Create Visualisations for BART Models
Determines the stump color for a legend based on its mean value
plotProximity
acceptRate
bartClassifDiag
bartDiag
bartRegrDiag
Constructor for bivariate range object
Constructor for bivariate scale object
Cluster Trees by Variable
Update Dummy Variable Names
extractTreeData
Generate Child and Parent Node Relationships
Get Observations Falling into Each Node
Colourfan guide
localProcedure
plotSingleTree
mdsBart
Calculate Node Depths in a Tree Data Frame
Variance suppressing uncertainty palette
permVimp
permVint
Plot Trees with Customisations
print.hideHelper
proximityMatrix
Sort Trees by Maximum Depth
splitDensity
Generate Terminal Node Indicator
Train range for bivariate scale
Transform tree data into a structured dataframe
Plot Frequency of Tree Structures
treeDepth
Generate a List of Tree Structures from BART Model Output
treeNodes
vimpBart
vimpPlot
vintPlot
viviBart
viviBartMatrix
viviBartPlot
Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>, 'dbarts' (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>, and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.