R/MAE.R

In skyee1/SurvMetrics: Predictive Evaluation Metrics in Survival Analysis

#' The Mean Absolute Error
#'
#' A somewhat naive criterion that is sometimes used consists of simply omitting all censored cases from the data set.
#' For survival analysis problems, the mean absolute error (MAE) can be defined as an average of the differences between the predicted time values and the actual observation time values.
#' Only the samples for which the event occurs are being considered in this metric.
#'
#' Condition: MAE can only be used for the evaluation of survival models which can provide the event time as the predicted target value.
#'
#' @param object object of class \code{Surv} created by Surv function.
#' @param pre_time a vector of predicted values of survival time of each observation.
#'
#' @return the value of the Mean Absolute Error between the survival time and the predicted result.
#'
#' @author Hanpu Zhou \email{zhouhanpu@csu.edu.cn}
#' @references
#' Matsuo, K. ,  Purushotham, S. ,  Jiang, B. ,  Mandelbaum, R. S. ,  Takiuchi, T. , &  Liu, Y. , et al. (2018). Survival outcome prediction in cervical cancer: cox models vs deep-learning model. American Journal of Obstetrics & Gynecology.
#' Coyle, E. J. , &  Lin, J. H. . (1988). Stack filters and the mean absolute error criterion. IEEE Trans Acoustics Speech Signal Processing, 36(8), 1244-1254.
#' @examples
#' library(survival)
#' time <- rexp(50)
#' status <- sample(c(0, 1), 50, replace = TRUE)
#' pre_time <- rexp(50)
#' MAE(Surv(time, status), pre_time)
#'
#' @importFrom survival Surv
#'
#' @export
MAE <- function(object, pre_time) {
  if (any(is.na(object)) | any(is.na(pre_time))) {
    stop("The input vector cannot have NA")
  }

  time <- object[, 1]
  status <- object[, 2]

  if (length(time) != length(status)) {
    stop("The lengths of time and status are not equal")
  }

  if (length(time) != length(pre_time)) {
    stop("The lengths of time and pre_time are not equal")
  }

  t_mae <- abs(time - pre_time) * status
  mae <- sum(t_mae) / sum(status)
  names(mae) <- "MAE"
  return(round(mae, 4))
}