This object stores the predictions returned by a learner of class LearnerSurv.
The task_type is set to "surv".
mlr3::Prediction -> PredictionSurv
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.
new()Creates a new instance of this R6 class.
PredictionSurv$new( task = NULL, row_ids = task$row_ids, truth = task$truth(), crank = NULL, distr = NULL, lp = NULL, response = NULL, check = TRUE )
task(TaskSurv)
Task, used to extract defaults for row_ids and truth.
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 time 0 with all entries 1 and the last
with all entries 0. If a VectorDistribution 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))
If TRUE, performs argument checks and predict type conversions.
clone()The objects of this class are cloneable with this method.
PredictionSurv$clone(deep = FALSE)
deepWhether to make a deep clone.
Other Prediction:
PredictionDens
# NOT RUN {
library(mlr3)
task = tsk("rats")
learner = lrn("surv.kaplan")
p = learner$train(task, row_ids = 1:20)$predict(task, row_ids = 21:30)
head(as.data.table(p))
# }
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