small style changes

R/learner_BART_surv_bart.R

@@ -20,9 +20,15 @@
 #' - `mc.cores` is initialized to 1 to avoid threading conflicts with \CRANpkg{future}.
 #'
 #' @section Custom mlr3 parameters:
-#' - `quiet` allows to suppress messages generated by the wrapped C++ code. Is initialized to `TRUE`.
-#' - `importance` allows to choose the type of importance. Default is `count`, see documentation of method `$importance()` for more details.
-#' - `which.curve` allows to choose which posterior draw will be used for the calculation of the `crank` prediction. If between (0,1) it is taken as the quantile of the curves otherwise if greater than 1 it is taken as the curve index, can also be 'mean'. By default the **median posterior** is used, i.e. `which.curve` is 0.5.
+#' - `quiet` allows to suppress messages generated by the wrapped C++ code. Is
+#' initialized to `TRUE`.
+#' - `importance` allows to choose the type of importance. Default is `count`,
+#' see documentation of method `$importance()` for more details.
+#' - `which.curve` allows to choose which posterior draw will be used for the
+#' calculation of the `crank` prediction. If between (0,1) it is taken as the
+#' quantile of the curves otherwise if greater than 1 it is taken as the curve
+#' index, can also be 'mean'. By default the **median posterior** is used,
+#' i.e. `which.curve` is 0.5.
 #'
 #' @templateVar id surv.bart
 #' @template learner
@@ -127,8 +133,8 @@ LearnerSurvLearnerSurvBART = R6Class("LearnerSurvLearnerSurvBART",
 
       x.train = as.data.frame(task$data(cols = task$feature_names))
       truth = task$truth()
-      times = truth[,1]
-      delta = truth[,2] # delta => status
+      times = truth[, 1]
+      delta = truth[, 2] # delta => status
 
       list(
         model = invoke(
@@ -196,14 +202,14 @@ LearnerSurvLearnerSurvBART = R6Class("LearnerSurvLearnerSurvBART",
 
       # Convert full posterior survival matrix to 3D survival array
       # See page 34-35 in Sparapani (2021) for more details
-      surv.array = aperm(
+      surv_array = aperm(
         array(pred$surv.test, dim = c(M, K, N), dimnames = list(NULL, times, NULL)),
         c(3, 2, 1)
       )
 
       # distr => 3d survival array
       # crank => expected mortality
-      mlr3proba::.surv_return(times = times, surv = surv.array,
+      mlr3proba::.surv_return(times = times, surv = surv_array,
         which.curve = pars$which.curve)
     }
   )