CortForest-Class {cort} | R Documentation |

CortForest class

```
CortForest(
x,
p_value_for_dim_red = 0.75,
n_trees = 10,
compte_loo_weights = FALSE,
min_node_size = 1,
pseudo_data = FALSE,
number_max_dim = NULL,
verbose_lvl = 2,
force_grid = FALSE,
oob_weighting = TRUE
)
```

`x` |
The data, must be provided as a matrix with each row as an observation. |

`p_value_for_dim_red` |
a p_value for the localised dimension reduction test |

`n_trees` |
Number of trees |

`compte_loo_weights` |
Defaults to FALSE. Allows to use an automatic re-weighting of the trees in the forest, based on leave-one-out considerations. |

`min_node_size` |
The minimum number of observation avaliable in a leaf to initialise a split. |

`pseudo_data` |
set to True if you are already providing data on the copula space. |

`number_max_dim` |
The maximum number of dimension a split occurs in. Defaults to be all of the dimensions. |

`verbose_lvl` |
verbosity level : can be 0 (default) or an integer. bigger the integer bigger the output level. |

`force_grid` |
boolean (default: FALSE). set to TRUE to force breakpoint to be on the n-checkerboard grid in every tree. |

`oob_weighting` |
boolean (default : TRUE) option to weight the trees with an oob criterion (otherwise they are equally weighted) |

This class implements the bagging of CORT models, with an out-of-bag error minimisation in the weights.

See O. Laverny, V. Maume-Deschamps, E. Masiello and D. Rullière (2020) for the details of this density estimation procedure, and `vignettes(package='cort')`

for examples of usecases.

An instance of the `CortForest`

S4 class. The object represent the fitted copula and can be used through several methods to query classical (r/d/p/v)Copula methods, constraint influence, etc.
Beside returning some inputted parameters, notable slots are :

`trees`

A list of Cort objects representing each fitted tree in the forest.`weights`

The weigths of each tree.`indexes`

The indexes of data points that were selected for fitting the trees`pmf`

The density of each tree on data points`norm_matrix`

The matrix of scalar product between trees`oob_pmf`

The density of each tree on data points it did not see during fitting`oob_kl`

The out-of-bag Kullback-Leibler divergence of each tree`oob_ise`

The out-of-bag Integrated Square Error of each tree

More details about these slots can be found in the reference.

Laverny O, Maume-Deschamps V, Masiello E, RulliÃ¨re D (2020).
“Dependence Structure Estimation Using Copula Recursive Trees.”
*arXiv preprint arXiv:2005.02912*.

```
(CortForest(LifeCycleSavings[,1:3],number_max_dim=2,n_trees=2))
```

[Package *cort* version 0.3.2 Index]