LearnerSurvGlmnetCox {mlsurvlrnrs}

R6 Class to construct a Glmnet survival learner for Cox regression

Description

The 'LearnerSurvGlmnetCox' class is the interface to perform a Cox regression with the 'glmnet' R package for use with the 'mlexperiments' package.

Details

Optimization metric: C-index Can be used with * [mlexperiments::MLTuneParameters] * [mlexperiments::MLCrossValidation] * [mlexperiments::MLNestedCVs]

Super class

mlexperiments::MLLearnerBase -> LearnerSurvGlmnetCox

Methods

Public methods

• LearnerSurvGlmnetCox$new()

• LearnerSurvGlmnetCox$clone()

Inherited methods

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

Method new()

Create a new 'LearnerSurvGlmnetCox' object.

Usage

LearnerSurvGlmnetCox$new()

Returns

A new 'LearnerSurvGlmnetCox' R6 object.

Examples

LearnerSurvGlmnetCox$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvGlmnetCox$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

[glmnet::glmnet()], [glmnet::cv.glmnet()]

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)]

param_list_glmnet <- expand.grid(
  alpha = seq(0, 1, .2)
)

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_glmnet_cox_optimizer <- mlexperiments::MLCrossValidation$new(
  learner = LearnerSurvGlmnetCox$new(),
  fold_list = fold_list,
  ncores = ncores,
  seed = seed
)
surv_glmnet_cox_optimizer$learner_args <- list(
  alpha = 0.8,
  lambda = 0.002
)
surv_glmnet_cox_optimizer$performance_metric <- c_index

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

surv_glmnet_cox_optimizer$execute()

## ------------------------------------------------
## Method `LearnerSurvGlmnetCox$new`
## ------------------------------------------------

LearnerSurvGlmnetCox$new()