Merge pull request #443 from spsanderson/development

Fixes #426

NAMESPACE

@@ -101,6 +101,7 @@ export(util_cauchy_stats_tbl)
 export(util_chisq_aic)
 export(util_chisquare_param_estimate)
 export(util_chisquare_stats_tbl)
+export(util_exponential_aic)
 export(util_exponential_param_estimate)
 export(util_exponential_stats_tbl)
 export(util_f_stats_tbl)

R/utils-aic-exponential.R

@@ -0,0 +1,73 @@
+#' Calculate Akaike Information Criterion (AIC) for Exponential Distribution
+#'
+#' This function calculates the Akaike Information Criterion (AIC) for an exponential distribution fitted to the provided data.
+#'
+#' @family Utility
+#' @author Steven P. Sanderson II, MPH
+#'
+#' @description
+#' This function estimates the rate parameter of an exponential 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 an exponential distribution.
+#'
+#' @details
+#' This function fits an exponential distribution to the provided data using maximum likelihood estimation. It estimates the rate parameter
+#' of the exponential distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.
+#'
+#' Initial parameter estimates: The function uses the reciprocal of the mean of the data as the initial estimate for the rate parameter.
+#'
+#' 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 <- rexp(30)
+#' util_exponential_aic(x)
+#'
+#' @return
+#' The AIC value calculated based on the fitted exponential distribution to the provided data.
+#'
+#' @name util_exponential_aic
+NULL
+
+#' @export
+#' @rdname util_exponential_aic
+util_exponential_aic <- function(.x) {
+  # Tidyeval
+  x <- as.numeric(.x)
+
+  # Negative log-likelihood function for exponential distribution
+  neg_log_lik_exponential <- function(par, data) {
+    rate <- par[1]
+    n <- length(data)
+    -sum(dexp(data, rate = rate, log = TRUE))
+  }
+
+  # Get initial parameter estimate: reciprocal of the mean of the data
+  pe <- TidyDensity::util_exponential_param_estimate(x)$parameter_tbl
+
+  # Fit exponential distribution using optim
+  fit_exponential <- optim(
+    pe$rate,
+    neg_log_lik_exponential,
+    data = x,
+    method = "Brent",
+    lower = 0.0001,
+    upper = 1000
+  )
+
+  # Extract log-likelihood and number of parameters
+  logLik_exponential <- -fit_exponential$value
+  k_exponential <- 1 # Number of parameters for exponential distribution (rate)
+
+  # Calculate AIC
+  AIC_exponential <- 2 * k_exponential - 2 * logLik_exponential
+
+  # Return AIC
+  return(AIC_exponential)
+}