plot_partial_dependencies: Function for plotting the partial dependencies of the created...

In AlexAfanasev/NeuralNetworkVisualization: Create Partial Dependence Plots for Neural Networks, Visualizes the marginal effects of Neural Networks

Description

plot_partial_dependencies creates the partial dependence plot for the specified predictors.

Usage

plot_partial_dependencies(neural_net, predictors = "all", probs = c(0,
  0), type = "ggplot", nrepetitions = 50, parallel = FALSE,
  use_stored_data = FALSE)

Arguments

neural_net	The NeuralNetwork instance, see: NeuralNetwork
predictors	Vector of predictors of the neural network for which to plot the partial dependencies.
probs	Vector of lower and upper bound probabilities for the confidence interval. If both are 0, intervals will not be plotted.
type	Either 'ggplot' if the plot should be created using 'ggplot' or 'ggplotly' if plotly should be used.
nrepetitions	Number of samples used within bootstrap for confidence intervals.
parallel	Boolean specifying if for the bootstrap confidence interval the plotting data creation should be parallelized.
use_stored_data	Boolean specifying if the stored data within a model should be used. Raises an error if no stored data is available.

Value

Created figure

Examples

## Not run: 
# Example: Numeric
library(MASS)
neural_network <- NeuralNetwork(f = medv ~ ., data = Boston,
                                layers = c(5, 3), scale = TRUE,
                                linear.output = TRUE)

plot_partial_dependencies(neural_network, predictors = "crim",
                          probs = c(0.2, 0.8), type = "ggplotly")
plot_partial_dependencies(neural_network, predictors = c("crim", "age"))
plot_partial_dependencies(neural_network, probs = c(0.1, 0.9))

# Example: Categoric or Binary
library(datasets)
model <- NeuralNetwork(
   Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
   data = iris, layers = c(10, 10), rep = 5, err.fct = "ce",
   linear.output = FALSE, lifesign = "minimal", stepmax = 1000000,
   threshold = 0.001, scale = FALSE)

plot_partial_dependencies(model, predictors = "Petal.Length")
plot_partial_dependencies(model,
                          predictors = c("Sepal.Length", "Petal.Length"))
plot_partial_dependencies(model, type = "ggplotly")

## End(Not run)

AlexAfanasev/NeuralNetworkVisualization documentation built on Sept. 23, 2019, 2:29 a.m.