plot_convergence_diagnostics: Plot Convergence Diagnostics

In bartMachine: Bayesian Additive Regression Trees

Plot Convergence Diagnostics

Description

A suite of plots to assess convergence diagonstics and features of the BART model.

Usage

plot_convergence_diagnostics(bart_machine,  
plots = c("sigsqs", "mh_acceptance", "num_nodes", "tree_depths"))

Arguments

bart_machine	An object of class “bartMachine”.
plots	The list of plots to be displayed. The four options are: "sigsqs", "mh_acceptance", "num_nodes", "tree_depths".

Details

The “sigsqs” option plots the posterior error variance estimates by the Gibbs sample number. This is a standard tool to assess convergence of MCMC algorithms. This option is not applicable to classification BART models.
The “mh_acceptance” option plots the proportion of Metropolis-Hastings steps accepted for each Gibbs sample (number accepted divided by number of trees).
The “num_nodes” option plots the average number of nodes across each tree in the sum-of-trees model by the Gibbs sample number (for post burn-in only). The blue line is the average number of nodes over all trees.
The “tree_depths” option plots the average tree depth across each tree in the sum-of-trees model by the Gibbs sample number (for post burn-in only). The blue line is the average number of nodes over all trees.

Value

None.

Note

The “sigsqs” plot separates the burn-in \sigma^2's for the first core by post burn-in \sigma^2's estimates for all cores by grey vertical lines. The “mh_acceptance” plot separates burn-in from post-burn in by a grey vertical line. Post burn-in, the different core proportions plot in different colors. The “num_nodes” plot separates different core estimates by vertical lines (post burn-in only). The 'tree_depths” plot separates different core estimates by vertical lines (post burn-in only).

Author(s)

Adam Kapelner and Justin Bleich

Examples

## Not run: 
#generate Friedman data
set.seed(11)
n  = 200 
p = 5
X = data.frame(matrix(runif(n * p), ncol = p))
y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n)

##build BART regression model
bart_machine = bartMachine(X, y)

#plot convergence diagnostics
plot_convergence_diagnostics(bart_machine)

## End(Not run)

bartMachine documentation built on July 9, 2023, 5:59 p.m.