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#' Calculate Akaike Information Criterion (AIC) for Weibull Distribution#'#' This function calculates the Akaike Information Criterion (AIC) for a Weibull #' distribution fitted to the provided data.#'#' @family Utility#' @author Steven P. Sanderson II, MPH#'#' @description#' This function estimates the shape and scale parameters of a Weibull distribution #' from the provided data using maximum likelihood estimation,#' and then calculates the AIC value based on the fitted distribution.#'#' @param .x A numeric vector containing the data to be fitted to a Weibull distribution.#'#' @details#' This function fits a Weibull distribution to the provided data using maximum #' likelihood estimation. It estimates the shape and scale parameters#' of the 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 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.#'#' @examples#' # Example 1: Calculate AIC for a sample dataset#' set.seed(123)#' x <- rweibull(100, shape = 2, scale = 1)#' util_weibull_aic(x)#'#' @return#' The AIC value calculated based on the fitted Weibull distribution to the provided data.#'#' @name util_weibull_aic#'#' @export#' @rdname util_weibull_aicutil_weibull_aic<-function(.x) {
# Tidyevalx<- as.numeric(.x)
# Negative log-likelihood function for Weibull distributionneg_log_lik_weibull<-function(par, data) {
shape<-par[1]
scale<-par[2]
n<- length(data)
-sum(dweibull(data, shape=shape, scale=scale, log=TRUE))
}
# Get initial parameter estimates: method of momentspe<-TidyDensity::util_weibull_param_estimate(x)$parameter_tbl# Fit Weibull distribution using optimfit_weibull<- optim(
c(pe$shape, pe$scale),
neg_log_lik_weibull,
data=x
)
# Extract log-likelihood and number of parameterslogLik_weibull<--fit_weibull$valuek_weibull<-2# Number of parameters for Weibull distribution (shape and scale)# Calculate AICAIC_weibull<-2*k_weibull-2*logLik_weibull# Return AICreturn(AIC_weibull)
}
Function:
Example:
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