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Calculate Akaike Information Criterion (AIC) for Pareto Distribution
Source:R/utis-aic-pareto1.R
util_pareto1_aic.Rd
This function estimates the shape and scale parameters of a Pareto distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
Details
This function calculates the Akaike Information Criterion (AIC) for a Pareto distribution fitted to the provided data.
This function fits a Pareto distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the 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 shape and scale parameters of the 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.
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_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()
Other Pareto:
tidy_generalized_pareto()
,
tidy_inverse_pareto()
,
tidy_pareto()
,
tidy_pareto1()
,
util_pareto1_param_estimate()
,
util_pareto1_stats_tbl()
,
util_pareto_param_estimate()
,
util_pareto_stats_tbl()
Examples
# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- tidy_pareto1()$y
util_pareto1_aic(x)
#> [1] 185.0364