# varImpScores: Variable importances as defined by Hernandez et al. (2018) In bartBMA: Bayesian Additive Regression Trees using Bayesian Model Averaging

## Description

This measure defines the importance of a variable as the model-probability weighted sum of the number of splits on the variable of interest, divided by the sum over all variables of such weighted counts of splits.

## Usage

 `1` ```varImpScores(object) ```

## Arguments

 `object` A bartBMA object obtained using the barBMA function.

## Value

A vector of variable importances. 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```#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 varImpScores(bart_bma_example) ```

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