varIncProb: Variable inclusion probabilities as defined by Linero (2018)

In bartBMA: Bayesian Additive Regression Trees using Bayesian Model Averaging

Description

This measure defines the posterior inclusion probability of a variable as the model-probability weighted sum of indicator variables for whether the variable was used in any splitting rules in any of the trees in the sum-of-tree model.

Usage

varIncProb(object)

Arguments

object

A bartBMA object obtained using the barBMA function.

Value

A vector of posterior inclusion probabilities. The variables are ordered in the same order that they occur in columns of the input covariate matrix used to obtain the input bartBMA object.

Examples

#set the seed
set.seed(100)
#simulate some data
N <- 100
p<- 100
epsilon <- rnorm(N)
xcov <- matrix(runif(N*p), nrow=N)
y <- sin(pi*xcov[,1]*xcov[,2]) + 20*(xcov[,3]-0.5)^2+10*xcov[,4]+5*xcov[,5]+epsilon
epsilontest <- rnorm(N)
xcovtest <- matrix(runif(N*p), nrow=N)
ytest <- sin(pi*xcovtest[,1]*xcovtest[,2]) + 20*(xcovtest[,3]-0.5)^2+10*xcovtest[,4]+
  5*xcovtest[,5]+epsilontest

#Train the object 
bart_bma_example <- bartBMA(x.train = xcov,y.train=y,x.test=xcovtest,zero_split = 1, 
                            only_max_num_trees = 1,split_rule_node = 0)
#Obtain the variable importances
varIncProb(bart_bma_example)

bartBMA documentation built on March 13, 2020, 5:06 p.m.