From d52c6fa66f10e0553682ef47b618758b44b3a4c5 Mon Sep 17 00:00:00 2001
From: john <bblodfon@gmail.com>
Date: Tue, 22 Aug 2023 09:46:58 +0200
Subject: [PATCH] update doc

---
 man/mlr_learners_surv.bart.Rd | 84 +++--------------------------------
 1 file changed, 7 insertions(+), 77 deletions(-)

diff --git a/man/mlr_learners_surv.bart.Rd b/man/mlr_learners_surv.bart.Rd
index bd043fc2a..1fb67cc0f 100644
--- a/man/mlr_learners_surv.bart.Rd
+++ b/man/mlr_learners_surv.bart.Rd
@@ -7,14 +7,12 @@
 \description{
 Fits a Bayesian Additive Regression Trees (BART) learner to right-censored
 survival data.
+
 For prediction, we return the mean posterior estimates of the survival
 function and the corresponding \code{crank} (expected mortality) using
 \link[mlr3proba:dot-surv_return]{mlr3proba::.surv_return}.
 The full posterior estimates are currently stored in the
-\code{learner$state$surv_test} slot, along with the number of test observations
-\code{N}, number of unique times in the train set \code{K} and number of posterior
-draws \code{M}.
-See example for more details.
+\code{learner$model$surv.test} slot.
 
 Calls \code{\link[BART:surv.bart]{BART::mc.surv.bart()}} from \CRANpkg{BART}.
 }
@@ -25,7 +23,7 @@ Calls \code{\link[BART:surv.bart]{BART::mc.surv.bart()}} from \CRANpkg{BART}.
 }
 }
 
-\section{Initial parameter values}{
+\section{Custom mlr3 parameters}{
 
 \itemize{
 \item \code{quiet} allows to suppress messages generated by the wrapped C++ code. Is \code{TRUE} by default.
@@ -90,79 +88,11 @@ lrn("surv.bart")
 }
 
 \examples{
-library(mlr3proba)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-
-learner = lrn("surv.bart", nskip = 10, ndpost = 20, keepevery = 2)
-task = tsk("lung")
-task$missings() # has missing values
-
-# split to train and test sets
-set.seed(42)
-part = partition(task)
-
-# Train
-learner$train(task, row_ids = part$train)
-
-# Importance: average number of times a feature has been used in the trees
-learner$importance()
-
-# Test
-p = learner$predict(task, row_ids = part$test)
-p$score() # C-index
-
-# Mean survival probabilities for the first 3 patients at given time points
-p$distr$survival(times = c(1,50,150))[,1:3]
-
-# number of posterior draws
-M = learner$state$M
-stopifnot(M == 20)
-# number of test observations
-N = learner$state$N
-stopifnot(N == length(part$test))
-# number of unique time points in the train set
-K = learner$state$K
-stopifnot(K == length(task$unique_times(rows = part$train)))
-# the actual times are also available in the `$model` slot:
-head(learner$model$times)
-
-# Full posterior prediction matrix
-surv_test = learner$state$surv_test
-stopifnot(all(dim(surv_test) == c(M, K * N)))
-
-# Posterior survival function estimates for the 1st test patient for all
-# time points (from the train set) - see Sparapani (2021), pages 34-35
-post_surv = surv_test[, 1:K]
-
-# For every time point, get the median survival estimate as well as
-# the lower and upper bounds of the 95\% quantile credible interval
-surv_data = post_surv \%>\%
-  as.data.frame() \%>\%
-  `colnames<-` (learner$model$times) \%>\%
-  summarise(across(everything(), list(
-    median   = ~ median(.),
-    low_qi   = ~ quantile(., 0.025),
-    high_qi  = ~ quantile(., 0.975)
-  ))) \%>\%
-  pivot_longer(
-    cols = everything(),
-    names_to = c("times", ".value"),
-    names_pattern = "(^[^_]+)_(.*)" # everything until the first underscore
-  ) \%>\%
-  mutate(times = as.numeric(times))
-surv_data
+learner = mlr3::lrn("surv.bart")
+print(learner)
 
-# Draw a survival curve for the first patient in the test set with
-# uncertainty quantified
-surv_data \%>\%
-  ggplot(aes(x = times, y = median)) +
-  geom_step(col = 'black') +
-  xlab('Time (Days)') +
-  ylab('Survival Probability') +
-  geom_ribbon(aes(ymin = low_qi, ymax = high_qi), alpha = 0.3) +
-  theme_bw()
+# available parameters:
+learner$param_set$ids()
 }
 \references{
 Sparapani, Rodney, Spanbauer, Charles, McCulloch, Robert (2021).