pred_ints_exact {bartBMA} | R Documentation |
This function produces prediction intervals for bart-bma output.
pred_ints_exact(
object,
l_quant,
u_quant,
newdata = NULL,
num_cores = 1,
root_alg_precision = 1e-05
)
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. |
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. |
#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)