Calculate Akaike Information Criterion (AIC) for Uniform Distribution

This function estimates the min and max parameters of a uniform distribution from the provided data and then calculates the AIC value based on the fitted distribution.

Usage

util_uniform_aic(.x)

Arguments

.x

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

Value

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

Details

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

This function fits a uniform distribution to the provided data. It estimates the min and max parameters of the uniform distribution from the range of the data. Then, it calculates the AIC value based on the fitted distribution.

Initial parameter estimates: The function uses the minimum and maximum values of the data as starting points for the min and max parameters of the uniform distribution.

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.

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_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_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 <- runif(30)
util_uniform_aic(x)
#> [1] 1.061835