util_zero_truncated_geometric_aic {TidyDensity}

Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution

Description

This function estimates the probability parameter of a Zero-Truncated Geometric distribution from the provided data and calculates the AIC value based on the fitted distribution.

Usage

util_zero_truncated_geometric_aic(.x)

Arguments

.x

A numeric vector containing the data to be fitted to a Zero-Truncated Geometric distribution.

Details

This function calculates the Akaike Information Criterion (AIC) for a Zero-Truncated Geometric distribution fitted to the provided data.

This function fits a Zero-Truncated Geometric distribution to the provided data. It estimates the probability parameter using the method of moments and calculates the AIC value.

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 Zero-Truncated 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_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_negative_binomial_aic(), util_zero_truncated_poisson_aic()

Examples

library(actuar)

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