Calculate Akaike Information Criterion (AIC) for Logistic Distribution

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

Usage

util_logistic_aic(.x)

Arguments

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

Value

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

Details

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

This function fits a logistic distribution to the provided data using maximum likelihood estimation. It estimates the location and scale parameters of the logistic 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 location and scale parameters of the logistic 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_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 <- rlogis(30)
util_logistic_aic(x)
#> [1] 125.0554