get_sigsqs: Get Posterior Error Variance Estimates

In bartMachine: Bayesian Additive Regression Trees

Get Posterior Error Variance Estimates

Description

Returns the posterior estimates of the error variance from the Gibbs samples with an option to create a histogram of the posterior estimates of the error variance with a credible interval overlaid.

Usage

get_sigsqs(bart_machine, after_burn_in = T, 
plot_hist = F, plot_CI = .95, plot_sigma = F)

Arguments

bart_machine	An object of class “bartMachine”.
after_burn_in	If TRUE, only the \sigma^2 draws after the burn-in period are returned.
plot_hist	If TRUE, a histogram of the posterior \sigma^2 draws is generated.
plot_CI	Confidence level for credible interval on histogram.
plot_sigma	If TRUE, plots \sigma instead of \sigma^2.

Value

Returns a vector of posterior \sigma^2 draws (with or without the burn-in samples).

Author(s)

Adam Kapelner and Justin Bleich

See Also

get_sigsqs

Examples

## Not run: 
#generate Friedman data
set.seed(11)
n  = 300 
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 posterior sigma^2's after burn-in and plot
sigsqs = get_sigsqs(bart_machine, plot_hist = TRUE)

## End(Not run)

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