copulareg {copulaboost}

copulareg

Description

This function fits joint distributions with an R-vine pair copula structure, that is constructed in a specific way so that the conditional density and distribution of the variable y can be computed explicitly.

Usage

copulareg(
  y,
  x,
  var_type_y,
  var_type_x,
  distr_x = NULL,
  distr_y = NULL,
  dvine = FALSE,
  family_set = c("gaussian", "clayton", "gumbel")
)

Arguments

y

A vector of n observations of the (univariate) outcome variable y

x

A (n x p) matrix of n observations of p covariates

var_type_y

A character that has to be specified as "d" or "c" to indicate whether y is discrete or continuous, respectively.

var_type_x

A vector of p characters that have to take the value "c" or "d" to indicate whether each margin of the covariates is discrete or continuous.

distr_x

Internally created object that contains a nested list. The first element of the 'outer' list is a list where each element is a distribution object similar to that created by ecdf, the second element is a function 'transform' that takes a matrix of values of x, and returns the corresponding cumulative distributions F(x).

distr_y

Similar to distr_x, but for the outcome y.

dvine

Logical variable indicating whether the function should fit a canonical d-vine to (y, x) with the ordering (x_1, ..., x_p, y).

family_set

A vector of strings that specifies the set of pair-copula families that the fitting algorithm chooses from. For an overview of which values that can be specified, see the documentation for bicop.

Value

A copulareg object. Consists of 'model', an rvinecopulib 'vinecop' object for the copula-model for (x_1, ..., x_p, y), a hash table containing all of the conditionals for the model (for the training set), objects distr_x and distr_y that contain the marginal distributions of the covariates and the outcome, and y, the y-values for the training data.