterion (AIC) for Generalized Beta Distribution

Description

This function estimates the shape1, shape2, shape3, and rate parameters of a generalized Beta distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.

Usage

util_generalized_beta_aic(.x)

Arguments

.x	A numeric vector containing the data to be fitted to a generalized Beta distribution.

Details

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

This function fits a generalized Beta distribution to the provided data using maximum likelihood estimation. It estimates the shape1, shape2, shape3, and rate parameters of the generalized Beta distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.

Initial parameter estimates: The function uses reasonable initial estimates for the shape1, shape2, shape3, and rate parameters of the generalized Beta distribution.

Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.

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 generalized Beta 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_f_aic(), util_gamma_aic(), util_generalized_pareto_aic(), util_geometric_aic(), util_hypergeometric_aic(), util_inverse_burr_aic(), util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), util_negative_binomial_aic(), util_normal_aic(), util_paralogistic_aic(), util_pareto1_aic(), util_pareto_aic(), util_poisson_aic(), util_t_aic(), util_triangular_aic(), util_uniform_aic(), util_weibull_aic(), util_zero_truncated_binomial_aic(), util_zero_truncated_geometric_aic(), util_zero_truncated_negative_binomial_aic(), util_zero_truncated_poisson_aic()

Examples

# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- tidy_generalized_beta(100, .shape1 = 2, .shape2 = 3,
                          .shape3 = 4, .rate = 5)[["y"]]
util_generalized_beta_aic(x)

TidyDensity documentation built on May 29, 2024, 11:06 a.m.