This function estimates the parameters of a beta distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_beta_aic(.x)The AIC value calculated based on the fitted beta distribution to the provided data.
A numeric vector containing the data to be fitted to a beta distribution.
Steven P. Sanderson II, MPH
This function calculates the Akaike Information Criterion (AIC) for a beta distribution fitted to the provided data.
Initial parameter estimates: The choice of initial values can impact the convergence of the optimization.
Optimization method: You might explore different optimization methods within
optim for potentially better performance.
Data transformation: Depending on your data, you may need to apply
transformations (e.g., scaling to [0,1] interval) before fitting the beta
distribution.
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_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_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 <- rbeta(30, 1, 1)
util_beta_aic(x)
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