splitDensity: splitDensity

In AlanInglis/BartVis: Create Visualisations for BART Models

splitDensity

Description

Density plots of the split value for each variable.

Usage

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.

Value

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')
}

AlanInglis/BartVis documentation built on July 27, 2024, 12:02 a.m.