mlsurvlrnrs (version 0.0.8)

LearnerSurvCoxPHCox: R6 Class to construct a Cox proportional hazards survival learner

Description

The LearnerSurvCoxPHCox class is the interface to perform a Cox regression with the survival R package for use with the mlexperiments package.

Super class

mlexperiments::MLLearnerBase -> LearnerSurvCoxPHCox

Methods

Public methods

• LearnerSurvCoxPHCox$new()

• LearnerSurvCoxPHCox$clone()

Inherited methods

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

---

Method new()

Create a new LearnerSurvCoxPHCox object.

Usage

LearnerSurvCoxPHCox$new()

Returns

A new LearnerSurvCoxPHCox R6 object.

Examples

if (requireNamespace("survival", quietly = TRUE)) {
  LearnerSurvCoxPHCox$new()
}

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCoxPHCox$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

Can be used with

• mlexperiments::MLCrossValidation

See Also

survival::coxph()

Examples

# survival analysis
if (requireNamespace("survival", quietly = TRUE) &&
requireNamespace("glmnet", 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)]

  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_coxph_cox_optimizer <- mlexperiments::MLCrossValidation$new(
    learner = LearnerSurvCoxPHCox$new(),
    fold_list = fold_list,
    ncores = 1L,
    seed = seed
  )
  surv_coxph_cox_optimizer$performance_metric <- c_index

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

  surv_coxph_cox_optimizer$execute()
}


## ------------------------------------------------
## Method `LearnerSurvCoxPHCox$new`
## ------------------------------------------------

if (requireNamespace("survival", quietly = TRUE)) {
  LearnerSurvCoxPHCox$new()
}