prepare_data_copula_cpp_caus: Generate (Gaussian) Copula MC samples for the causal setup...
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

prepare_data_copula_cpp_caus

R Documentation

Generate (Gaussian) Copula MC samples for the causal setup with a single MC sample for each explicand

Description

Generate (Gaussian) Copula MC samples for the causal setup with a single MC sample for each explicand

Usage

prepare_data_copula_cpp_caus(
  MC_samples_mat,
  x_explain_mat,
  x_explain_gaussian_mat,
  x_train_mat,
  S,
  mu,
  cov_mat
)

Arguments

`MC_samples_mat`	arma::mat. Matrix of dimension (`n_MC_samples`, `n_features`) containing samples from the univariate standard normal.
`x_explain_mat`	arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the observations to explain.
`x_explain_gaussian_mat`	arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the observations to explain after being transformed using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution.
`x_train_mat`	arma::mat. Matrix of dimension (`n_train`, `n_features`) containing the training observations.
`S`	arma::mat. Matrix of dimension (`n_coalitions`, `n_features`) containing binary representations of the used coalitions. S cannot contain the empty or grand coalition, i.e., a row containing only zeros or ones. This is not a problem internally in shapr as the empty and grand coalitions are treated differently.
`mu`	arma::vec. Vector of length `n_features` containing the mean of each feature after being transformed using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution.
`cov_mat`	arma::mat. Matrix of dimension (`n_features`, `n_features`) containing the pairwise covariance between all pairs of features after being transformed using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution.

Value

An arma::cube/3D array of dimension (n_MC_samples, n_explain * n_coalitions, n_features), where the columns (,j,) are matrices of dimension (n_MC_samples, n_features) containing the conditional Gaussian copula MC samples for each explicand and coalition on the original scale.