mlr_pipeops_trafopred_classifsurv_disctime: PipeOpPredClassifSurvDiscTime

In mlr-org/mlr3proba: Probabilistic Supervised Learning for 'mlr3'

PipeOpPredClassifSurvDiscTime

Description

Transform PredictionClassif to PredictionSurv by converting event probabilities of a pseudo status variable (discrete time hazards) to survival probabilities using the product rule (Tutz et al. 2016):

P_k = p_k\cdot ... \cdot p_1

Where:

• We assume that continuous time is divided into time intervals [0, t_1), [t_1, t_2), ..., [t_n, \infty)

• P_k = P(T > t_k) is the survival probability at time t_k

• h_k is the discrete-time hazard (classifier prediction), i.e. the conditional probability for an event in the k-interval.

• p_k = 1 - h_k = P(T \ge t_k | T \ge t_{k-1})

Dictionary

This PipeOp can be instantiated via the dictionary mlr3pipelines::mlr_pipeops or with the associated sugar function mlr3pipelines::po():

PipeOpPredClassifSurvDiscTime$new()
mlr_pipeops$get("trafopred_classifsurv_disctime")
po("trafopred_classifsurv_disctime")

Input and Output Channels

The input is a PredictionClassif and a data.table with the transformed data both generated by PipeOpTaskSurvClassifDiscTime. The output is the input PredictionClassif transformed to a PredictionSurv. Only works during prediction phase.

Super class

mlr3pipelines::PipeOp -> PipeOpPredClassifSurvDiscTime

Active bindings

predict_type

(character(1))
Returns the active predict type of this PipeOp, which is "crank"

Methods

Public methods

• PipeOpPredClassifSurvDiscTime$new()

• PipeOpPredClassifSurvDiscTime$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

PipeOpPredClassifSurvDiscTime$new(id = "trafopred_classifsurv_disctime")

Arguments

id

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

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpPredClassifSurvDiscTime$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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

pipeline_survtoclassif_disctime

Other Transformation PipeOps: mlr_pipeops_trafopred_classifsurv_IPCW, mlr_pipeops_trafopred_regrsurv_pem, mlr_pipeops_trafotask_survclassif_IPCW, mlr_pipeops_trafotask_survclassif_disctime, mlr_pipeops_trafotask_survregr_pem

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