pred_intervals_new_initials_GS {bartBMA}

R Documentation

Prediction intervals for bart-bma output

Description

This function produces prediction intervals for BART-BMA estimates by post-hoc Gibbs-sampling from the full conditionals of the terminal node parameters and the variance of the error term. See Hernandez et al. (2018) Appendix D for details.

Usage

pred_intervals_new_initials_GS(
  object,
  num_iter,
  burnin,
  l_quant,
  u_quant,
  newdata = NULL,
  update_resids = 1,
  trainingdata
)

Arguments

object	bartBMA object obtained from function bartBMA
num_iter	Total number of iterations of the Gibbs sampler (including burn-in).
burnin	Number of burn-on iterations of the Gibbs sampler.
l_quant	Lower quartile of the prediction interval.
u_quant	Upper quartile of the prediction interval.
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.
update_resids	Option for whether to update the partial residuals in the gibbs sampler. If equal to 1, updates partial residuals, if equal to zero, does not update partial residuals. The defaullt setting is to update the partial residuals.
trainingdata	The matrix of training data.

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_intervals_new_initials_GS(bart_bma_example,1000,100,0.025,0.975,
newdata=NULL,update_resids=1,xcov)

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