% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/bart_package_predicts.R

\name{predict.bartMachine}

\alias{predict.bartMachine}

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

\usage{

\method{predict}{bartMachine}(

object,

new_data,

type = "prob",

prob_rule_class = NULL,

verbose = TRUE,

...

)

}

\arguments{

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

\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{type}{Only relevant if the bartMachine model is classification. The type can be ``prob'' which will

return the estimate of \eqn{P(Y = 1)}(the ``positive'' class) or ``class'' which will return the best guess as to the

class of the object, in the original label, based on if the probability estimate is greater

than \code{prob_rule_class}. Default is ``prob.''}

\item{prob_rule_class}{The rule to determine when the class estimate is \eqn{Y = 1} (the ``positive'' class) based on the probability estimate. This

defaults to what was originally specified in the \code{bart_machine} object.}

\item{verbose}{Prints out prediction-related messages. Currently in use only for probability predictions to let the user know which class

is being predicted. Default is \code{TRUE}.}

\item{...}{Parameters that are ignored.}

}

\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 a fitted BART model for regression or classification.

}

\examples{

#Regression example

\dontrun{

#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)

##make predictions on the training data

y_hat = predict(bart_machine, 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)

##make probability predictions on the training data

p_hat = predict(bart_machine, X)

##make class predictions on test data

y_hat_class = predict(bart_machine, X, type = "class")

##make class predictions on test data conservatively for ''versicolor''

y_hat_class_conservative = predict(bart_machine, X, type = "class", prob_rule_class = 0.9)

}

}

\seealso{

\code{\link{bart_predict_for_test_data}}

}

\author{

Adam Kapelner and Justin Bleich

}