plot_variable_transition: Plot variable profile

In krystian8207/xspliner: Assisted Model Building, using Surrogate Black-Box Models to Train Interpretable Spline Based Additive Models

Description

The function plots variable profile. In case of quantiitative variable it plots original transition function and its spline approximation. The function provides possibility to plot data points and transition derivative as well. In case of qualitative variable it plots merging path for variable levels. When no variable is specified it plots transitions for first n_plots variables.

Usage

plot_variable_transition(x, variable_names = NULL,
  plot_response = TRUE, plot_approx = TRUE, data = NULL,
  plot_data = FALSE, plot_deriv = FALSE, n_plots = 6,
  use_coeff = TRUE)

Arguments

x	Object of class 'xspliner'.
variable_names	Names of predictors which transitions should be plotted.
plot_response	If TRUE black box model response is drawn.
plot_approx	If TRUE black box model response approximation is drawn.
data	Training data used for building x model. Required for plot_data option.
plot_data	If TRUE raw data is drawn.
plot_deriv	If TRUE derivative of approximation is showed on plot.
n_plots	Threshold for number of plots when plotting all variables.
use_coeff	If TRUE both PDP function and its approximation is scaled with corresponding surrogate model coefficient.

Examples

library(randomForest)
set.seed(1)
data <- iris
# regression model
iris.rf <- randomForest(Petal.Width ~  Sepal.Length + Petal.Length + Species, data = data)
iris.xs <- xspline(iris.rf)
# plot Sepal.Length transition
plot_variable_transition(iris.xs, "Sepal.Length")
# plot Species transition
plot_variable_transition(iris.xs, "Species")
# plot all transitions
plot_variable_transition(iris.xs)
# plot Sepal.Length transition, its derivative and data points
plot_variable_transition(iris.xs, "Sepal.Length", data = data, plot_data = TRUE, plot_deriv = TRUE)

krystian8207/xspliner documentation built on Oct. 6, 2019, 11:02 p.m.