mlr3proba: Probabilistic Supervised Learning for 'mlr3'

mlr_graphs_responsecompositor

R Documentation

Estimate Survival Time/Response Predict Type Pipeline

Description

Wrapper around PipeOpResponseCompositor to simplify Graph creation.

Usage

pipeline_responsecompositor(
  learner,
  method = "rmst",
  tau = NULL,
  add_crank = FALSE,
  overwrite = FALSE,
  graph_learner = FALSE
)

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.
`method`	(`character(1)`) Determines what method should be used to produce a survival time (response) from the survival distribution. Available methods are `"rmst"` and `"median"`, corresponding to the restricted mean survival time and the median survival time respectively.
`tau`	(`numeric(1)`) Determines the time point up to which we calculate the restricted mean survival time (works only for the `"rmst"` method). If `NULL` (default), all the available time points in the predicted survival distribution will be used.
`add_crank`	(`logical(1)`) If `TRUE` then `crank` predict type will be set as `-response` (as higher survival times correspond to lower risk). Works only if `overwrite` is `TRUE`.
`overwrite`	(`logical(1)`) If `FALSE` (default) and the prediction already has a `response` prediction, then the compositor returns the input prediction unchanged. If `TRUE`, then the `response` (and the `crank`, if `add_crank` is `TRUE`) will be overwritten.
`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

Dictionary

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

mlr_graphs$get("responsecompositor")
ppl("responsecompositor")

Examples


## Not run: 
  library(mlr3)
  library(mlr3pipelines)

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

  # add survival time prediction type to the predictions of a Cox model
  grlrn = ppl(
    "responsecompositor",
    learner = lrn("surv.coxph"),
    method = "rmst",
    overwrite = TRUE,
    graph_learner = TRUE
  )
  grlrn$train(task, part$train)
  grlrn$predict(task, part$test)

## End(Not run)

mlr-org/mlr3proba documentation built on April 12, 2025, 4:38 p.m.