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.
get_sigsqs(bart_machine, after_burn_in = T,
plot_hist = F, plot_CI = .95, plot_sigma = F)An object of class ``bartMachine''.
If TRUE, only the \(\sigma^2\) draws after the burn-in period are returned.
If TRUE, a histogram of the posterior \(\sigma^2\) draws is generated.
Confidence level for credible interval on histogram.
If TRUE, plots \(\sigma\) instead of \(\sigma^2\).
Returns a vector of posterior \(\sigma^2\) draws (with or without the burn-in samples).
# 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)
# }
# NOT RUN {
# }