interaction_investigator {bartMachine}

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)

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.

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.

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.

Note

In the plot, the red bars correspond to the standard error of the variable inclusion proportion estimates (since multiple replicates were used).

Author(s)

Adam Kapelner and Justin Bleich

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

## Not run: 
#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)

## End(Not run)