blackboost_train | R Documentation |

`blackboost_train()`

is a wrapper for the `blackboost()`

function in the
mboost package that fits tree-based models
where all of the model arguments are in the main function.

blackboost_train( formula, data, family, weights = NULL, teststat = "quad", testtype = "Teststatistic", mincriterion = 0, minsplit = 10, minbucket = 4, maxdepth = 2, saveinfo = FALSE, ... )

`teststat` |
a character specifying the type of the test statistic to be applied for variable selection. |

`testtype` |
a character specifying how to compute the distribution of the test statistic. The first three options refer to p-values as criterion, Teststatistic uses the raw statistic as criterion. Bonferroni and Univariate relate to p-values from the asymptotic distribution (adjusted or unadjusted). Bonferroni-adjusted Monte-Carlo p-values are computed when both Bonferroni and MonteCarlo are given. |

`mincriterion` |
the value of the test statistic or 1 - p-value that must be exceeded in order to implement a split. |

`minsplit` |
the minimum sum of weights in a node in order to be considered for splitting. |

`minbucket` |
the minimum sum of weights in a terminal node. |

`maxdepth` |
maximum depth of the tree. The default maxdepth = Inf means that no restrictions are applied to tree sizes. |

`saveinfo` |
logical. Store information about variable selection procedure in info slot of each partynode. |

`...` |
Other arguments to pass. |

`x` |
A data frame or matrix of predictors. |

`y` |
A factor vector with 2 or more levels |

A fitted blackboost model.

blackboost_train(Surv(time, status) ~ age + ph.ecog, data = lung[-14, ], family = mboost::CoxPH())

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