pd_plot: Partial Dependence Plot

In bartMachine: Bayesian Additive Regression Trees

Partial Dependence Plot

Description

Creates a partial dependence plot for a BART model for regression or classification.

Usage

pd_plot(bart_machine, j, 
levs = c(0.05, seq(from = 0.1, to = 0.9, by = 0.1), 0.95), 
lower_ci = 0.025, upper_ci = 0.975, prop_data = 1)

Arguments

bart_machine	An object of class “bartMachine”.
j	The number or name of the column in the design matrix for which the partial dependence plot is to be created.
levs	Quantiles at which the partial dependence function should be evaluated. Linear extrapolation is performed between these points.
lower_ci	Lower limit for credible interval
upper_ci	Upper limit for credible interval
prop_data	The proportion of the training data to use. Default is 1. Use a lower proportion for speedier pd_plots. The closer to 1, the more resolution the PD plot will have; the closer to 0, the lower but faster.

Details

For regression models, the units on the y-axis are the same as the units of the response. For classification models, the units on the y-axis are probits.

Value

Invisibly, returns a list with the following components:

x_j_quants	Quantiles at which the partial dependence function is evaluated
bart_avg_predictions_by_quantile_by_gibbs	All samples of \hat{f}(x)
bart_avg_predictions_by_quantile	Posterior means for \hat{f}(x) at x_j_quants
bart_avg_predictions_lower	Lower bound of the desired confidence of the credible interval of \hat{f}(x)
bart_avg_predictions_upper	Upper bound of the desired confidence of the credible interval of \hat{f}(x)
prop_data	The proportion of the training data to use as specified when this function was executed

Note

This function is parallelized by the number of cores set in set_bart_machine_num_cores.

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

HA Chipman, EI George, and RE McCulloch. BART: Bayesian Additive Regressive Trees. The Annals of Applied Statistics, 4(1): 266–298, 2010.

Examples

## Not run: 
#Regression example

#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)

#partial dependence plot for quadratic term
pd_plot(bart_machine, "X3")


#Classification example

#get data and only use 2 factors
data(iris)
iris2 = iris[51:150,]
iris2$Species = factor(iris2$Species)

#build BART classification model
bart_machine = bartMachine(iris2[ ,1:4], iris2$Species)

#partial dependence plot 
pd_plot(bart_machine, "Petal.Width")

## End(Not run)

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