This function estimates the rate parameter of an exponential distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.
util_exponential_aic(.x)The AIC value calculated based on the fitted exponential distribution to the provided data.
A numeric vector containing the data to be fitted to an exponential distribution.
Steven P. Sanderson II, MPH
This function calculates the Akaike Information Criterion (AIC) for an exponential distribution fitted to the provided data.
This function fits an exponential distribution to the provided data using maximum likelihood estimation. It estimates the rate parameter of the exponential distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the reciprocal of the mean of the data as the initial estimate for the rate parameter.
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_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 <- rexp(30)
util_exponential_aic(x)
Run the code above in your browser using DataLab