% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/bart_arrays.R

\name{predict_bartMachineArr}

\alias{predict_bartMachineArr}

\title{Make a prediction on data using a BART array object}

\usage{

predict_bartMachineArr(object, new_data, ...)

}

\arguments{

\item{object}{An object of class ``bartMachineArr''.}

\item{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.}

\item{...}{Not supported. Note that parameters \code{type} and \code{prob_rule_class} for

\code{\link{predict.bartMachine}} are not supported.}

}

\value{

If regression, a numeric vector of \code{y_hat}, the best guess as to the response. If classification and \code{type = ``prob''},

a numeric vector of \code{p_hat}, the best guess as to the probability of the response class being the ''positive'' class. If classification and

\code{type = ''class''}, a character vector of the best guess of the response's class labels.

}

\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.

}

\examples{

\dontrun{

#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)

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])

}

}

\seealso{

\code{\link{predict.bartMachine}}

}

\author{

Adam Kapelner

}