bart_machine_get_posterior {bartMachine} R Documentation

## Get Full Posterior Distribution

### Description

Generates draws from posterior distribution of \hat{f}(x) for a specified set of observations.

### Usage

bart_machine_get_posterior(bart_machine, new_data)


### Arguments

 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.

### Value

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 num_iterations_after_burn_in for each observation. For regression, the estimates have the same units as the response. For classification, the estimates are probabilities.

### Note

This function is parallelized by the number of cores set in set_bart_machine_num_cores.

### Author(s)

calc_credible_intervals, calc_prediction_intervals

### Examples

## 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)



[Package bartMachine version 1.2.6 Index]