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#' Calculate Akaike Information Criterion (AIC) for Uniform Distribution#'#' This function calculates the Akaike Information Criterion (AIC) for a uniform #' distribution fitted to the provided data.#'#' @family Utility#' @author Steven P. Sanderson II, MPH#'#' @description#' This function estimates the min and max parameters of a uniform distribution #' from the provided data and then calculates the AIC value based on the fitted #' distribution.#'#' @param .x A numeric vector containing the data to be fitted to a uniform distribution.#'#' @details#' This function fits a uniform distribution to the provided data. It estimates #' the min and max parameters of the uniform distribution from the range of the data.#' Then, it calculates the AIC value based on the fitted distribution.#' #' Initial parameter estimates: The function uses the minimum and maximum values #' of the data as starting points for the min and max parameters of the uniform #' distribution.#' #' Optimization method: Since the parameters are directly calculated from the #' data, no optimization is needed.#' #' 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 <- runif(30)#' util_uniform_aic(x)#'#' @return#' The AIC value calculated based on the fitted uniform distribution to the provided data.#'#' @name util_uniform_aicNULL#' @export#' @rdname util_uniform_aicutil_uniform_aic<-function(.x) {
# Tidyevalx<- as.numeric(.x)
# Estimate min and max parametersmin_val<- min(x)
max_val<- max(x)
# Calculate AICk_uniform<-2# Number of parameters for uniform distribution (min and max)logLik_uniform<--length(x) * log(max_val-min_val)
AIC_uniform<-2*k_uniform-2*logLik_uniform# Return AICreturn(AIC_uniform)
}
Function:
Example:
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