add examples for scoring rules

R/MeasureSurvCindex.R

@@ -69,6 +69,7 @@
 #'
 #' # Harrell's C-index evaluated up to a specific time horizon
 #' p$score(msr("surv.cindex", t_max = 97))
+#'
 #' # Harrell's C-index evaluated up to the time corresponding to 30% of censoring
 #' p$score(msr("surv.cindex", p_max = 0.3))
 #'

R/MeasureSurvGraf.R

@@ -53,6 +53,7 @@
 #'
 #' @family Probabilistic survival measures
 #' @family distr survival measures
+#' @template example_scoring_rules
 #' @export
 MeasureSurvGraf = R6Class("MeasureSurvGraf",
   inherit = MeasureSurv,

R/MeasureSurvIntLogloss.R

@@ -51,6 +51,7 @@
 #'
 #' @family Probabilistic survival measures
 #' @family distr survival measures
+#' @template example_scoring_rules
 #' @export
 MeasureSurvIntLogloss = R6Class("MeasureSurvIntLogloss",
   inherit = MeasureSurv,

R/MeasureSurvSchmid.R

@@ -50,6 +50,7 @@
 #'
 #' @family Probabilistic survival measures
 #' @family distr survival measures
+#' @template example_scoring_rules
 #' @export
 MeasureSurvSchmid = R6Class("MeasureSurvSchmid",
   inherit = MeasureSurv,

man-roxygen/example_scoring_rules.R

@@ -0,0 +1,45 @@
+#' <% measure = suppressWarnings(get(fullname)$new()) %>
+#'
+#' @examples
+#' library(mlr3)
+#'
+#' # Define a survival Task
+#' task = tsk("lung")
+#'
+#' # Create train and test set
+#' part = partition(task)
+#'
+#' # Train Cox learner on the train set
+#' cox = lrn("surv.coxph")
+#' cox$train(task, row_ids = part$train)
+#'
+#' # Make predictions for the test set
+#' p = cox$predict(task, row_ids = part$test)
+#'
+#' # <%=improper_id%>, G(t) calculated using the test set
+#' p$score(msr("<%=measure$id%>"))
+#'
+#' # <%=improper_id%>, G(t) calculated using the train set (always recommended)
+#' p$score(msr("<%=measure$id%>"), task = task, train_set = part$train)
+#'
+#' # <%=improper_id%>, ERV score (comparing with KM baseline)
+#' p$score(msr("<%=measure$id%>", ERV = TRUE), task = task, train_set = part$train)
+#'
+#' # <%=improper_id%> at specific time point
+#' p$score(msr("<%=measure$id%>", times = 365), task = task, train_set = part$train)
+#'
+#' # <%=improper_id%> at multiple time points (integrated)
+#' p$score(msr("<%=measure$id%>", times = c(125, 365, 450), integrated = TRUE), task = task, train_set = part$train)
+#'
+#' # <%=improper_id%>, use time cutoff
+#' p$score(msr("<%=measure$id%>", t_max = 700), task = task, train_set = part$train)
+#'
+#' # <%=improper_id%>, use time cutoff and also remove observations
+#' p$score(msr("<%=measure$id%>", t_max = 700, remove_obs = TRUE), task = task, train_set = part$train)
+#'
+#' # <%=improper_id%>, use time cutoff corresponding to specific proportion of censoring on the test set
+#' p$score(msr("<%=measure$id%>", p_max = 0.8), task = task, train_set = part$train)
+#'
+#' # <%=proper_id%>, G(t) calculated using the train set
+#' p$score(msr("<%=measure$id%>", proper = TRUE), task = task, train_set = part$train)
+#'