This object stores the predictions returned by a learner of class LearnerSurv.
The task_type
is set to "surv"
.
See also
Other Prediction:
PredictionDens
Super class
mlr3::Prediction
-> PredictionSurv
Active bindings
truth
(
Surv
)
True (observed) outcome.crank
(
numeric()
)
Access the stored predicted continuous ranking.distr
(distr6::Matdist|distr6::VectorDistribution)
Convert the stored survival matrix to a survival distribution.lp
(
numeric()
)
Access the stored predicted linear predictor.response
(
numeric()
)
Access the stored predicted survival time.
Methods
Method new()
Creates a new instance of this R6 class.
Usage
PredictionSurv$new(
task = NULL,
row_ids = task$row_ids,
truth = task$truth(),
crank = NULL,
distr = NULL,
lp = NULL,
response = NULL,
check = TRUE
)
Arguments
task
(TaskSurv)
Task, used to extract defaults forrow_ids
andtruth
.row_ids
(
integer()
)
Row ids of the predicted observations, i.e. the row ids of the test set.truth
(
survival::Surv()
)
True (observed) response.crank
(
numeric()
)
Numeric vector of predicted continuous rankings (or relative risks). One element for each observation in the test set. For a pair of continuous ranks, a higher rank indicates that the observation is more likely to experience the event.distr
(
matrix()|[distr6::Matdist]|[distr6::VectorDistribution]
)
Either a matrix of predicted survival probabilities or a distr6::VectorDistribution or a distr6::Matdist. If a matrix then column names must be given and correspond to survival times. Rows of matrix correspond to individual predictions. It is advised that the first column should be time0
with all entries1
and the last with all entries0
. If aVectorDistribution
then each distribution in the vector should correspond to a predicted survival distribution.lp
(
numeric()
)
Numeric vector of linear predictor scores. One element for each observation in the test set. \(lp = X\beta\) where \(X\) is a matrix of covariates and \(\beta\) is a vector of estimated coefficients.response
(
numeric()
)
Numeric vector of predicted survival times. One element for each observation in the test set.check
(
logical(1)
)
IfTRUE
, performs argument checks and predict type conversions.
Examples
library(mlr3)
task = tsk("rats")
learner = lrn("surv.kaplan")
p = learner$train(task, row_ids = 1:20)$predict(task, row_ids = 21:30)
head(as.data.table(p))
#> row_ids time status crank distr
#> 1: 21 79 FALSE 0.4616396 <list[1]>
#> 2: 22 91 FALSE 0.4616396 <list[1]>
#> 3: 23 98 FALSE 0.4616396 <list[1]>
#> 4: 24 76 FALSE 0.4616396 <list[1]>
#> 5: 25 89 FALSE 0.4616396 <list[1]>
#> 6: 26 104 FALSE 0.4616396 <list[1]>