Calculate Akaike Information Criterion (AIC) for t Distribution

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

Usage

util_t_aic(.x)

Arguments

.x

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

Value

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

Details

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

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

See also

rt for generating t-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_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_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 t-distributed data
set.seed(123)
x <- rt(100, df = 5, ncp = 0.5)

# Calculate AIC for the generated data
util_t_aic(x)
#> [1] 305.77