ROC plots for response variable classes

Implementation of the `clplots()` method

DESCRIPTION

@@ -1,33 +1,34 @@
-Package: caretExtra
-Type: Package
-Title: Extra functionality for predictions and quality control of caret models
-Version: 1.1.2
-Description: Tools for user-friendly prediction of outcome in training, 
-    cross-validation and test data, plotting the prediction results and 
-    calculation of error statistics.
-License: GPL-3
-Encoding: UTF-8
-LazyData: true
-Authors@R: 
-    person("Piotr", "Tymoszuk", , "[email protected]", 
-    role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0398-6034"))
-Imports: 
-    coxed,
-    DescTools,
-    ggrepel,
-    plotROC,
-    qgam,
-    stringi,
-    survival
-Depends: 
-    caret,
-    clustTools,
-    dplyr,
-    generics,
-    ggplot2,
-    purrr,
-    rlang,
-    stats,
-    tibble
-RoxygenNote: 7.2.3
-Roxygen: list(markdown = TRUE)
+Package: caretExtra
+Type: Package
+Title: Extra functionality for predictions and quality control of caret models
+Version: 1.1.2
+Description: Tools for user-friendly prediction of outcome in training, 
+    cross-validation and test data, plotting the prediction results and 
+    calculation of error statistics.
+License: GPL-3
+Encoding: UTF-8
+LazyData: true
+Authors@R: 
+    person("Piotr", "Tymoszuk", , "[email protected]", 
+    role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0398-6034"))
+Imports: 
+    coxed,
+    DescTools,
+    ggrepel,
+    plotROC,
+    qgam,
+    scales,
+    stringi,
+    survival
+Depends: 
+    caret,
+    clustTools,
+    dplyr,
+    generics,
+    ggplot2,
+    purrr,
+    rlang,
+    stats,
+    tibble
+RoxygenNote: 7.2.3
+Roxygen: list(markdown = TRUE)

NAMESPACE

@@ -5,6 +5,8 @@ S3method(calibration,caretx)
 S3method(calibration,predx)
 S3method(classp,caretx)
 S3method(classp,predx)
+S3method(clplots,caretx)
+S3method(clplots,predx)
 S3method(clstats,caretx)
 S3method(clstats,predx)
 S3method(components,caretx)
@@ -36,6 +38,9 @@ export(caretx)
 export(classp)
 export(classp.caretx)
 export(classp.predx)
+export(clplots)
+export(clplots.caretx)
+export(clplots.predx)
 export(clstats)
 export(clstats.caretx)
 export(clstats.predx)

R/class_stats.R

@@ -1,5 +1,7 @@
 # computation of numeric metrics of the class identification quality.
 
+# Numeric stats -------
+
 #' Class detection quality.
 #'
 #' @description
@@ -34,7 +36,7 @@
 #' receiver-operator characteristic as well as class-specific Brier scores
 #' and average class assignment probabilities.
 #' For `caretx` a list of such data frames, each one for the train, resample
-#' and train data set.
+#' and, it `newdata` is specified, the training data set.
 #'
 #' @export
 
@@ -79,4 +81,56 @@
 
   }
 
+# Class ROC plots --------
+
+#' Receiver operating characteristic plots for outcome classes.
+#'
+#' @description
+#' Draws receiver operating characteristic (ROC) plots for particular outcome
+#' classes. The ROC statistics for particular classes are obtained by comparing
+#' the given class the remaining ones (one versus rest comparison).
+#'
+#' @details
+#' The function employs internally \code{\link[caret]{multiClassSummary}} and
+#' plotting tools from the `plotROC` package. `clplots` is a S3 generic
+#' function.
+#'
+#' @param one_plot logical: should all ROC curves be displayed in one plot?
+#' @param ... extra arguments passed to \code{\link{plot_class_roc}}.
+#' @inheritParams clstats
+#'
+#' @return a single `ggplot` object or a list of `ggplot` objects.
+#' For `clplots.caretex` a list of `ggplots` with plots for the training data,
+#' resamples and, if `newdata` is specified, also for the test data set.
+#'
+#' @export clplots
+
+  clplots <- function(x, ...) UseMethod('clplots')
+
+#' @rdname clplots
+#' @export clplots.predx
+#' @export
+
+  clplots.predx <- function(x, one_plot = TRUE, ...) {
+
+    plot_class_roc(x, one_plot = one_plot, ...)
+
+  }
+
+#' @rdname clplots
+#' @export clplots.caretx
+#' @export
+
+  clplots.caretx <- function(x,
+                             newdata = NULL,
+                             one_plot = TRUE, ...) {
+
+    stopifnot(is_caretx(x))
+
+    preds <- compact(predict(x, newdata = newdata))
+
+    map(preds, clplots, one_plot = one_plot, ...)
+
+  }
+
 # END ------

R/numbers.R

@@ -72,7 +72,10 @@
 
     ## Number of observations -----
 
-    pred_data <- components(x, 'data')
+    .observation <- NULL
+
+    pred_data <- filter(components(x, 'data'),
+                        !duplicated(.observation))
 
     classes <- c('.fitted' = '.fitted',
                  '.outcome' = '.outcome')