survSuperLearner.CV.control: Control parameters for the cross validation steps in survival...

In tedwestling/survSuperLearner: Super Learning for Conditional Survival Functions with Right-Censored Data

Control parameters for the cross validation steps in survival Super Learner

Description

This function initiates control parameters for the cross-validation in survSuperLearner function.

Usage

survSuperLearner.CV.control(
  V = 10L,
  stratifyCV = TRUE,
  shuffle = TRUE,
  validRows = NULL
)

Arguments

V	Number of cross-validation folds. Defaults to 10.
stratifyCV	Logical indicating whether to balance number of observed events across folds. Defaults to TRUE.
shuffle	Logical indicating whether to shuffle the indices, or to simply assign sequentially. Defaults to TRUE. Should almost always be set to TRUE unless it is explicitly desired to assign sequentially.
validRows	Optional custom list of indices for validation folds.

Value

Returns a list of length V with validation indices for each of the folds.

tedwestling/survSuperLearner documentation built on Dec. 12, 2024, 4:16 p.m.