util_geometric_aic {TidyDensity}

Calculate Akaike Information Criterion (AIC) for Geometric Distribution

Description

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

Usage

util_geometric_aic(.x)

Arguments

.x

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

Details

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

This function fits a geometric distribution to the provided data. It estimates the probability parameter of the geometric 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 estimate as a starting point for the probability parameter of the geometric distribution.

Optimization method: Since the parameter is 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.

Value

The AIC value calculated based on the fitted geometric 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_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()

Examples

# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- rgeom(100, prob = 0.2)
util_geometric_aic(x)