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

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 = dbarts::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 \mid x \sim N(f(x), \sigma^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 \mid X = x) = \Phi(f(x) + \mathrm{offset})`, where `\Phi` 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.