Uses a predicted `distr`

in a PredictionSurv to estimate (or 'compose') a `crank`

prediction.

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

:

PipeOpCrankCompositor$new() mlr_pipeops$get("crankcompose") po("crankcompose")

PipeOpCrankCompositor has one input channel named "input", which takes
`NULL`

during training and PredictionSurv during prediction.

PipeOpCrankCompositor 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 the `crank`

predict type overwritten by the given estimation method.

The `$state`

is left empty (`list()`

).

`method`

::`character(1)`

Determines what method should be used to produce a continuous ranking from the distribution. One of`median`

,`mode`

, or`mean`

corresponding to the respective functions in the predicted survival distribution. Note that for models with a proportional hazards form, the ranking implied by`mean`

and`median`

will be identical (but not the value of`crank`

itself). Default is`mean`

.`which`

::`numeric(1)`

If`method = "mode"`

then specifies which mode to use if multi-modal, default is the first.`response`

::`logical(1)`

If`TRUE`

then the`response`

predict type is estimated with the same values as`crank`

.`overwrite`

::`logical(1)`

If`FALSE`

(default) then if the "pred" input already has a`crank`

, the compositor only composes a`response`

type if`response = TRUE`

and does not already exist. If`TRUE`

then both the`crank`

and`response`

are overwritten.

The `median`

, `mode`

, or `mean`

will use analytical expressions if possible but if not they are
calculated using methods from distr6. `mean`

requires cubature.

Other survival compositors:
`mlr_pipeops_compose_distr`

`mlr3pipelines::PipeOp`

-> `PipeOpCrankCompositor`

`new()`

Creates a new instance of this R6 class.

PipeOpCrankCompositor$new( id = "compose_crank", param_vals = list(method = "mean", response = FALSE, overwrite = FALSE) )

`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.

`clone()`

The objects of this class are cloneable with this method.

PipeOpCrankCompositor$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

if (FALSE) { if (requireNamespace("mlr3pipelines", quietly = TRUE)) { library(mlr3) library(mlr3pipelines) task = tsk("rats") learn = lrn("surv.coxph")$train(task)$predict(task) poc = po("crankcompose", param_vals = list(method = "median")) poc$predict(list(learn))[[1]] if (requireNamespace("cubature", quietly = TRUE)) { learn = lrn("surv.coxph")$train(task)$predict(task) poc = po("crankcompose", param_vals = list(method = "mean")) poc$predict(list(learn))[[1]] } } }