shapr (version 0.1.3)

plot.shapr: Plot of the Shapley value explanations

Description

Plots the individual prediction explanations.

Usage

# 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.

Examples

# NOT RUN {
#' # 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)

# Plot the explantion (this function)
plot(explanation)

# }