Description Usage Arguments Value References Examples

Estimates Boosting of Smooth Trees (BooST)

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`x` |
Design matrix with explanatory variables. |

`y` |
Response variable. |

`v` |
Learning rate (default 0.2). |

`p` |
Proportion of variables tested in each node split (default 2/3). |

`d_max` |
Number of splits in each tree (default 4). |

`gamma` |
Transiction function intensity. Bigger numbers makes the transition less smoth. The default is a sequence of values (0.5:5) to be randomized in each new node. Multiple values may be supplied in a vector to increase the model randomness. |

`M` |
Number of trees. |

`display` |
If TRUE, displays iteration counter. |

`stochastic` |
If TRUE the model will be estimated using Stochasting Gradient Boosting. |

`s_prop` |
Used only if stochastic=TRUE. Determines the proportion of data used in each tree. |

`node_obs` |
Equivalent to the minimum number of observations in a termina node for a discrete tree. |

`random` |
If TRUE trees are grown randomly (default = FALSE) |

An object with S3 class "Boost".

`Model` |
A list with all trees. |

`fitted.values` |
Final model fitted values. |

`brmse` |
Boost rmse in each iteratiob. |

`Model` |
A list with all trees. |

`ybar` |
Average value of y used in the first iteration. |

`v` |
Chosen learning rate. |

`rho` |
Vector of gradient estimates for each iteration. |

`nvar` |
Numver of variables in x |

`varnames` |
colnames of x to be used in other functions. |

`params` |
Model parameters. |

`call` |
The matched call. |

blablabla

1 | ```
## == to be made == ##
``` |

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