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#' Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution#'#' This function calculates the Akaike Information Criterion (AIC) for a log-normal distribution fitted to the provided data.#'#' @family Utility#' @author Steven P. Sanderson II, MPH#'#' @description#' This function estimates the meanlog and sdlog parameters of a log-normal #' 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 log-normal distribution.#'#' @details#' This function fits a log-normal distribution to the provided data using maximum #' likelihood estimation. It estimates the meanlog and sdlog parameters#' of the log-normal 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 meanlog and sdlog parameters of the log-normal #' 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 <- rlnorm(100, meanlog = 0, sdlog = 1)#' util_lognormal_aic(x)#'#' @return#' The AIC value calculated based on the fitted log-normal distribution to the provided data.#'#' @name util_lognormal_aic#'#' @export#' @rdname util_lognormal_aicutil_lognormal_aic<-function(.x) {
# Tidyevalx<- as.numeric(.x)
# Negative log-likelihood function for log-normal distributionneg_log_lik_lognormal<-function(par, data) {
meanlog<-par[1]
sdlog<-par[2]
n<- length(data)
-sum(dlnorm(data, meanlog=meanlog, sdlog=sdlog, log=TRUE))
}
# Get initial parameter estimates: method of momentsm1<- mean(log(x))
m2<- mean(log(x)^2)
meanlog_est<-m1sdlog_est<- sqrt(m2-m1^2)
# Fit log-normal distribution using optimfit_lognormal<- optim(
c(meanlog_est, sdlog_est),
neg_log_lik_lognormal,
data=x
)
# Extract log-likelihood and number of parameterslogLik_lognormal<--fit_lognormal$valuek_lognormal<-2# Number of parameters for log-normal distribution (meanlog and sdlog)# Calculate AICAIC_lognormal<-2*k_lognormal-2*logLik_lognormal# Return AICreturn(AIC_lognormal)
}
Function:
Example:
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