R/summary.logistic_fit.R

In rumenGP: Rumen Gas Production Modeling, Comparison, and Visualization

#' Summary of Logistic Fits
#'
#' Summarizes parameter estimates and goodness-of-fit
#' statistics for a fitted Logistic model.
#'
#' The summary typically includes:
#'
#' \itemize{
#'   \item Asymptotic gas production (\code{A})
#'   \item Fractional rate constant (\code{k})
#'   \item Lag time (\code{lambda})
#'   \item Residual Sum of Squares (RSS)
#'   \item Root Mean Squared Error (RMSE)
#'   \item R-squared (R²)
#'   \item Akaike Information Criterion (AIC)
#'   \item Bayesian Information Criterion (BIC)
#' }
#'
#' The Logistic model describes gas production
#' using a sigmoidal curve characterized by:
#'
#' \itemize{
#'   \item An initial lag phase
#'   \item A rapid fermentation phase
#'   \item A plateau approaching asymptotic gas production
#' }
#'
#' The lag parameter (\code{lambda}) determines
#' the position of the sigmoid along the time axis,
#' while \code{k} controls curve steepness.
#'
#' @param object A \code{logistic_fit} object.
#'
#' @param ... Additional arguments passed to methods.
#'
#' @examples
#'
#' files <- example_data()
#'
#' raw_data <- read_ankom(
#'   files$ankom
#' )
#'
#' metadata <- read_metadata(
#'   files$metadata
#' )
#'
#' gp <- process_ankom(
#'   raw_data,
#'   metadata,
#'   headspace_ml = 210,
#'   temperature_c = 39
#' )
#'
#' fit <- fit_logistic(
#'   gp
#' )
#'
#' summary(
#'   fit
#' )
#'
#' @return A data frame containing parameter estimates
#' and model diagnostics for each fitted bottle.
#'
#' @seealso
#' \code{\link{fit_logistic}},
#' \code{\link{fit_gompertz}},
#' \code{\link{plot_fit}},
#' \code{\link{plot_residuals}},
#' \code{\link{compare_models}}
#'
#' @export
summary.logistic_fit <- function(
    object,
    ...
) {

  diagnostics <- object$diagnostics

  n_total <- nrow(diagnostics)

  n_success <- sum(
    diagnostics$Converged,
    na.rm = TRUE
  )

  n_failed <- sum(
    !diagnostics$Converged,
    na.rm = TRUE
  )

  n_low_r2 <- sum(
    diagnostics$R2 < 0.90,
    na.rm = TRUE
  )

  n_lambda_boundary <- sum(
    diagnostics$Lambda_Boundary,
    na.rm = TRUE
  )

  cat(
    "\nLogistic model summary\n",
    "----------------------\n",
    "Total bottles: ", n_total, "\n",
    "Successful fits: ", n_success, "\n",
    "Failed fits: ", n_failed, "\n",
    "Low R-squared (< 0.90): ", n_low_r2, "\n",
    "Lambda at boundary: ", n_lambda_boundary, "\n\n",
    sep = ""
  )

  invisible(object)

}