R: Discrete Bayesian Additive Regression Trees Sampler

dbarts {dbarts} | R Documentation

## 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

    dbarts(
        formula, data, test, subset, weights, offset, offset.test = offset,
        verbose = FALSE, n.samples = 800L,
        tree.prior = cgm, node.prior = normal, resid.prior = chisq,
        proposal.probs = c(
            birth_death = 0.5, swap = 0.1, change = 0.4, birth = 0.5),
        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, split.probs)` 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.
`proposal.probs` | Named numeric vector or `NULL`, optionally specifying the proposal rules and their probabilities. Elements should be `"birth_death"`, `"change"`, and `"swap"` to control tree change proposals, and `"birth"` to give the relative frequency of birth/death in the `"birth_death"` step.
`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`.

[Package dbarts version 0.9-22 Index]