mlr_pipeops_trafotask_survregr_pem: PipeOpTaskSurvRegrPEM

In mlr-org/mlr3proba: Probabilistic Supervised Learning for 'mlr3'

PipeOpTaskSurvRegrPEM

Description

Transform TaskSurv to TaskRegr by dividing continuous time into multiple time intervals for each observation. This transformation creates a new target variable pem_status that indicates whether an event occurred within each time interval.

Dictionary

This PipeOp can be instantiated via the dictionary mlr3pipelines::mlr_pipeops or with the associated sugar function mlr3pipelines::po():

PipeOpTaskSurvRegrPEM$new()
mlr_pipeops$get("trafotask_survregr_pem")
po("trafotask_survregr_pem")

Input and Output Channels

PipeOpTaskSurvRegrPEM has one input channel named "input", and two output channels, one named "output" and the other "transformed_data".

During training, the "output" is the "input" TaskSurv transformed to a TaskRegr. The target column is named "pem_status" and indicates whether an event occurred in each time interval. An additional numeric feature named "tend" contains the end time point of each interval. Lastly, the "output" task has an offset column "offset". The offset, also referred to as exposure, is the logarithm of time spent in interval j, i.e. log(t_j). The "transformed_data" is an empty data.table.

During prediction, the "input" TaskSurv is transformed to the "output" TaskRegr with "pem_status" as target, "tend" included as feature and and the "offset" column which is assigned the offset "col_role". The "transformed_data" is a data.table with columns the "pem_status" target of the "output" task, the "id" (original observation ids), "obs_times" (observed times per "id") and "tend" (end time of each interval). This "transformed_data" is only meant to be used with the PipeOpPredRegrSurvPEM.

State

The ⁠$state⁠ contains information about the cut parameter used.

Parameters

The parameters are

• cut :: numeric()
  Split points, used to partition the data into intervals based on the time column. If unspecified, all unique event times will be used. If cut is a single integer, it will be interpreted as the number of equidistant intervals from 0 until the maximum event time.

• max_time :: numeric(1)
  If cut is unspecified, this will be the last possible event time. All event times after max_time will be administratively censored at max_time. Needs to be greater than the minimum event time in the given task.

Super class

mlr3pipelines::PipeOp -> PipeOpTaskSurvRegrPEM

Methods

Public methods

• PipeOpTaskSurvRegrPEM$new()

• PipeOpTaskSurvRegrPEM$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

PipeOpTaskSurvRegrPEM$new(id = "trafotask_survregr_pem")

Arguments

id

(character(1))
Identifier of the resulting object.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTaskSurvRegrPEM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Bender, Andreas, Groll, Andreas, Scheipl, Fabian (2018). “A generalized additive model approach to time-to-event analysis.” Statistical Modelling, 18(3-4), 299–321. https://doi.org/10.1177/1471082X17748083.

See Also

pipeline_survtoregr_pem

Other PipeOps: mlr_pipeops_survavg, mlr_pipeops_trafopred_regrsurv_pem

Other Transformation PipeOps: mlr_pipeops_trafopred_classifsurv_IPCW, mlr_pipeops_trafopred_classifsurv_disctime, mlr_pipeops_trafopred_regrsurv_pem, mlr_pipeops_trafotask_survclassif_IPCW, mlr_pipeops_trafotask_survclassif_disctime

Examples

## Not run: 
  library(mlr3)
  library(mlr3learners)
  library(mlr3pipelines)

  task = tsk("lung")

  # transform the survival task to a regression task
  # all unique event times are used as cutpoints
  po_pem = po("trafotask_survregr_pem")
  task_regr = po_pem$train(list(task))[[1L]]

  # the end time points of the discrete time intervals
  unique(task_regr$data(cols = "tend")[[1L]])

  # train a regression learner that supports poisson regression
  # e.g. regr.gam
  # won't run unless learner can accept offset column role
  learner = lrn("regr.gam", formula = pem_status ~ s(age) + s(tend), family = "poisson")
  learner$train(task_regr)

  # e.g. regr.xgboost, note prior data processing steps
  learner = as_learner(
    po("modelmatrix", formula = ~ as.factor(tend) + .) %>>%
    lrn("regr.xgboost", objective = "count:poisson", nrounds = 100, eta = 0.1)
  )
  learner$train(task_regr)
  
## End(Not run)

mlr-org/mlr3proba documentation built on April 12, 2025, 4:38 p.m.