resolvesplitMethod: Resolve the splitMethod for estimation of prediction...

In pec: Prediction Error Curves for Risk Prediction Models in Survival Analysis

Resolve the splitMethod for estimation of prediction performance

Description

The function computes a matrix of random indices obtained by drawing from the row numbers of a data set either with or without replacement. The matrix can be used to repeatedly set up independent training and validation sets.

Usage

resolvesplitMethod(splitMethod, B, N, M)

Arguments

splitMethod	String that determines the splitMethod to use. Available splitMethods are none/noPlan (no splitting), bootcv or outofbag (bootstrap cross-validation), cvK (K-fold cross-validation, e.g. cv10 gives 10-fold), boot632, boot632plus or boot632+, loocv (leave-one-out)
B	The number of repetitions.
N	The sample size
M	For subsampling bootstrap the size of the subsample. Note M<N.

Value

A list with the following components

name	the official name of the splitMethod
internal.name	the internal name of the splitMethod
index	a matrix of indices with B columns and either N or M rows, dependent on splitMethod
B	the value of the argument B
N	the value of the argument N
M	the value of the argument M

Author(s)

Thomas Alexander Gerds [e-mail]

Examples

  # BootstrapCrossValidation: Sampling with replacement   
  resolvesplitMethod("BootCv",N=10,B=10)

  # 10-fold cross-validation: repeated 2 times
  resolvesplitMethod("cv10",N=10,B=2)

  # leave-one-out cross-validation
  resolvesplitMethod("loocv",N=10)

  resolvesplitMethod("bootcv632plus",N=10,B=2)
  
  

pec documentation built on April 11, 2023, 5:55 p.m.