Update performance_roc.R

R/performance_roc.R

@@ -4,6 +4,15 @@
 #' @description This function calculates a simple ROC curves of x/y coordinates
 #' based on response and predictions of a binomial model.
 #'
+#' It returns the area under the curve (AUC) as a percentage, which corresponds
+#' to the probability that a randomly chosen observation of "condition 1" is correctly
+#' classified by the model as having a higher probability of being "condition 1" than
+#' a randomly chosen "condition 2" observation.
+#'
+#' Applying `as.data.frame()` to the ouput returns a data frame containing the following:
+#' - `Sensitivity` (that actually corresponds to `1 - Specificity`): It is the False Positive Rate.
+#' - `Sensitivity`: It is the True Positive Rate, which is the proportion of correctly classified "condition 1" observations.
+#'
 #' @param x A numeric vector, representing the outcome (0/1), or a model with
 #'   binomial outcome.
 #' @param predictions If `x` is numeric, a numeric vector of same length