Merge pull request #445 from spsanderson/development

Fixes #428

NAMESPACE

@@ -112,6 +112,7 @@ export(util_geometric_param_estimate)
 export(util_geometric_stats_tbl)
 export(util_hypergeometric_param_estimate)
 export(util_hypergeometric_stats_tbl)
+export(util_logistic_aic)
 export(util_logistic_param_estimate)
 export(util_logistic_stats_tbl)
 export(util_lognormal_param_estimate)

R/utils-aic-logistic.R

@@ -0,0 +1,79 @@
+#' Calculate Akaike Information Criterion (AIC) for Logistic Distribution
+#'
+#' This function calculates the Akaike Information Criterion (AIC) for a logistic distribution fitted to the provided data.
+#'
+#' @family Utility
+#' @author Steven P. Sanderson II, MPH
+#'
+#' @description
+#' This function estimates the location and scale parameters of a logistic
+#' 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 logistic distribution.
+#'
+#' @details
+#' This function fits a logistic distribution to the provided data using maximum
+#' likelihood estimation. It estimates the location and scale parameters
+#' of the logistic 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 location and scale parameters of the logistic
+#' 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 <- rlogis(30)
+#' util_logistic_aic(data)
+#'
+#' @return
+#' The AIC value calculated based on the fitted logistic distribution to the provided data.
+#'
+#' @name util_logistic_aic
+NULL
+
+#' @export
+#' @rdname util_logistic_aic
+util_logistic_aic <- function(.x) {
+  # Tidyeval
+  x <- as.numeric(.x)
+
+  # Negative log-likelihood function for logistic distribution
+  neg_log_lik_logistic <- function(par, data) {
+    location <- par[1]
+    scale <- par[2]
+    n <- length(data)
+    -sum(dlogis(data, location = location, scale = scale, log = TRUE))
+  }
+
+  # Get initial parameter estimates: method of moments
+  pe <- TidyDensity::util_logistic_param_estimate(x)$parameter_tbl |>
+    subset(method == "EnvStats_MLE")
+
+  # Fit logistic distribution using optim
+  fit_logistic <- optim(
+    c(pe$location, pe$scale),
+    neg_log_lik_logistic,
+    data = x
+  )
+
+  # Extract log-likelihood and number of parameters
+  logLik_logistic <- -fit_logistic$value
+  k_logistic <- 2 # Number of parameters for logistic distribution (location and scale)
+
+  # Calculate AIC
+  AIC_logistic <- 2 * k_logistic - 2 * logLik_logistic
+
+  # Return AIC
+  return(AIC_logistic)
+}