Get Posterior Error Variance Estimates
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)
bart_machine |
An object of class “bartMachine”. |
after_burn_in |
If TRUE, only the σ^2 draws after the burn-in period are returned. |
plot_hist |
If TRUE, a histogram of the posterior σ^2 draws is generated. |
plot_CI |
Confidence level for credible interval on histogram. |
plot_sigma |
If TRUE, plots σ instead of σ^2. |
Returns a vector of posterior σ^2 draws (with or without the burn-in samples).
Adam Kapelner and Justin Bleich
## 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)
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