shapr (version 1.0.1)

create_marginal_data_training: Function that samples data from the empirical marginal training distribution

Description

Sample observations from the empirical distribution P(X) using the training dataset.

Usage

create_marginal_data_training(
  x_train,
  n_explain,
  Sbar_features,
  n_MC_samples = 1000,
  stable_version = TRUE
)

Value

Data table of dimension n_MC_samples

\(\times\)

length(Sbar_features) with the sampled observations.

Arguments

x_train

Data.table with training data.

Sbar_features

Vector of integers containing the features indices to generate marginal observations for. That is, if Sbar_features is c(1,4), then we sample n_MC_samples observations from \(P(X_1, X_4)\) using the empirical training observations (with replacements). That is, we sample the first and fourth feature values from the same training observation, so we do not break the dependence between them.

stable_version

Logical. If TRUE and n_MC_samples > n_train, then we include each training observation n_MC_samples %/% n_train times and then sample the remaining n_MC_samples %% n_train samples. Only the latter is done when n_MC_samples < n_train. This is done separately for each explicand. If FALSE, we randomly sample the from the observations.

Author

Lars Henry Berge Olsen

Examples

if (FALSE) {
data("airquality")
data <- data.table::as.data.table(airquality)
data <- data[complete.cases(data), ]

x_var <- c("Solar.R", "Wind", "Temp", "Month")
y_var <- "Ozone"

ind_x_explain <- 1:6
x_train <- data[-ind_x_explain, ..x_var]
x_train
shapr:::create_marginal_data_training(
  x_train = x_train,
  Sbar_features = c(1, 4),
  n_MC_samples = 10
)
}