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Get Posterior Error Variance Estimates

Source: R/bart_package_plots.R
get_sigsqs.Rd

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 = TRUE,
  plot_hist = FALSE,
  plot_CI = 0.95,
  plot_sigma = F,
  verbose = TRUE
)

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 \(\sigma^2\).

verbose

If TRUE, prints plots to the active device.

Value

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

See also

get_sigsqs

Author

Adam Kapelner and Justin Bleich

Examples

if (FALSE) { # \dontrun{
#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)
} # }