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

Performs repeated cross-validation (CV) to evaluate the result of Ridge regression where the optimal Ridge parameter lambda was chosen on a fast evaluation scheme.

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

`data` |
data frame to be analyzed |

`lambdaopt` |
optimal Ridge parameter lambda |

`repl` |
number of replications for the CV |

`segments` |
the number of segments to use for CV,
or a list with segments (see |

`segment.type` |
the type of segments to use. Ignored if 'segments' is a list |

`length.seg` |
Positive integer. The length of the segments to use. If specified, it overrides 'segments' unless 'segments' is a list |

`trace` |
logical; if 'TRUE', the segment number is printed for each segment |

`plot.opt` |
if TRUE a plot will be generated that shows the predicted versus the observed y-values |

`...` |
additional plot arguments |

Generalized Cross Validation (GCV) is used by the function
`lm.ridge`

to get a quick answer for the optimal Ridge parameter.
This function should make a careful evaluation once the optimal parameter lambda has
been selected. Measures for the prediction quality are computed and optionally plots
are shown.

`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 |

`sMAD` |
MAD of Prediction computed for each column of "residuals" |

`sMADm` |
mean of MAD values |

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

`RMSEPm` |
mean RMSEP value |

Peter Filzmoser <[email protected]>

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

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