% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/IL_Utilities.R
\encoding{utf-8}
\name{SL.BART}
\alias{SL.BART}
\title{Wrapper for bartMachine learner}
\usage{
SL.BART(
  Y,
  X,
  newX,
  family,
  obsWeights,
  id,
  num_trees = 50,
  num_burn_in = 250,
  verbose = F,
  alpha = 0.95,
  beta = 2,
  k = 2,
  q = 0.9,
  nu = 3,
  num_iterations_after_burn_in = 1000,
  serialize = TRUE,
  seed = 5678,
  ...
)
}
\arguments{
\item{Y}{Outcome variable}

\item{X}{Covariate dataframe}

\item{newX}{Optional dataframe to predict the outcome}

\item{family}{"gaussian" for regression, "binomial" for binary
classification}

\item{obsWeights}{Optional observation-level weights (supported but not tested)}

\item{id}{Optional id to group observations from the same unit (not used
currently).}

\item{num_trees}{The number of trees to be grown in the sum-of-trees model.}

\item{num_burn_in}{Number of MCMC samples to be discarded as "burn-in".}

\item{verbose}{Prints information about progress of the algorithm to the
screen.}

\item{alpha}{Base hyperparameter in tree prior for whether a node is
nonterminal or not.}

\item{beta}{Power hyperparameter in tree prior for whether a node is
nonterminal or not.}

\item{k}{For regression, k determines the prior probability that E(Y|X) is
contained in the interval (y_{min}, y_{max}), based on a normal
distribution. For example, when k=2, the prior probability is 95\%. For
classification, k determines the prior probability that E(Y|X) is between
(-3,3). Note that a larger value of k results in more shrinkage and a more
conservative fit.}

\item{q}{Quantile of the prior on the error variance at which the data-based
estimate is placed. Note that the larger the value of q, the more
aggressive the fit as you are placing more prior weight on values lower
than the data-based estimate. Not used for classification.}

\item{nu}{Degrees of freedom for the inverse chi^2 prior. Not used for
classification.}

\item{num_iterations_after_burn_in}{Number of MCMC samples to draw from the
posterior distribution of f(x).}

\item{serialize}{If TRUE, bartMachine results can be saved to a file, but
will require additional RAM.}

\item{...}{Additional arguments (not used)}
}
\description{
Support bayesian additive regression trees via the bartMachine package.
}