mlr3proba (version 0.2.1)

mlr_graphs_distrcompositor: Estimate Survival distr Predict Type Pipeline

Description

Wrapper around PipeOpDistrCompositor to simplify Graph creation.

Usage

pipeline_distrcompositor(
  learner,
  estimator = c("kaplan", "nelson"),
  form = c("aft", "ph", "po"),
  overwrite = FALSE,
  graph_learner = FALSE
)
distrcompositor(...)

Arguments

learner

[mlr3::Learner]|[mlr3pipelines::PipeOp]|[mlr3pipelines::Graph] Either a Learner which will be wrapped in mlr3pipelines::PipeOpLearner, a PipeOp which will be wrapped in mlr3pipelines::Graph or a Graph itself. Underlying Learner should be LearnerSurv.

estimator

character(1) One of kaplan (default) or nelson, corresponding to the Kaplan-Meier and Nelson-Aalen estimators respectively. Used to estimate the baseline survival distribution.

form

character(1) One of aft (default), ph, or po, corresponding to accelerated failure time, proportional hazards, and proportional odds respectively. Used to determine the form of the composed survival distribution.

overwrite

logical(1) If FALSE (default) then if the learner already has a distr, the compositor does nothing. If TRUE then the distr is overwritten by the compositor if already present, which may be required for changing the prediction distr from one model form to another.

graph_learner

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

...

ANY For use with distrcompositor, now deprecated.

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_probregrcompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoregr

Examples

# NOT RUN {
library("mlr3")
library("mlr3pipelines")

task = tgen("simsurv")$generate(10)
pipe = ppl(
  "distrcompositor",
  learner = lrn("surv.rpart"),
  estimator = "kaplan",
  form = "ph"
)
pipe$train(task)
pipe$predict(task)
# }