Merge pull request #453 from spsanderson/development

Fixes #436
spsanderson · Apr 25, 2024 · f71ba07 · f71ba07
2 parents 8a0cb6b + 215b0a5
commit f71ba07
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diff --git a/NAMESPACE b/NAMESPACE
@@ -129,6 +129,7 @@ export(util_normal_stats_tbl)
 export(util_pareto_aic)
 export(util_pareto_param_estimate)
 export(util_pareto_stats_tbl)
+export(util_poisson_aic)
 export(util_poisson_param_estimate)
 export(util_poisson_stats_tbl)
 export(util_t_stats_tbl)

diff --git a/R/utils-aic-poisson.R b/R/utils-aic-poisson.R
@@ -0,0 +1,58 @@
+#' Calculate Akaike Information Criterion (AIC) for Poisson Distribution
+#'
+#' This function calculates the Akaike Information Criterion (AIC) for a Poisson
+#' distribution fitted to the provided data.
+#'
+#' @family Utility
+#' @author Steven P. Sanderson II, MPH
+#'
+#' @description
+#' This function estimates the lambda parameter of a Poisson 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 Poisson distribution.
+#'
+#' @details
+#' This function fits a Poisson distribution to the provided data. It estimates the
+#' lambda parameter of the Poisson distribution from the data. Then, it calculates
+#' the AIC value based on the fitted distribution.
+#'
+#' Initial parameter estimates: The function uses the method of moments estimate
+#' as a starting point for the lambda parameter of the Poisson distribution.
+#'
+#' Optimization method: Since the parameter is 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 <- rpois(100, lambda = 2)
+#' util_poisson_aic(x)
+#'
+#' @return
+#' The AIC value calculated based on the fitted Poisson distribution to the provided data.
+#'
+#' @name util_poisson_aic
+NULL
+
+#' @export
+#' @rdname util_poisson_aic
+util_poisson_aic <- function(.x) {
+  # Tidyeval
+  x <- as.numeric(.x)
+
+  # Estimate lambda parameter
+  lambda <- mean(x, na.rm = TRUE)
+
+  # Calculate AIC
+  k_poisson <- 1 # Number of parameters for Poisson distribution (lambda)
+  logLik_poisson <- sum(dpois(x, lambda = lambda, log = TRUE))
+  AIC_poisson <- 2 * k_poisson - 2 * logLik_poisson
+
+  # Return AIC
+  return(AIC_poisson)
+}
diff --git a/man/check_duplicate_rows.Rd b/man/check_duplicate_rows.Rd
diff --git a/man/convert_to_ts.Rd b/man/convert_to_ts.Rd
diff --git a/man/quantile_normalize.Rd b/man/quantile_normalize.Rd
diff --git a/man/tidy_mcmc_sampling.Rd b/man/tidy_mcmc_sampling.Rd
diff --git a/man/util_beta_aic.Rd b/man/util_beta_aic.Rd
diff --git a/man/util_binomial_aic.Rd b/man/util_binomial_aic.Rd
diff --git a/man/util_cauchy_aic.Rd b/man/util_cauchy_aic.Rd
diff --git a/man/util_chisq_aic.Rd b/man/util_chisq_aic.Rd
diff --git a/man/util_exponential_aic.Rd b/man/util_exponential_aic.Rd
diff --git a/man/util_gamma_aic.Rd b/man/util_gamma_aic.Rd
diff --git a/man/util_geometric_aic.Rd b/man/util_geometric_aic.Rd
diff --git a/man/util_hypergeometric_aic.Rd b/man/util_hypergeometric_aic.Rd
diff --git a/man/util_logistic_aic.Rd b/man/util_logistic_aic.Rd
diff --git a/man/util_lognormal_aic.Rd b/man/util_lognormal_aic.Rd
diff --git a/man/util_normal_aic.Rd b/man/util_normal_aic.Rd
diff --git a/man/util_pareto_aic.Rd b/man/util_pareto_aic.Rd
diff --git a/man/util_poisson_aic.Rd b/man/util_poisson_aic.Rd
diff --git a/man/util_uniform_aic.Rd b/man/util_uniform_aic.Rd
diff --git a/man/util_weibull_aic.Rd b/man/util_weibull_aic.Rd