Transform PredictionRegr to PredictionSurv. The predicted piece-wise constant hazards contained in PredictionRegr are transformed into survival probabilities and wrapped in a PredictionSurv object.
We compute the survival probability from the predicted hazards using the following relation: $$S(t | \mathbf{x}) = \exp \left( - \int_{0}^{t} \lambda(s | \mathbf{x}) \, ds \right) = \exp \left( - \sum_{j = 1}^{J} \lambda(j | \mathbf{x}) d_j\, \right),$$ where \(j = 1, \ldots, J\) denotes the interval, \(t\) the time, and \(d_j\) the duration of interval \(j\).
For a more detailed description of PEM, refer to pipeline_survtoregr_pem or the referred article.
Dictionary
This PipeOp can be instantiated via the
dictionary mlr3pipelines::mlr_pipeops
or with the associated sugar function mlr3pipelines::po()
:
Input and Output Channels
The input consists of a PredictionRegr and a data.table containing the transformed data. The PredictionRegr is provided by the mlr3::LearnerRegr, while the data.table is generated by PipeOpTaskSurvRegrPEM. The output is the input PredictionRegr transformed to a PredictionSurv. Only works during prediction phase.
References
Bender, Andreas, Groll, Andreas, Scheipl, Fabian (2018). “A generalized additive model approach to time-to-event analysis.” Statistical Modelling, 18(3-4), 299–321. https://doi.org/10.1177/1471082X17748083.
See also
Other PipeOps:
mlr_pipeops_survavg
,
mlr_pipeops_trafotask_survregr_pem
Other Transformation PipeOps:
mlr_pipeops_trafopred_classifsurv_IPCW
,
mlr_pipeops_trafopred_classifsurv_disctime
,
mlr_pipeops_trafotask_survclassif_IPCW
,
mlr_pipeops_trafotask_survclassif_disctime
,
mlr_pipeops_trafotask_survregr_pem
Super class
mlr3pipelines::PipeOp
-> PipeOpPredRegrSurvPEM
Active bindings
predict_type
(
character(1)
)
Returns the active predict type of this PipeOp, which is"crank"
Methods
Method new()
Creates a new instance of this R6 class.
Usage
PipeOpPredRegrSurvPEM$new(id = "trafopred_regrsurv_pem")