util_beta_aic {TidyDensity}

Calculate Akaike Information Criterion (AIC) for Beta Distribution

Description

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.

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.

Value

The AIC value calculated based on the fitted 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_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()

Examples

# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- rbeta(30, 1, 1)
util_beta_aic(x)