bartMachine (version 1.3.4.1)

interaction_investigator: Explore Pairwise Interactions in BART Model

Description

Explore the pairwise interaction counts for a BART model to learn about interactions fit by the model. This function includes an option to generate a plot of the pairwise interaction counts.

Usage

interaction_investigator(bart_machine, plot = TRUE, 
num_replicates_for_avg = 5, num_trees_bottleneck = 20, 
num_var_plot = 50, cut_bottom = NULL, bottom_margin = 10)

Value

interaction_counts

For each of the \(p \times p\) interactions, what is the count across all num_replicates_for_avg BART model replicates' post burn-in Gibbs samples in all trees.

interaction_counts_avg

For each of the \(p \times p\) interactions, what is the average count across all num_replicates_for_avg BART model replicates' post burn-in Gibbs samples in all trees.

interaction_counts_sd

For each of the \(p \times p\) interactions, what is the sd of the interaction counts across the num_replicates_for_avg BART models replicates.

interaction_counts_avg_and_sd_long

For each of the \(p \times p\) interactions, what is the average and sd of the interaction counts across the num_replicates_for_avg BART models replicates. The output is organized as a convenient long table of class data.frame.

Arguments

bart_machine

An object of class ``bartMachine''.

plot

If TRUE, a plot of the pairwise interaction counts is generated.

num_replicates_for_avg

The number of replicates of BART to be used to generate pairwise interaction inclusion counts. Averaging across multiple BART models improves stability of the estimates.

num_trees_bottleneck

Number of trees to be used in the sum-of-trees model for computing pairwise interactions counts. A small number of trees should be used to force the variables to compete for entry into the model.

num_var_plot

Number of variables to be shown on the plot. If ``Inf,'' all variables are plotted (not recommended if the number of predictors is large). Default is 50.

cut_bottom

A display parameter between 0 and 1 that controls where the y-axis is plotted. A value of 0 would begin the y-axis at 0; a value of 1 begins the y-axis at the minimum of the average pairwise interaction inclusion count (the smallest bar in the bar plot). Values between 0 and 1 begin the y-axis as a percentage of that minimum.

bottom_margin

A display parameter that adjusts the bottom margin of the graph if labels are clipped. The scale of this parameter is the same as set with par(mar = c(....)) in R. Higher values allow for more space if the crossed covariate names are long. Note that making this parameter too large will prevent plotting and the plot function in R will throw an error.

Author

Adam Kapelner and Justin Bleich

Details

An interaction between two variables is considered to occur whenever a path from any node of a tree to any of its terminal node contains splits using those two variables. See Kapelner and Bleich, 2013, Section 4.11.

References

Adam Kapelner, Justin Bleich (2016). bartMachine: Machine Learning with Bayesian Additive Regression Trees. Journal of Statistical Software, 70(4), 1-40. doi:10.18637/jss.v070.i04

See Also

investigate_var_importance

Examples

if (FALSE) {
#generate Friedman data
set.seed(11)
n  = 200 
p = 10
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, num_trees = 20)

#investigate interactions
interaction_investigator(bart_machine)
}