PipeOpPredClassifSurvDiscTime
Source:R/PipeOpPredClassifSurvDiscTime.R
mlr_pipeops_trafopred_classifsurv_disctime.Rd
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()
:
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.
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.
Super class
mlr3pipelines::PipeOp
-> PipeOpPredClassifSurvDiscTime
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
PipeOpPredClassifSurvDiscTime$new(id = "trafopred_classifsurv_disctime")