Get Full Posterior Distribution
Generates draws from posterior distribution of \hat{f}(x) for a specified set of observations.
bart_machine_get_posterior(bart_machine, new_data)
bart_machine |
An object of class “bartMachine”. |
new_data |
A data frame containing observations at which draws from posterior distribution of \hat{f}(x) are to be obtained. |
Returns a list with the following components:
y_hat |
Posterior mean estimates. For regression, the estimates have the same units as the response. For classification, the estimates are probabilities. |
new_data |
The data frame with rows at which the posterior draws are to be generated. Column names should match that of the training data. |
y_hat_posterior_samples |
The full set of posterior samples of size |
This function is parallelized by the number of cores set in set_bart_machine_num_cores.
Adam Kapelner and Justin Bleich
## Not run: #Regression example #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 posterior distribution posterior = bart_machine_get_posterior(bart_machine, X) print(posterior$y_hat) #Classification example #get data and only use 2 factors data(iris) iris2 = iris[51:150,] iris2$Species = factor(iris2$Species) #build BART classification model bart_machine = bartMachine(iris2[ ,1 : 4], iris2$Species) #get posterior distribution posterior = bart_machine_get_posterior(bart_machine, iris2[ ,1 : 4]) print(posterior$y_hat) ## End(Not run)
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