Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/generate.learningsets.r

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

1 | ```
generate.learningsets(n,method,fold=NULL,niter=NULL,nlearn=NULL)
``` |

`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 |

`niter` |
Number of iterations. |

`nlearn` |
Number of observations in the learning sets. Used only for |

When `method="CV"`

, `niter`

gives the number of times
the whole CV-procedure is repeated. The output matrix has then `fold`

x`niter`

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"`

.

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.

Anne-Laure Boulesteix (http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/eng.html)

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# 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)
``` |

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