Estimates (or 'composes') a survival distribution from a predicted baseline distr and a crank or lp from two PredictionSurvs.

Compositor Assumptions:

  • The baseline distr is a discrete estimator, e.g. surv.kaplan.

  • The composed distr is of a linear form

  • If lp is missing then crank is equivalent

These assumptions are strong and may not be reasonable. Future updates will upgrade this compositor to be more flexible.

Dictionary

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

PipeOpDistrCompositor$new()
mlr_pipeops$get("distrcompose")
po("distrcompose")

Input and Output Channels

PipeOpDistrCompositor has two input channels, "base" and "pred". Both input channels take NULL during training and PredictionSurv during prediction.

PipeOpDistrCompositor has one output channel named "output", producing NULL during training and a PredictionSurv during prediction.

The output during prediction is the PredictionSurv from the "pred" input but with an extra (or overwritten) column for distr predict type; which is composed from the distr of "base" and lp or crank of "pred".

State

The $state is left empty (list()).

Parameters

The parameters are:

  • form :: character(1)
    Determines the form that the predicted linear survival model should take. This is either, accelerated-failure time, aft, proportional hazards, ph, or proportional odds, po. Default aft.

  • overwrite :: logical(1)
    If FALSE (default) then if the "pred" input already has a distr, the compositor does nothing and returns the given PredictionSurv. If TRUE then the distr is overwritten with the distr composed from lp/crank - this is useful for changing the prediction distr from one model form to another.

Internals

The respective forms above have respective survival distributions: $$aft: S(t) = S_0(\frac{t}{exp(lp)})$$ $$ph: S(t) = S_0(t)^{exp(lp)}$$ $$po: S(t) = \frac{S_0(t)}{exp(-lp) + (1-exp(-lp)) S_0(t)}$$ # nolint where \(S_0\) is the estimated baseline survival distribution, and \(lp\) is the predicted linear predictor. If the input model does not predict a linear predictor then crank is assumed to be the lp - this may be a strong and unreasonable assumption.

See also

Super class

mlr3pipelines::PipeOp -> PipeOpDistrCompositor

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

PipeOpDistrCompositor$new(
  id = "compose_distr",
  param_vals = list(form = "aft", overwrite = FALSE)
)

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.


Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpDistrCompositor$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { if (requireNamespace("mlr3pipelines", quietly = TRUE)) { library(mlr3) library(mlr3pipelines) task = tsk("rats") base = lrn("surv.kaplan")$train(task)$predict(task) pred = lrn("surv.coxph")$train(task)$predict(task) pod = po("distrcompose", param_vals = list(form = "aft", overwrite = TRUE)) pod$predict(list(base = base, pred = pred))[[1]] } }