gpb.plot.partial.dependence: Plot partial dependence plots

Description

Plot partial dependence plots

Usage

gpb.plot.partial.dependence(model, data, variable, n.pt = 100,
  subsample = pmin(1, n.pt * 100/nrow(data)), discrete.x = FALSE,
  which.class = NULL, xlab = deparse(substitute(variable)), ylab = "",
  type = if (discrete.x) "p" else "b", main = "",
  return_plot_data = FALSE, ...)

Value

A two-dimensional matrix with data for creating the partial dependence plot. This is only returned if return_plot_data==TRUE

Arguments

model: A gpb.Booster model object
data: A matrix with data for creating partial dependence plots
variable: A string with a name of the column or an integer with an index of the column in data for which a dependence plot is created
n.pt: Evaluation grid size (used only if x is not discrete)
subsample: Fraction of random samples in data to be used for calculating the partial dependence plot
discrete.x: A boolean. If TRUE, the evaluation grid is set to the unique values of x
which.class: An integer indicating the class in multi-class classification (value from 0 to num_class - 1)
xlab: Parameter passed to plot
ylab: Parameter passed to plot
type: Parameter passed to plot
main: Parameter passed to plot
return_plot_data: A boolean. If TRUE, the data for creating the partial dependence plot is returned
...: Additional parameters passed to plot

Author

Fabio Sigrist (adapted from a version by Michael Mayer)

Examples

Run this code

library(gpboost)
data(GPBoost_data, package = "gpboost")

gp_model <- GPModel(group_data = group_data[,1], likelihood = "gaussian")
gpboost_model <- gpboost(data = X,
                         label = y,
                         gp_model = gp_model,
                         nrounds = 16,
                         learning_rate = 0.05,
                         max_depth = 6,
                         min_data_in_leaf = 5,
                         objective = "regression_l2",
                         verbose = 0)
gpb.plot.partial.dependence(gpboost_model, X, variable = 1)