CortForest-Class {cort} R Documentation

## Bagged Cort copulas

CortForest class

### Usage

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
)


### Arguments

 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)

### Details

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.

### Value

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.

### References

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

### Examples

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


[Package cort version 0.3.2 Index]