LearnerSurvCoxPHCox {mlsurvlrnrs}

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.

Details

Can be used with

• mlexperiments::MLCrossValidation

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

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.

See Also

survival::coxph()

Examples

# survival analysis

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`
## ------------------------------------------------

LearnerSurvCoxPHCox$new()