Calculate Akaike Information Criterion (AIC) for Weibull Distribution

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

Usage

util_weibull_aic(.x)

Arguments

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

Value

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

Details

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

This function fits a Weibull distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the Weibull distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.

Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape and scale parameters of the Weibull 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.

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_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_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 <- rweibull(100, shape = 2, scale = 1)
util_weibull_aic(x)
#> [1] 119.1065