generate.learningsets {MAclinical}R Documentation

Generating learning sets

Description

This function generates a design matrix giving the indices of observations forming the learning data set for several iterations.

Usage

generate.learningsets(n,method,fold=NULL,niter=NULL,nlearn=NULL)

Arguments

n The total number of observations in the available data set.
method One of "LOOCV" (leave-one-out cross-validation),"CV" (cross-validation),"MCCV" (Monte-Carlo cross-validation, also called subsampling),"bootstrap" (bootstrap sampling - with replacement).
fold Gives the number of CV-groups. Used only when method="CV".
niter Number of iterations.
nlearn Number of observations in the learning sets. Used only for method="MCCV" and method="bootstrap". When method="bootstrap", the default is nlearn=n.

Details

  • When method="CV", niter gives the number of times the whole CV-procedure is repeated. The output matrix has then foldxniter rows. When method="MCCV" or method="bootstrap", niter is simply the number of considered learning sets.
  • Note that method="CV",fold=n is equivalent to method="LOOCV".

    Value

    A matrix giving the indices (from 1 to n) of the observations included in the learning sets. Each row corresponds to a learning set. The order of the columns is not important. The number of rows is equal to n when method="LOOCV", niter when method="MCCV" or method="bootstrap", fold when method="CV" and niter is null, and fold x niter when method="CV" and niter is non-null.

    Author(s)

    Anne-Laure Boulesteix (http://www.slcmsr.net/boulesteix)

    References

    Boulesteix AL, Porzelius C, Daumer M, 2008. Microarray-based classification and clinical predictors: On combined classifiers and additional predictive value. Bioinformatics 24:1698-1706.

    See Also

    testclass.

    Examples

    # load MAclinical library
    # library(MAclinical)
    
    # LOOCV
    generate.learningsets(n=40,method="LOOCV")
    
    # CV
    generate.learningsets(n=40,method="CV",fold=5)
    generate.learningsets(n=40,method="CV",fold=5,niter=3)
    
    # MCCV
    generate.learningsets(n=40,method="MCCV",niter=3,nlearn=30)
    
    # bootstrap
    generate.learningsets(n=40,method="bootstrap",niter=3)
    

    [Package MAclinical version 1.0-2 Index]