bartMachine (version 1.2.3)

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, ...)

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.

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.

See Also

predict.bartMachine

Examples

#Regression example
## Not run: 
# #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])
# ## End(Not run)

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