This function estimates the shape1, shape2, and rate parameters of a generalized Pareto distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_generalized_pareto_aic(.x)The AIC value calculated based on the fitted generalized Pareto distribution to the provided data.
A numeric vector containing the data to be fitted to a generalized Pareto distribution.
Steven P. Sanderson II, MPH
This function calculates the Akaike Information Criterion (AIC) for a generalized Pareto distribution fitted to the provided data.
This function fits a generalized Pareto distribution to the provided data using maximum likelihood estimation. It estimates the shape1, shape2, and rate parameters of the generalized Pareto distribution using maximum likelihood estimation. 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 shape1, shape2, and rate parameters of the generalized Pareto distribution.
Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.
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_binomial_aic(),
util_cauchy_aic(),
util_chisq_aic(),
util_exponential_aic(),
util_f_aic(),
util_gamma_aic(),
util_generalized_beta_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()
# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- actuar::rgenpareto(100, shape1 = 1, shape2 = 2, scale = 3)
util_generalized_pareto_aic(x)
Run the code above in your browser using DataLab