new_shapley_algorithm: Class constructor for class `shapley_algorithm`.

Description

This function creates an object of class shapley_algorithm that is returned by the SCD and MOE functions.

Usage

new_shapley_algorithm(
  x = numeric(),
  phi = numeric(),
  x_original = numeric(),
  mu_tilde = NULL,
  non_centrality = NULL,
  x_history = NULL,
  phi_history = NULL,
  mu_tilde_history = NULL,
  S_history = NULL
)

Value

Named list of class shapley_algorithm, containing the input parameters.

Arguments

x: A \(p\)-dimensional vector (or a \(n \times p\) matrix) containing the imputed data.
phi: A \(p\)-dimensional vector (or a \(n \times p\) matrix) containing the Shapley values (outlyingness-scores) of a \(p\)-dimensional data vector (or a \(n \times p\) data matrix).
x_original: A \(p\)-dimensional vector (or a \(n \times p\) matrix) containing the original data.
mu_tilde: Optional. A \(p\)-dimensional vector (or a \(n \times p\) matrix) containing the alternative reference points based on the regular cells of the original observations.
non_centrality: Optional. The non-centrality parameters for the Chi-Squared distribution, which are given by mahlanobis(mu_tilde, mu, Sigma).
x_history: Optional. A list with \(n\) elements, each containing the path of how the original data vector was modified.
phi_history: Optional. A list with \(n\) elements, each containing the Shapley values corresponding to x_history.
mu_tilde_history: Optional. A list with \(n\) elements, each containing the alternative reference points corresponding to x_history.
S_history: Optional. A list with \(n\) elements, each containing the indices of the outlying cells in each iteration.