predict_bartMachineArr: Make a prediction on data using a BART array object

Description

Makes a prediction on new data given an array of fitted BART model for regression or classification. If BART creates models that are variable, running many and averaging is a good strategy. It is well known that the Gibbs sampler gets locked into local modes at times. This is a way to average over many chains.

Usage

predict_bartMachineArr(object, new_data, ...)

Value

If regression, a numeric vector of y_hat, the best guess as to the response. If classification and type = ``prob'', a numeric vector of p_hat, the best guess as to the probability of the response class being the ''positive'' class. If classification and type = ''class'', a character vector of the best guess of the response's class labels.

Arguments

object: An object of class ``bartMachineArr''.
new_data: A data frame where each row is an observation to predict. The column names should be the same as the column names of the training data.
...: Not supported. Note that parameters type and prob_rule_class for predict.bartMachine are not supported.

Author

Adam Kapelner

Examples

Run this code

#Regression example
if (FALSE) {
#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)
bart_machine_arr = bartMachineArr(bart_machine)

##make predictions on the training data
y_hat = predict(bart_machine_arr, X)

#Classification example
data(iris)
iris2 = iris[51 : 150, ] #do not include the third type of flower for this example
iris2$Species = factor(iris2$Species)  
bart_machine = bartMachine(iris2[ ,1:4], iris2$Species)
bart_machine_arr = bartMachineArr(bart_machine)

##make probability predictions on the training data
p_hat = predict_bartMachineArr(bart_machine_arr, iris2[ ,1:4])
}