Explain.barp: Approximate Shapley Values Computed from the BARP Model

Description

This function is implemented to calculate the contribution of each variable in the BARP (Bayesian Additive Regression Tree with post-stratification) model using the permutation method.

Usage

# S3 method for barp
Explain(
  object,
  feature_names = NULL,
  X = NULL,
  nsim = 1,
  pred_wrapper = NULL,
  census = NULL,
  geo.unit = NULL,
  parallel = FALSE,
  ...
)

Value

Returns of class Explainbarp with consisting of a list with the following components:

phis: A list containing the Shapley values for each variable.
newdata: The data used to check the contribution of variables. If a variable has two categories, it is dummy-coded, and if it has three or more categories, categorical variables are one-hot encoded.
fnull: The expected value of the model's predictions.
fx: The prediction value for each observation.
factor_names: The name of the categorical variable. If the data contains only continuous or dummy variables, it is set to NULL.

Arguments

object: A BARP model (Bayesian Additive Regression Tree) estimated using the barps function, a modified version of the barp function from the BARP library with a fixed seed.
feature_names: The name of the variable for which you want to check the contribution. The default value is set to NULL, which means the contribution of all variables in X will be calculated.
X: The dataset containing all independent variables used as input when estimating the BART model. The explanatory variables X included in the model must be converted to factors prior to input.
nsim: The number of Monte Carlo sampling iterations, which is fixed at 1 by default in the case of the BARP model.
pred_wrapper: A function used to estimate the predicted values of the model.
census: Census data containing the names of the X columns. It should also have the same format as X and include a variable named `proportion`, which indicates the number of individuals corresponding to each combination.
geo.unit: Enter the name of the stratification variable used in post stratification.
parallel: The default value is set to FALSE, but it can be changed to TRUE for parallel computation.
...: Additional arguments to be passed