Description Usage Arguments Details Value See Also Examples

This function trains a model fit by `owl()`

by tuning its parameters
through cross-validation.

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`x` |
the feature matrix, which can be either a dense
matrix of the standard |

`y` |
the response. For Gaussian models this must be numeric; for binomial models, it can be a factor. |

`q` |
shape of lambda sequence |

`number` |
number of folds (cross-validation) |

`repeats` |
number of repeats for each fold (for repeated |

`measure` |
measure to try to optimize; note that you may
supply |

`cl` |
cluster if parallel fitting is desired. Can be any
cluster accepted by |

`...` |
other arguments to pass on to |

Note that by default this method matches all of the available metrics
for the given model family against those provided in the argument
`measure`

. Collecting these measures is not particularly demanding
computationally so it is almost always best to leave this argument
as it is and then choose which argument to focus on in the call
to `plot.TrainedOwl()`

.

An object of class `"TrainedOwl"`

, with the following slots:

`summary` |
a summary of the results with means, standard errors, and 0.95 confidence levels |

`data` |
the raw data from the model training |

`optima` |
a |

`measure` |
a |

`model` |
the model fit to the entire data set |

`call` |
the call |

parallel::parallel, `plot.TrainedOwl()`

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