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

Repeated Cross Validation for multiple linear regression: a cross-validation is performed repeatedly, and standard evaluation measures are returned.

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`formula` |
formula, like y~X, i.e., dependent~response variables |

`data` |
data set including y and X |

`repl` |
number of replication for Cross Validation |

`segments` |
number of segments used for splitting into training and test data |

`segment.type` |
"random", "consecutive", "interleaved" splitting into training and test data |

`length.seg` |
number of parts for training and test data, overwrites segments |

`trace` |
if TRUE intermediate results are reported |

`...` |
additional plotting arguments |

Repeating the cross-validation with allow for a more careful evaluation.

`residuals` |
matrix of size length(y) x repl with residuals |

`predicted` |
matrix of size length(y) x repl with predicted values |

`SEP` |
Standard Error of Prediction computed for each column of "residuals" |

`SEPm` |
mean SEP value |

`RMSEP` |
Root MSEP value computed for each column of "residuals" |

`RMSEPm` |
mean RMSEP value |

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

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