Description Usage Arguments Examples

Main function to calculate stability coefficients

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`formula` |
a formula, weight a response to left of ~. |

`data` |
Data frame to run models on |

`methods` |
Which tree methods to use. Defaults: lm, rpart, tree, ctree, evtree. Also can use "rf" for random forests |

`samp.method` |
Sampling method. Refer to caret package trainControl() documentation. Default is repeated cross-validation. Other options include "cv" and "boot". |

`tuneLength` |
Number of tuning parameters to try. Applies to train() |

`n.rep` |
Number of times to replicate each method |

`bump.rep` |
Number of repetitions for bumping |

`parallel` |
Whether to run all reps in parallel |

`ncore` |
Number of cores to use |

`roundVal` |
How much to round cut points when calculating stability |

`stablelearner` |
Whether or not to use the stablelearner package to calculate stability |

`subset` |
Whether to subset |

`perc.sub` |
What fraction of data to put into train dataset. 1-frac.sub is allocated to test dataset. Defaults to 0.75 |

`weights` |
Optional weights for each case. |

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