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#' Calculate Akaike Information Criterion (AIC) for Gamma Distribution#'#' This function calculates the Akaike Information Criterion (AIC) for a gamma #' 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 gamma 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 gamma distribution.#'#' @details#' This function fits a gamma distribution to the provided data using maximum #' likelihood estimation. It estimates the shape and scale parameters of the #' gamma 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 #' gamma 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 <- rgamma(30, shape = 1)#' util_gamma_aic(x)#'#' @return#' The AIC value calculated based on the fitted gamma distribution to the provided data.#'#' @name util_gamma_aicNULL#' @export#' @rdname util_gamma_aicutil_gamma_aic<-function(.x) {
# Tidyevalx<- as.numeric(.x)
# Negative log-likelihood function for gamma distributionneg_log_lik_gamma<-function(par, data) {
shape<-par[1]
scale<-par[2]
n<- length(data)
-sum(dgamma(data, shape=shape, scale=scale, log=TRUE))
}
# Get initial parameter estimates: method of momentspe<-TidyDensity::util_gamma_param_estimate(x)$parameter_tbl|>
subset(method=="EnvStats_MMUE")
# Fit gamma distribution using optimfit_gamma<- optim(
c(pe$shape, pe$scale),
neg_log_lik_gamma,
data=x
)
# Extract log-likelihood and number of parameterslogLik_gamma<--fit_gamma$valuek_gamma<-2# Number of parameters for gamma distribution (shape and scale)# Calculate AICAIC_gamma<-2*k_gamma-2*logLik_gamma# Return AICreturn(AIC_gamma)
}
Function:
Example:
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