pred_ints_exact_par {bartBMA}R Documentation

Prediction intervals for bart-bma output obtained using linear algebra to obtain means and variances, and using bisection to find the quantiles of the mixture of t distributions.

Description

This function produces prediction intervals for bart-bma output.

Usage

pred_ints_exact_par(
  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

## Not run: 
#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_par(bart_bma_example,0.025,0.975,newdata=NULL,num_cores=1)

## End(Not run)

[Package bartBMA version 1.0 Index]