Estimate Survival crank Predict Type Pipeline

Wrapper around PipeOpCrankCompositor to simplify Graph creation.

Usage

pipeline_crankcompositor(
  learner,
  method = c("mort"),
  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 continuous ranking from the distribution. Currently only mort is supported, which is the sum of the cumulative hazard, also called expected/ensemble mortality, see Ishwaran et al. (2008). For more details, see get_mortality().

overwrite

(logical(1))
If FALSE (default) and the prediction already has a crank prediction, then the compositor returns the input prediction unchanged. If TRUE, then the crank 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("crankcompositor")
ppl("crankcompositor")

See also

Other pipelines: mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime

Examples

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

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

  # change the crank prediction type of a Cox's model predictions
  grlrn = ppl(
    "crankcompositor",
    learner = lrn("surv.coxph"),
    method = "mort",
    overwrite = TRUE,
    graph_learner = TRUE
  )
  grlrn$train(task, part$train)
  grlrn$predict(task, part$test)
} # }