calc_credible_intervals: Calculate Credible Intervals

In bartMachine: Bayesian Additive Regression Trees

Calculate Credible Intervals

Description

Generates credible intervals for \hat{f}(x) for a specified set of observations.

Usage

calc_credible_intervals(bart_machine, new_data, 
ci_conf = 0.95)

Arguments

bart_machine	An object of class “bartMachine”.
new_data	A data frame containing observations at which credible intervals for \hat{f}(x) are to be computed.
ci_conf	Confidence level for the credible intervals. The default is 95%.

Details

This interval is the appropriate quantiles based on the confidence level, ci_conf, of the predictions for each of the Gibbs samples post-burn in.

Value

Returns a matrix of the lower and upper bounds of the credible intervals for each observation in new_data.

Note

This function is parallelized by the number of cores set in set_bart_machine_num_cores.

Author(s)

Adam Kapelner and Justin Bleich

See Also

calc_prediction_intervals, bart_machine_get_posterior

Examples

## Not run: 
#generate Friedman data
set.seed(11)
n  = 200 
p = 5
X = data.frame(matrix(runif(n * p), ncol = p))
y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n)

##build BART regression model
bart_machine = bartMachine(X, y)

#get credible interval
cred_int = calc_credible_intervals(bart_machine, X)
print(head(cred_int))

## End(Not run)

bartMachine documentation built on July 9, 2023, 5:59 p.m.