Calculate Akaike Information Criterion (AIC) for Binomial Distribution

This function estimates the size and probability parameters of a binomial distribution from the provided data and then calculates the AIC value based on the fitted distribution.

Usage

util_binomial_aic(.x)

Arguments

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

Value

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

Details

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

This function fits a binomial distribution to the provided data. It estimates the size and probability parameters of the binomial distribution from the data. 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 size and probability parameters of the binomial distribution.

Optimization method: Since the parameters are directly calculated from the data, no optimization is needed.

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.

Other Utility: check_duplicate_rows(), convert_to_ts(), quantile_normalize(), tidy_mcmc_sampling(), util_beta_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_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 <- rbinom(30, size = 10, prob = 0.2)
util_binomial_aic(x)
#> [1] 170.3297