Description Usage Arguments Author(s) References

This function shows the cross-validated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested cross-validation procedure.

1 |

`trainx, ` |
matrix or data frame containing columns of predictor variables |

`trainoffset, ` |
vector of offset, must have length equal to the number of rows in |

`trainy, ` |
vector of response, must have length equal to the number of rows in |

`cv.fold, ` |
number of folds in the cross-validation |

`scale, ` |
if |

`step, ` |
if |

`mtry, ` |
a function of number of remaining predictor variables to use as the |

`recursive, ` |
whether variable importance is (re-)assessed at each step of variable reduction |

`..., ` |
other arguments passed on to |

Andy Liaw

Svetnik, V., Liaw, A., Tong, C. and Wang, T., “Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules”, MCS 2004, Roli, F. and Windeatt, T. (Eds.) pp. 334-343.

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