util_normal_aic: Calculate Akaike Information Criterion (AIC) for Normal...

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

Calculate Akaike Information Criterion (AIC) for Normal Distribution

Description

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

Usage

util_normal_aic(.x)

Arguments

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

Details

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

Value

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

Author(s)

Steven P. Sanderson II, MPH

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_lognormal_aic(), util_negative_binomial_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()

Examples

# Example 1: Calculate AIC for a sample dataset
set.seed(123)
data <- rnorm(30)
util_normal_aic(data)

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