Explain.barp: Approximate Shapley Values Computed from the BARP Model
In bartXViz: Visualization of BART and BARP using SHAP

Explain.barp

R Documentation

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 class 'barp'
Explain(
  object,
  feature_names = NULL,
  X = NULL,
  nsim = 1,
  pred_wrapper = NULL,
  census = NULL,
  geo.unit = NULL,
  parallel = FALSE,
  ...
)

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

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`.