get_data_forecast: Set up data for explain_forecast

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

Set up data for explain_forecast

Description

Set up data for explain_forecast

Usage

get_data_forecast(
  y,
  xreg,
  train_idx,
  explain_idx,
  explain_y_lags,
  explain_xreg_lags,
  horizon
)

Arguments

y	Matrix, data.frame/data.table or a numeric vector. Contains the endogenous variables used to estimate the (conditional) distributions needed to properly estimate the conditional expectations in the Shapley formula including the observations to be explained.
xreg	Matrix, data.frame/data.table or a numeric vector. Contains the exogenous variables used to estimate the (conditional) distributions needed to properly estimate the conditional expectations in the Shapley formula including the observations to be explained. As exogenous variables are used contemporaneously when producing a forecast, this item should contain nrow(y) + horizon rows.
train_idx	Numeric vector. The row indices in data and reg denoting points in time to use when estimating the conditional expectations in the Shapley value formula. If train_idx = NULL (default) all indices not selected to be explained will be used.
explain_idx	Numeric vector. The row indices in data and reg denoting points in time to explain.
explain_y_lags	Numeric vector. Denotes the number of lags that should be used for each variable in y when making a forecast.
explain_xreg_lags	Numeric vector. If xreg != NULL, denotes the number of lags that should be used for each variable in xreg when making a forecast.
horizon	Numeric. The forecast horizon to explain. Passed to the predict_model function.

Value

A list containing

• The data.frames x_train and x_explain which holds the lagged data examples.

• A numeric, n_endo denoting how many columns are endogenous in x_train and x_explain.

• A list, group with groupings of each variable to explain per variable and not per variable and lag.

shapr documentation built on April 4, 2025, 12:18 a.m.