Calculate Akaike Information Criterion (AIC) for F Distribution

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

Usage

util_f_aic(.x)

Arguments

.x

A numeric vector containing the data to be fitted to an F distribution.

Value

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

Details

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

This function fits an F distribution to the input data using maximum likelihood estimation and then computes the Akaike Information Criterion (AIC) based on the fitted distribution.

See also

rf for generating F-distributed data, optim for optimization.

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_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()

Author

Steven P. Sanderson II, MPH

Examples

# Generate F-distributed data
set.seed(123)
x <- rf(100, df1 = 5, df2 = 10, ncp = 1)

# Calculate AIC for the generated data
util_f_aic(x)
#> [1] 250.9574