R: Abstract Class for Integrated Measures

MeasureSurvIntegrated {mlr3proba} | R Documentation

## Abstract Class for Integrated Measures

### Description

This is an abstract class that should not be constructed directly.

### Super classes

`mlr3::Measure` -> `mlr3proba::MeasureSurv` -> `MeasureSurvIntegrated`

### Active bindings

`eps`

  (`numeric(1)`)
  Very small number used to prevent log(0) and 1/0 error.

`integrated`

  `(logical(1))` Returns if the measure should be integrated or not. Settable.

`times`

  `(numeric())` Returns the times at which the measure should be evaluated at, or integrated over. Settable.

`method`

  `(integer(1))` Returns which method is used for approximating integration. Settable.

`proper`

  (`logical(1)`)
  If `TRUE` then weights scores by the censoring distribution at the observed event time, which results in a strictly proper scoring rule if censoring and survival time distributions are independent and a sufficiently large dataset is used to weight the measure. If `FALSE` then weights scores by the Graf method which is the more common usage but the loss is not proper.

### Methods

#### Public methods

- `MeasureSurvIntegrated$new()`

- `MeasureSurvIntegrated$clone()`

Inherited methods

- `mlr3::Measure$aggregate()`

- `mlr3::Measure$format()`

- `mlr3::Measure$help()`

- `mlr3::Measure$score()`

- `mlr3proba::MeasureSurv$print()`

#### Method `new()`

This is an abstract class that should not be constructed directly.

##### Usage

    MeasureSurvIntegrated$new(
      integrated = TRUE,
      times,
      method = 2,
      proper = FALSE,
      eps = 0.000000000000001,
      id,
      range,
      minimize,
      packages,
      predict_type,
      properties = character(),
      man = NA_character_,
      se = FALSE
    )

##### Arguments

`integrated`

  (`logical(1)`)
  If `TRUE` (default), returns the integrated score; otherwise, not integrated.

`times`

  (`numeric()`)
  If `integrate == TRUE` then a vector of time-points over which to integrate the score. If `integrate == FALSE` then a single time point at which to return the score.

`method`

  (`integer(1)`)
  If `integrate == TRUE` selects the integration weighting method. `method == 1` corresponds to weighting each time-point equally and taking the mean score over discrete time-points. `method == 2` corresponds to calculating a mean weighted by the difference between time-points. `method == 2` is default to be in line with other packages.

`proper`

  (`logical(1)`)
  If `TRUE` then weights scores by the censoring distribution at the observed event time, which results in a strictly proper scoring rule if censoring and survival time distributions are independent and a sufficiently large dataset is used to weight the measure. If `FALSE` then weights scores by the Graf method which is the more common usage but the loss is not proper. In v0.5.0, the default will be changed to `TRUE`.

`eps`

  (`numeric(1)`)
  Very small number to set zero-valued predicted probabilities to in order to prevent errors in log(0) and 1/0 calculation.

`id`

  (`character(1)`)
  Identifier for the new instance.

`range`

  (`numeric(2)`)
  Feasible range for this measure as `c(lower_bound, upper_bound)`. Both bounds may be infinite.

`minimize`

  (`logical(1)`)
  Set to `TRUE` if good predictions correspond to small values, and to `FALSE` if good predictions correspond to large values. If set to `NA` (default), tuning this measure is not possible.

`packages`

  (`character()`)
  Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand via `requireNamespace()`.

`predict_type`

  (`character(1)`)
  Required predict type of the Learner. Possible values are stored in mlr_reflections$learner_predict_types.

`properties`

  (`character()`)
  Properties of the measure. Must be a subset of mlr_reflections$measure_properties. Supported by `mlr3`:

  - `"requires_task"` (requires the complete Task),

  - `"requires_learner"` (requires the trained Learner),

  - `"requires_train_set"` (requires the training indices from the Resampling), and

  - `"na_score"` (the measure is expected to occasionally return `NA` or `NaN`).

`man`

  (`character(1)`)
  String in the format `[pkg]::[topic]` pointing to a manual page for this object. The referenced help package can be opened via method `$help()`.

`se`

  (`logical(1)`)
  If `TRUE` returns the standard error of the measure.

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    MeasureSurvIntegrated$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

[Package mlr3proba version 0.4.2 Index]