util_t_aic: Calculate Akaike Information Criterion (AIC) for t...

In TidyDensity: Functions for Tidy Analysis and Generation of Random Data

Calculate Akaike Information Criterion (AIC) for t Distribution

Description

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.

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.

Value

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

Author(s)

Steven P. Sanderson II, MPH

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()

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)

TidyDensity documentation built on May 29, 2024, 11:06 a.m.