k-fold cross validation for the selection of the number of components for partial robust M regression.

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`formula` |
an object of class formula. |

`data` |
a data frame or list which contains the variables given in formula. |

`as` |
a vector with positive integers, which are the number of PRM components to be estimated in the models. |

`nfold` |
the number of folds used for cross validation, default is |

`fun` |
an internal weighting function for case weights. Choices are |

`probp1` |
the 1-alpha value at which to set the first outlier cutoff for the weighting function. |

`hampelp2` |
the 1-alpha values for second cutoff. Only applies to |

`hampelp3` |
the 1-alpha values for third cutoff. Only applies to |

`center` |
type of centering of the data in form of a string that matches an R function, e.g. |

`scale` |
type of scaling for the data in form of a string that matches an R function, e.g. |

`usesvd` |
logical, default is |

`plot` |
logical, default is |

`numit` |
the number of maximal iterations for the convergence of the coefficient estimates. |

`prec` |
a value for the precision of estimation of the coefficients. |

`alpha` |
value used for alpha trimmed mean squared error, which is the cross validation criterion (see Details). |

The `alpha`

- trimmed mean squared error of the predicted response over all observations is used as robust decision criterion to choose the optimal model. For `plot=TRUE`

a graphic visualizes the `alpha`

- trimmed mean squared error for each model.

`opt.mod` |
object of class prm. (see |

`spe` |
matrix with squared prediction error for each observation and each number of components. |

Irene Hoffmann

Hoffmann, I., Serneels, S., Filzmoser, P., Croux, C. (2015). Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, 50-59.

Serneels, S., Croux, C., Filzmoser, P., Van Espen, P.J. (2005). Partial Robust M-Regression. Chemometrics and Intelligent Laboratory Systems, 79, 55-64.

`prms`

, `plot.prm`

, `predict.prm`

, `sprmsCV`

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