Survival to Classification Reduction using Discrete Time Pipeline

Wrapper around PipeOpTaskSurvClassifDiscTime and PipeOpPredClassifSurvDiscTime to simplify Graph creation.

Usage

pipeline_survtoclassif_disctime(
  learner,
  cut = NULL,
  max_time = NULL,
  graph_learner = FALSE
)

Arguments

learner

LearnerClassif
Classification learner to fit the transformed TaskClassif. learner must have predict_type of type "prob".

cut

(numeric())
Split points, used to partition the data into intervals. If unspecified, all unique event times will be used. If cut is a single integer, it will be interpreted as the number of equidistant intervals from 0 until the maximum event time.

max_time

(numeric(1))
If cut is unspecified, this will be the last possible event time. All event times after max_time will be administratively censored at max_time.

graph_learner

(logical(1))
If TRUE returns wraps the Graph as a GraphLearner otherwise (default) returns as a Graph.

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Details

The pipeline consists of the following steps:

1. PipeOpTaskSurvClassifDiscTime Converts TaskSurv to a TaskClassif.

2. A LearnerClassif is fit and predicted on the new TaskClassif.

3. PipeOpPredClassifSurvDiscTime transforms the resulting PredictionClassif to PredictionSurv.

4. Optionally: PipeOpModelMatrix is used to transform the formula of the task before fitting the learner.

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("survtoclassif_disctime")
ppl("survtoclassif_disctime")

References

Tutz, Gerhard, Schmid, Matthias (2016). Modeling Discrete Time-to-Event Data, series Springer Series in Statistics. Springer International Publishing. ISBN 978-3-319-28156-8 978-3-319-28158-2, http://link.springer.com/10.1007/978-3-319-28158-2.

See also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoregr_pem

Examples

if (FALSE) { # \dontrun{
  library(mlr3)
  library(mlr3learners)
  library(mlr3pipelines)

  task = tsk("lung")
  part = partition(task)

  grlrn = ppl(
    "survtoclassif_disctime",
    learner = lrn("classif.log_reg"),
    cut = 4, # 4 equidistant time intervals
    graph_learner = TRUE
  )
  grlrn$train(task, row_ids = part$train)
  grlrn$predict(task, row_ids = part$test)
} # }