mlsurvlrnrs (version 0.0.8)

LearnerSurvRpartCox: LearnerSurvRpartCox R6 class

Description

This learner is a wrapper around rpart::rpart() in order to fit recursive partitioning and regression trees with survival data.

Super class

mlexperiments::MLLearnerBase -> LearnerSurvRpartCox

Methods

Public methods

• LearnerSurvRpartCox$new()

• LearnerSurvRpartCox$clone()

Inherited methods

• mlexperiments::MLLearnerBase$bayesian_scoring_function()
• mlexperiments::MLLearnerBase$cross_validation()
• mlexperiments::MLLearnerBase$fit()
• mlexperiments::MLLearnerBase$predict()

---

Method new()

Create a new LearnerSurvRpartCox object.

Usage

LearnerSurvRpartCox$new()

Details

This learner is a wrapper around rpart::rpart() in order to fit recursive partitioning and regression trees with survival data.

Examples

if (requireNamespace("rpart", quietly = TRUE)) {
  LearnerSurvRpartCox$new()
}

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRpartCox$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

Optimization metric: C-index * Can be used with

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

Implemented methods:

• $fit To fit the model.

• $predict To predict new data with the model.

• $cross_validation To perform a grid search (hyperparameter optimization).

• $bayesian_scoring_function To perform a Bayesian hyperparameter optimization.

Parameters that are specified with parameter_grid and / or learner_args are forwarded to rpart's argument control (see rpart::rpart.control() for further details).

See Also

rpart::rpart(), c_index(), rpart::rpart.control()

rpart::rpart(), c_index(),

Examples

# survival analysis
if (requireNamespace("survival", quietly = TRUE) &&
requireNamespace("glmnet", quietly = TRUE) &&
requireNamespace("rpart", quietly = TRUE) &&
requireNamespace("splitTools", quietly = TRUE)) {

  dataset <- survival::colon |>
    data.table::as.data.table() |>
    na.omit()
  dataset <- dataset[get("etype") == 2, ]

  seed <- 123
  surv_cols <- c("status", "time", "rx")

  feature_cols <- colnames(dataset)[3:(ncol(dataset) - 1)]

  ncores <- 2L

  split_vector <- splitTools::multi_strata(
    df = dataset[, .SD, .SDcols = surv_cols],
    strategy = "kmeans",
    k = 4
  )

  train_x <- model.matrix(
    ~ -1 + .,
    dataset[, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])]
  )
  train_y <- survival::Surv(
    event = (dataset[, get("status")] |>
               as.character() |>
               as.integer()),
    time = dataset[, get("time")],
    type = "right"
  )

  fold_list <- splitTools::create_folds(
    y = split_vector,
    k = 3,
    type = "stratified",
    seed = seed
  )

  surv_rpart_optimizer <- mlexperiments::MLCrossValidation$new(
    learner = LearnerSurvRpartCox$new(),
    fold_list = fold_list,
    ncores = ncores,
    seed = seed
  )
  surv_rpart_optimizer$learner_args <- list(
    minsplit = 10L,
    maxdepth = 20L,
    cp = 0.03,
    method = "exp"
  )
  surv_rpart_optimizer$performance_metric <- c_index

  # set data
  surv_rpart_optimizer$set_data(
    x = train_x,
    y = train_y
  )

  surv_rpart_optimizer$execute()
}


## ------------------------------------------------
## Method `LearnerSurvRpartCox$new`
## ------------------------------------------------

if (requireNamespace("rpart", quietly = TRUE)) {
  LearnerSurvRpartCox$new()
}