# dbarts: Discrete Bayesian Additive Regression Trees Sampler In dbarts: Discrete Bayesian Additive Regression Trees Sampler

## Description

Creates a sampler object for a given problem which fits a Bayesian Additive Regreesion Trees model. Internally stores state in such a way as to be mutable.

## Usage

 ```1 2 3 4``` ```dbarts(formula, data, test, subset, weights, offset, offset.test = offset, verbose = FALSE, n.samples = 800L, tree.prior = cgm, node.prior = normal, resid.prior = chisq, control = dbartsControl(), sigma = NA_real_) ```

## Arguments

 `formula` An object of class `formula` following an analogous model description syntax as `lm`. For backwards compatibility, can also be the `bart` matrix `x.train`. `data` An optional data frame, list, or environment containing predictors to be used with the model. For backwards compatibility, can also be the `bart` vector `y.train`. `test` An optional matrix or data frame with the same number of predictors as `data`, or `formula` in backwards compatibility mode. If column names are present, a matching algorithm is used. `subset` An optional vector specifying a subset of observations to be used in the fitting process. `weights` An optional vector of weights to be used in the fitting process. When present, BART fits a model with observations y | x ~ N(f(x), σ^2 / w), where f(x) is the unknown function. `offset` An optional vector specifying an offset from 0 for the relationship between the underyling function, f(x), and the response y. Only is useful for binary responses, in which case the model fit is to assume P(Y = 1 | X = x) = Φ(f(x) + offset), where Φ is the standard normal cumulative distribution function. `offset.test` The equivalent of `offset` for test observations. Will attempt to use `offset` when applicable. `verbose` A logical determining if additional output is printed to the console. See `dbartsControl`. `n.samples` A positive integer setting the default number of posterior samples to be returned for each run of the sampler. Can be overriden at run-time. See `dbartsControl`. `tree.prior` An expression of the form `cgm` or `cgm(power, base)` setting the tree prior used in fitting. `node.prior` An expression of the form `normal` or `normal(k)` that sets the prior used on the averages within nodes. `resid.prior` An expression of the form `chisq` or `chisq(df, quant)` that sets the prior used on the residual/error variance. `control` An object inheriting from `dbartsControl`, created by the `dbartsControl` function. `sigma` A positive numeric estimate of the residual standard deviation. If `NA`, a linear model is used with all of the predictors to obtain one.

## Details

“Discrete sampler” refers to that `dbarts` is implemented using ReferenceClasses, so that there exists a mutable object constructed in C++ that is largely obscured from R. The `dbarts` function is the primary way of creating a `dbartsSampler`, for which a variety of methods exist.

## Value

A reference object of `dbartsSampler`.

dbarts documentation built on March 20, 2020, 5:08 p.m.