Merge pull request #446 from spsanderson/development

Fixes #429

NAMESPACE

@@ -115,6 +115,7 @@ export(util_hypergeometric_stats_tbl)
 export(util_logistic_aic)
 export(util_logistic_param_estimate)
 export(util_logistic_stats_tbl)
+export(util_lognormal_aic)
 export(util_lognormal_param_estimate)
 export(util_lognormal_stats_tbl)
 export(util_negative_binomial_param_estimate)

R/utils-aic-lognormal.R

@@ -0,0 +1,80 @@
+#' 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_aic
+util_lognormal_aic <- function(.x) {
+  # Tidyeval
+  x <- as.numeric(.x)
+
+  # Negative log-likelihood function for log-normal distribution
+  neg_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 moments
+  m1 <- mean(log(x))
+  m2 <- mean(log(x)^2)
+  meanlog_est <- m1
+  sdlog_est <- sqrt(m2 - m1^2)
+
+  # Fit log-normal distribution using optim
+  fit_lognormal <- optim(
+    c(meanlog_est, sdlog_est),
+    neg_log_lik_lognormal,
+    data = x
+  )
+
+  # Extract log-likelihood and number of parameters
+  logLik_lognormal <- -fit_lognormal$value
+  k_lognormal <- 2 # Number of parameters for log-normal distribution (meanlog and sdlog)
+
+  # Calculate AIC
+  AIC_lognormal <- 2 * k_lognormal - 2 * logLik_lognormal
+
+  # Return AIC
+  return(AIC_lognormal)
+}