Wrapper around PipeOpCrankCompositor to simplify Graph creation.

## Usage

```
pipeline_crankcompositor(
learner,
method = c("mort"),
overwrite = FALSE,
graph_learner = FALSE
)
```

## Arguments

- learner
`[mlr3::Learner]|[mlr3pipelines::PipeOp]|[mlr3pipelines::Graph]`

Either a`Learner`

which will be wrapped in mlr3pipelines::PipeOpLearner, a`PipeOp`

which will be wrapped in mlr3pipelines::Graph or a`Graph`

itself. Underlying`Learner`

should be LearnerSurv.- method
`character(1)`

Determines what method should be used to produce a continuous ranking from the distribution. Currently only`mort`

is supported, which is the sum of the cumulative hazard, also called*expected/ensemble mortality*, see Ishwaran et al. (2008). For more details, see`get_mortality()`

.- overwrite
`logical(1)`

If`FALSE`

(default) and the prediction already has a`crank`

prediction, then the compositor returns the input prediction unchanged. If`TRUE`

, then the`crank`

will be overwritten.- graph_learner
`logical(1)`

If`TRUE`

returns wraps the Graph as a GraphLearner otherwise (default) returns as a`Graph`

.

## Examples

```
if (FALSE) { # \dontrun{
if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
library("mlr3")
library("mlr3pipelines")
task = tsk("lung")
part = partition(task)
# change the crank prediction type of a Cox's model predictions
grlrn = ppl(
"crankcompositor",
learner = lrn("surv.coxph"),
method = "mort",
overwrite = TRUE,
graph_learner = TRUE
)
grlrn$train(task, part$train)
grlrn$predict(task, part$test)
}
} # }
```