Calculate Akaike Information Criterion (AIC) for Beta Distribution

This function estimates the parameters of a beta distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.

Usage

util_beta_aic(.x)

Arguments

.x: A numeric vector containing the data to be fitted to a beta distribution.

Value

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

Details

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

Initial parameter estimates: The choice of initial values can impact the convergence of the optimization.

Optimization method: You might explore different optimization methods within optim for potentially better performance. Data transformation: Depending on your data, you may need to apply transformations (e.g., scaling to [0,1] interval) before fitting the beta distribution.

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.

Other Utility: check_duplicate_rows(), convert_to_ts(), quantile_normalize(), tidy_mcmc_sampling(), util_binomial_aic(), util_cauchy_aic(), util_chisq_aic(), util_exponential_aic(), util_f_aic(), util_gamma_aic(), util_generalized_beta_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()

Author

Steven P. Sanderson II, MPH

Examples

# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- rbeta(30, 1, 1)
util_beta_aic(x)
#> There was no need to scale the data.
#> [1] 5.691712