splitDensity function

splitDensity

Density plots of the split value for each variable.

splitDensity(
  trees,
  data,
  bandWidth = NULL,
  panelScale = NULL,
  scaleFactor = NULL,
  display = "histogram"
)

Arguments

• trees: A list of trees created using the trees function.
• data: Data frame containing variables from the model.
• bandWidth: Bandwidth used for density calculation. If not provided, is estimated from the data.
• panelScale: If TRUE, the default, relative scaling is calculated separately for each panel. If FALSE, relative scaling is calculated globally. @param scaleFactor A scaling factor to scale the height of the ridgelines relative to the spacing between them. A value of 1 indicates that the maximum point of any ridgeline touches the baseline right above, assuming even spacing between baselines.
• scaleFactor: A numerical value to scale the plot.
• display: Choose how to display the plot. Either histogram, facet wrap, ridges or display both the split value and density of the predictor by using dataSplit.

Returns

A faceted group of density plots

Examples

if(requireNamespace("dbarts", quietly = TRUE)){
 # Load the dbarts package to access the bart function
 library(dbarts)
 # Get Data
 df <- na.omit(airquality)
 # Create Simple dbarts Model For Regression:
 set.seed(1701)
 dbartModel <- bart(df[2:6], df[, 1], ntree = 5, keeptrees = TRUE, nskip = 10, ndpost = 10)

 # Tree Data
 trees_data <- extractTreeData(model = dbartModel, data = df)
 splitDensity(trees = trees_data, data = df, display = 'ridge')
}