mlr_pipeops_trafotask_regrsurv | R Documentation |
Transform TaskRegr to TaskSurv.
Input and output channels are inherited from PipeOpTaskTransformer.
The output is the input TaskRegr transformed to a TaskSurv.
The $state
is a named list
with the $state
elements inherited from PipeOpTaskTransformer.
The parameters are
status :: (numeric(1))
If NULL
then assumed no censoring in the dataset. Otherwise should be a vector of 0/1
s
of same length as the prediction object, where 1
is dead and 0
censored.
mlr3pipelines::PipeOp
-> mlr3proba::PipeOpTransformer
-> mlr3proba::PipeOpTaskTransformer
-> PipeOpTaskRegrSurv
new()
Creates a new instance of this R6 class.
PipeOpTaskRegrSurv$new(id = "trafotask_regrsurv")
id
(character(1)
)
Identifier of the resulting object.
clone()
The objects of this class are cloneable with this method.
PipeOpTaskRegrSurv$clone(deep = FALSE)
deep
Whether to make a deep clone.
Other PipeOps:
PipeOpPredTransformer
,
PipeOpTaskTransformer
,
PipeOpTransformer
,
mlr_pipeops_survavg
,
mlr_pipeops_trafopred_regrsurv
,
mlr_pipeops_trafopred_survregr
,
mlr_pipeops_trafotask_survregr
Other Transformation PipeOps:
mlr_pipeops_trafopred_regrsurv
,
mlr_pipeops_trafopred_survregr
,
mlr_pipeops_trafotask_survregr
## Not run: if (requireNamespace("mlr3pipelines", quietly = TRUE)) { library(mlr3) library(mlr3pipelines) task = tsk("boston_housing") po = po("trafotask_regrsurv") # assume no censoring new_task = po$train(list(task_regr = task, task_surv = NULL))[[1]] print(new_task) # add censoring task_surv = tsk("rats") task_regr = po("trafotask_survregr", method = "omit")$train(list(task_surv, NULL))[[1]] print(task_regr) new_task = po$train(list(task_regr = task_regr, task_surv = task_surv))[[1]] new_task$truth() task_surv$truth() } ## End(Not run)
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