![](../logo.png)
Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution
Source:R/utils-aic-inv-weibull.R
util_inverse_weibull_aic.Rd
This function estimates the shape and scale parameters of an inverse Weibull distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
Value
The AIC value calculated based on the fitted inverse Weibull distribution to the provided data.
Details
This function calculates the Akaike Information Criterion (AIC) for an inverse Weibull distribution fitted to the provided data.
This function fits an inverse Weibull distribution to the provided data using maximum likelihood estimation. It estimates the shape and scale parameters of the inverse Weibull 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 inverse Weibull 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_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()
Examples
# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- tidy_inverse_weibull(.n = 100, .shape = 2, .scale = 1)[["y"]]
util_inverse_weibull_aic(x)
#> [1] 217.9124