You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
#' Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution#'#' This function calculates the Akaike Information Criterion (AIC) for a #' hypergeometric distribution fitted to the provided data.#'#' @family Utility#' @author Steven P. Sanderson II, MPH#'#' @description#' This function estimates the parameters m, n, and k of a hypergeometric 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 hypergeometric #' distribution.#'#' @details#' This function fits a hypergeometric distribution to the provided data. It #' estimates the parameters m, n, and k of the hypergeometric distribution from #' the data. Then, it calculates the AIC value based on the fitted distribution.#' #' Initial parameter estimates: The function does not estimate parameters; they #' are directly calculated from the data.#' #' 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 <- rhyper(100, m = 10, n = 10, k = 5)#' util_hypergeometric_aic(x)#'#' @return#' The AIC value calculated based on the fitted hypergeometric distribution to the provided data.#'#' @name util_hypergeometric_aicNULL#' @export#' @rdname util_hypergeometric_aicutil_hypergeometric_aic<-function(.x) {
# Tidyevalx<- as.numeric(.x)
# Estimate parameters m, n, and kN<- length(x)
k<- max(x)
n<-Nm<-N-k# Calculate AICk_hypergeometric<-3# Number of parameters for hypergeometric distribution (m, n, and k)logLik_hypergeometric<- sum(dhyper(x, m=m, n=n, k=k, log=TRUE))
AIC_hypergeometric<-2*k_hypergeometric-2*logLik_hypergeometric# Return AICreturn(AIC_hypergeometric)
}
Function:
Example:
The text was updated successfully, but these errors were encountered: