Description Usage Arguments Value See Also

Cross-validated estimation of the empirical misclassification error for boosting parameter selection.

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`x` |
a data matrix containing the variables in the model. |

`y` |
vector of multi class responses. |

`balance` |
logical value. If TRUE, The K parts were roughly balanced, ensuring that the classes were distributed proportionally among each of the K parts. |

`K` |
K-fold cross-validation |

`nu` |
a small number (between 0 and 1) defining the step size or shrinkage parameter. |

`mstop` |
number of boosting iteration. |

`interaction.depth` |
used in gbm to specify the depth of trees. |

`trace` |
if TRUE, iteration results printed out. |

`plot.it` |
a logical value, to plot the cross-validation error if |

`se` |
a logical value, to plot with 1 standard deviation curves. |

`...` |
additional arguments. |

object with

`residmat ` |
empirical risks in each cross-validation at boosting iterations |

`fraction` |
abscissa values at which CV curve should be computed. |

`cv` |
The CV curve at each value of fraction |

`cv.error` |
The standard error of the CV curve |

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