Generates plots for mlr3proba::PredictionSurv, depending on argument `type`

:

`"calib"`

(default): Calibration plot comparing the average predicted survival distribution
to a Kaplan-Meier prediction, this is *not* a comparison of a stratified `crank`

or `lp`

prediction. `object`

must have `distr`

prediction. `geom_line()`

is used for comparison split
between the prediction (`Pred`

) and Kaplan-Meier estimate (`KM`

). In addition labels are added
for the x (`T`

) and y (`S(T)`

) axes.

`"dcalib"`

: Distribution calibration plot. A model is D-calibrated if X% of deaths occur before
the X/100 quantile of the predicted distribution, e.g. if 50% of observations die before their
predicted median survival time. A model is D-calibrated if the resulting plot lies on x = y.

# S3 method for PredictionSurv
autoplot(
object,
type = c("calib", "dcalib"),
task = NULL,
row_ids = NULL,
times = NULL,
xyline = TRUE,
cuts = 11L,
...
)

## Arguments

object |
(mlr3proba::PredictionSurv). |

type |
(character(1)):
Type of the plot. See description. |

task |
(mlr3proba::TaskSurv)
If `type = "calib"` then `task` is passed to `$predict` in the Kaplan-Meier learner. |

row_ids |
(`integer()` )
If `type = "calib"` then `row_ids` is passed to `$predict` in the Kaplan-Meier learner. |

times |
(`numeric()` )
If `type = "calib"` then `times` is the values on the x-axis to plot over,
if `NULL` uses all times from `task` . |

xyline |
(`logical(1)` )
If `TRUE` (default) plots the x-y line for `type = "dcalib"` . |

cuts |
(`integer(1)` )
Number of cuts in (0,1) to plot `dcalib` over, default is `11` . |

... |
(`any` ):
Additional arguments, currently unused. |

## References

Haider H, Hoehn B, Davis S, Greiner R (2020).
“Effective Ways to Build and Evaluate Individual Survival Distributions.”
*Journal of Machine Learning Research*, **21**(85), 1-63.
https://jmlr.org/papers/v21/18-772.html.

## Examples