Plot of the Shapley value explanations

Plots the individual prediction explanations.

# S3 method for shapr
plot(
  x,
  digits = 3,
  plot_phi0 = TRUE,
  index_x_test = NULL,
  top_k_features = NULL,
  ...
)

Arguments

x: An shapr object. See explain.
digits: Integer. Number of significant digits to use in the feature description
plot_phi0: Logical. Whether to include phi0 in the plot
index_x_test: Integer vector. Which of the test observations to plot. E.g. if you have explained 10 observations using explain, you can generate a plot for the first 5 observations by setting index_x_test = 1:5.
top_k_features: Integer. How many features to include in the plot. E.g. if you have 15 features in your model you can plot the 5 most important features, for each explanation, by setting top_k_features = 1:5.
...: Currently not used.

Value

ggplot object with plots of the Shapley value explanations

Details

See vignette("understanding_shapr", package = "shapr") for an example of how you should use the function.

Author

Martin Jullum

Examples

if (requireNamespace("MASS", quietly = TRUE)) {
  #' # Load example data
  data("Boston", package = "MASS")

  # Split data into test- and training data
  x_train <- head(Boston, -3)
  x_test <- tail(Boston, 3)

  # Fit a linear model
  model <- lm(medv ~ lstat + rm + dis + indus, data = x_train)

  # Create an explainer object
  explainer <- shapr(x_train, model)

  # Explain predictions
  p <- mean(x_train$medv)

  # Empirical approach
  explanation <- explain(x_test,
    explainer,
    approach = "empirical",
    prediction_zero = p,
    n_samples = 1e2
  )

  if (requireNamespace("ggplot2", quietly = TRUE)) {
    # Plot the explantion (this function)
    plot(explanation)
  }
}
#> 
#> Success with message:
#> The columns(s) crim, zn, chas, nox, age, rad, tax, ptratio, black, medv is not used by the model and thus removed from the data.
#> 
#> Success with message:
#> The columns(s) crim, zn, chas, nox, age, rad, tax, ptratio, black, medv is not used by the model and thus removed from the data.