PipeOpPredRegrSurv

Transform PredictionRegr to PredictionSurv.

Input and Output Channels

Input and output channels are inherited from PipeOpPredTransformer.

The output is the input PredictionRegr transformed to a PredictionSurv. Censoring can be added with the status hyper-parameter. se is ignored.

State

The $state is a named list with the $state elements inherited from PipeOpPredTransformer.

Parameters

The parameters are

• status :: (numeric(1))
  If NULL then assumed no censoring in the dataset. Otherwise should be a vector of 0/1s of same length as the prediction object, where 1 is dead and 0 censored.

See also

Other PipeOps: PipeOpPredTransformer, PipeOpTaskTransformer, PipeOpTransformer, mlr_pipeops_survavg, mlr_pipeops_trafopred_survregr, mlr_pipeops_trafotask_regrsurv, mlr_pipeops_trafotask_survregr

Other Transformation PipeOps: mlr_pipeops_trafopred_survregr, mlr_pipeops_trafotask_regrsurv, mlr_pipeops_trafotask_survregr

Super classes

mlr3pipelines::PipeOp -> mlr3proba::PipeOpTransformer -> mlr3proba::PipeOpPredTransformer -> PipeOpPredRegrSurv

Methods

Public methods

• PipeOpPredRegrSurv$new()

• PipeOpPredRegrSurv$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

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Method new()

Creates a new instance of this R6 class.

Usage

PipeOpPredRegrSurv$new(id = "trafopred_regrsurv", param_vals = list())

Arguments

id

(character(1))
Identifier of the resulting object.

param_vals

(list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.

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Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpPredRegrSurv$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) {
if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
  library(mlr3)
  library(mlr3pipelines)

  # simple example
  pred = PredictionRegr$new(row_ids = 1:10, truth = 1:10, response = 1:10)
  po = po("trafopred_regrsurv")

  # assume no censoring
  new_pred = po$predict(list(pred = pred, task = NULL))[[1]]
  po$train(list(NULL, NULL))
  print(new_pred)

  # add censoring
  task_surv = tsk("rats")
  task_regr = po("trafotask_survregr", method = "omit")$train(list(task_surv, NULL))[[1]]
  learn = lrn("regr.featureless")
  pred = learn$train(task_regr)$predict(task_regr)
  po = po("trafopred_regrsurv")
  new_pred = po$predict(list(pred = pred, task = task_surv))[[1]]
  all.equal(new_pred$truth, task_surv$truth())
}
}