add and use metric_range_chr()

Author: EmilHvitfeldt

R/aaa-new.R

@@ -155,6 +155,10 @@ metric_direction <- function(x) {
 metric_range <- function(x) {
   attr(x, "range", exact = TRUE)
 }
+metric_range_chr <- function(x, i) {
+  val <- metric_range(x)[[i]]
+  if (is.infinite(val)) paste0(if (val < 0) "-", "Inf") else as.character(val)
+}
 `metric_range<-` <- function(x, value) {
   attr(x, "range") <- value
   x

R/class-accuracy.R

@@ -25,8 +25,8 @@
 #' \deqn{\text{Accuracy} = \frac{A + D}{A + B + C + D}}
 #'
 #' Accuracy is a metric that should be `r attr(accuracy, "direction")`d. The
-#' output ranges from `r metric_range(accuracy)[1]` to
-#' `r metric_range(accuracy)[2]`, with `r metric_optimal(accuracy)` indicating
+#' output ranges from `r metric_range_chr(accuracy, 1)` to
+#' `r metric_range_chr(accuracy, 2)`, with `r metric_optimal(accuracy)` indicating
 #' perfect predictions.
 #'
 #' @author Max Kuhn

R/class-bal_accuracy.R

@@ -27,7 +27,7 @@
 #'
 #' Balanced accuracy is a metric that should be
 #' `r attr(bal_accuracy, "direction")`d. The output ranges from
-#' `r metric_range(bal_accuracy)[1]` to `r metric_range(bal_accuracy)[2]`, with
+#' `r metric_range_chr(bal_accuracy, 1)` to `r metric_range_chr(bal_accuracy, 2)`, with
 #' `r metric_optimal(bal_accuracy)` indicating perfect predictions.
 #'
 #' @author Max Kuhn

R/class-detection_prevalence.R

@@ -24,7 +24,7 @@
 #'
 #' Detection prevalence is a metric that should be
 #' `r attr(detection_prevalence, "direction")`d. The output ranges from
-#' `r metric_range(detection_prevalence)[1]` to `r metric_range(detection_prevalence)[2]`.
+#' `r metric_range_chr(detection_prevalence, 1)` to `r metric_range_chr(detection_prevalence, 2)`.
 #' The "optimal" value depends on the true prevalence of positive events in the data.
 #'
 #' @author Max Kuhn

R/class-f_meas.R

@@ -36,8 +36,8 @@
 #' \deqn{F_{meas} = \frac{(1 + \beta^2) \cdot \text{Precision} \cdot \text{Recall}}{(\beta^2 \cdot \text{Precision}) + \text{Recall}}}
 #'
 #' F measure is a metric that should be `r attr(f_meas, "direction")`d. The
-#' output ranges from `r metric_range(f_meas)[1]` to
-#' `r metric_range(f_meas)[2]`, with `r metric_optimal(f_meas)` indicating
+#' output ranges from `r metric_range_chr(f_meas, 1)` to
+#' `r metric_range_chr(f_meas, 2)`, with `r metric_optimal(f_meas)` indicating
 #' perfect precision and recall.
 #'
 #' @references

R/class-fall_out.R

@@ -35,8 +35,8 @@
 #' \deqn{\text{Fall-out} = \frac{B}{B + D}}
 #'
 #' Fall-out is a metric that should be `r attr(fall_out, "direction")`d. The
-#' output ranges from `r metric_range(fall_out)[1]` to
-#' `r metric_range(fall_out)[2]`, with `r metric_optimal(fall_out)` indicating
+#' output ranges from `r metric_range_chr(fall_out, 1)` to
+#' `r metric_range_chr(fall_out, 2)`, with `r metric_optimal(fall_out)` indicating
 #' that all actual negatives were correctly predicted as negative (no false
 #' positives).
 #'

R/class-j_index.R

@@ -22,8 +22,8 @@
 #' \deqn{\text{J-index} = \text{Sensitivity} + \text{Specificity} - 1}
 #'
 #' J-index is a metric that should be `r attr(j_index, "direction")`d. The
-#' output ranges from `r metric_range(j_index)[1]` to
-#' `r metric_range(j_index)[2]`, with `r metric_optimal(j_index)` indicating no
+#' output ranges from `r metric_range_chr(j_index, 1)` to
+#' `r metric_range_chr(j_index, 2)`, with `r metric_optimal(j_index)` indicating no
 #' false positives and no false negatives.
 #'
 #' The binary version of J-index is equivalent to the binary concept of

R/class-kap.R

@@ -44,7 +44,7 @@
 #' expected agreement by chance.
 #'
 #' Kappa is a metric that should be `r attr(kap, "direction")`d. The output
-#' ranges from `r metric_range(kap)[1]` to `r metric_range(kap)[2]`, with
+#' ranges from `r metric_range_chr(kap, 1)` to `r metric_range_chr(kap, 2)`, with
 #' `r metric_optimal(kap)` indicating perfect agreement. Negative values
 #' indicate agreement worse than chance.
 #'

R/class-markedness.R

@@ -24,8 +24,8 @@
 #' \deqn{\text{Markedness} = \text{Precision} + \text{Inverse Precision} - 1}
 #'
 #' Markedness is a metric that should be `r attr(markedness, "direction")`d. The
-#' output ranges from `r metric_range(markedness)[1]` to
-#' `r metric_range(markedness)[2]`, with `r metric_optimal(markedness)` indicating
+#' output ranges from `r metric_range_chr(markedness, 1)` to
+#' `r metric_range_chr(markedness, 2)`, with `r metric_optimal(markedness)` indicating
 #' perfect predictions.
 #'
 #' Markedness is to the predicted condition (precision and inverse precision)

R/class-mcc.R

@@ -25,7 +25,7 @@
 #' \deqn{\text{MCC} = \frac{(A \cdot D) - (B \cdot C)}{\sqrt{(A + B)(A + C)(D + B)(D + C)}}}
 #'
 #' MCC is a metric that should be `r attr(mcc, "direction")`d. The output
-#' ranges from `r metric_range(mcc)[1]` to `r metric_range(mcc)[2]`, with
+#' ranges from `r metric_range_chr(mcc, 1)` to `r metric_range_chr(mcc, 2)`, with
 #' `r metric_optimal(mcc)` indicating perfect predictions. A value of 0
 #' indicates no better than random prediction, and negative values indicate
 #' inverse prediction.