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

`mlr3::Measure`

-> `mlr3proba::MeasureSurv`

-> `MeasureSurvIntegrated`

`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.

`new()`

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

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

`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.`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.

`clone()`

The objects of this class are cloneable with this method.

MeasureSurvIntegrated$clone(deep = FALSE)

`deep`

Whether to make a deep clone.