bartmachine updates

[ecpolley]

With the current version of bartMachine (version 1.3), should we update to use bartMachine:: build_bart_machine directly? I see a environment error when trying to locate the correct variables and will explore more, example below:

library(SuperLearner)

set.seed(23432)

training set

n <- 500
p <- 50
X1 <- matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X1) <- paste("X", 1:p, sep="")
X1 <- data.frame(X1)
Y1 <- X1[, 1] + sqrt(abs(X1[, 2] * X1[, 3])) + X1[, 2] - X1[, 3] + rnorm(n)

generate Library and run Super Learner

SL.library <- c("SL.glm", "SL.bartMachine", "SL.mean")
test <- SuperLearner(Y = Y1, X = X1, SL.library = SL.library, verbose = TRUE, method = "method.NNLS")
test

SL.mybartMachine <- function(Y, X, newX, family, obsWeights, id, num_trees = 50,
num_burn_in = 250, verbose = FALSE, alpha = 0.95, beta = 2, k = 2,
q = 0.9, nu = 3, num_iterations_after_burn_in = 1000, ...)
{
require("bartMachine")
model = bartMachine:: build_bart_machine(X = X,
y = Y,
num_trees = num_trees,
num_burn_in = num_burn_in,
verbose = verbose,
alpha = alpha,
beta = beta,
k = k,
q = q,
nu = nu,
num_iterations_after_burn_in = num_iterations_after_burn_in,
serialize = TRUE)
pred <- predict(model, newX)
fit <- list(object = model)
class(fit) <- c("SL.bartMachine")
out <- list(pred = pred, fit = fit)
return(out)
}

SL.library <- c("SL.glm", "SL.mybartMachine", "SL.mean")
test <- SuperLearner(Y = Y1, X = X1, SL.library = SL.library, verbose = TRUE, method = "method.NNLS")
test