CortForest-Class: Bagged Cort copulas

In cort: Some Empiric and Nonparametric Copula Models

Description

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

\insertRef

laverny2020cort

Examples

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

cort documentation built on Jan. 13, 2021, 8:57 p.m.