util_poisson_aic {TidyDensity}

Calculate Akaike Information Criterion (AIC) for Poisson Distribution

Description

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

Usage

util_poisson_aic(.x)

Arguments

.x

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

Details

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

This function fits a Poisson distribution to the provided data. It estimates the lambda parameter of the Poisson 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 lambda parameter of the Poisson 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 Poisson 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_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 <- rpois(100, lambda = 2)
util_poisson_aic(x)