pred_ints_exact: Prediction intervals for bart-bma output obtained using...

In bartBMA: Bayesian Additive Regression Trees using Bayesian Model Averaging

Description

This function produces prediction intervals for bart-bma output.

Usage

pred_ints_exact(
  object,
  l_quant,
  u_quant,
  newdata = NULL,
  num_cores = 1,
  root_alg_precision = 1e-05
)

Arguments

object	bartBMA object obtained from function bartBMA
l_quant	Lower quantile of credible intervals for the ITEs, CATT, CATNT.
u_quant	Upper quantile of credible intervals for the ITEs, CATT, CATNT.
newdata	Test data for which predictions are to be produced. Default = NULL. If NULL, then produces prediction intervals for training data if no test data was used in producing the bartBMA object, or produces prediction intervals for the original test data if test data was used in producing the bartBMA object.
num_cores	Number of cores used in parallel.
root_alg_precision	The algorithm should obtain approximate bounds that are within the distance root_alg_precision of the true quantile for the chosen average of models.

Value

The output is a list of length 2:

PI	A 3 by n matrix, where n is the number of observations. The first row gives the l_quant*100 quantiles. The second row gives the medians. The third row gives the u_quant*100 quantiles.
meanpreds	An n by 1 matrix containing the estimated means.

Examples

#load the package
library(bartBMA)
#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 prediction intervals
pred_ints_exact(bart_bma_example,0.025,0.975,newdata=NULL,num_cores=1)

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