Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution

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

Usage

util_lognormal_aic(.x)

Arguments

.x

A numeric vector containing the data to be fitted to a log-normal distribution.

Value

The AIC value calculated based on the fitted log-normal distribution to the provided data.

Details

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

This function fits a log-normal distribution to the provided data using maximum likelihood estimation. It estimates the meanlog and sdlog parameters of the log-normal 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 meanlog and sdlog parameters of the log-normal 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.

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_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 <- rlnorm(100, meanlog = 0, sdlog = 1)
util_lognormal_aic(x)
#> [1] 286.6196