dbartsSampler-class | R Documentation |

A reference class object that contains a Bayesian Additive Regression Trees sampler in such a way that it can be modified, stopped, and started all while maintaining its own state.

## S4 method for signature 'dbartsSampler' run(numBurnIn, numSamples, updateState = NA) ## S4 method for signature 'dbartsSampler' sampleTreesFromPrior(updateState = NA) ## S4 method for signature 'dbartsSampler' sampleNodeParametersFromPrior(updateState = NA) ## S4 method for signature 'dbartsSampler' copy(shallow = FALSE) ## S4 method for signature 'dbartsSampler' show() ## S4 method for signature 'dbartsSampler' predict(x.test, offset.test) ## S4 method for signature 'dbartsSampler' setControl(control) ## S4 method for signature 'dbartsSampler' setModel(model) ## S4 method for signature 'dbartsSampler' setData(data) ## S4 method for signature 'dbartsSampler' setResponse(y, updateState = NA) ## S4 method for signature 'dbartsSampler' setOffset(offset, updateScale = FALSE, updateState = NA) ## S4 method for signature 'dbartsSampler' setSigma(sigma, updateState = NA) ## S4 method for signature 'dbartsSampler' setPredictor(x, column, updateState = NA) ## S4 method for signature 'dbartsSampler' setTestPredictor(x.test, column, updateState = NA) ## S4 method for signature 'dbartsSampler' setTestPredictorAndOffset(x.test, offset.test, updateState = NA) ## S4 method for signature 'dbartsSampler' setTestOffset(offset.test, updateState = NA) ## S4 method for signature 'dbartsSampler' printTrees(treeNums) ## S4 method for signature 'dbartsSampler' plotTree( treeNum, treePlotPars = c( nodeHeight = 12, nodeWidth = 40, nodeGap = 8), ...)

`numBurnIn` |
A non-negative integer determining how many iterations the sampler should skip before storing results. If missing or |

`numSamples` |
A positive integer determining how many posterior samples should be returned. If missing or |

`updateState` |
A logical determining if the local cache of the sampler's state should be updated after the completion of the run. If |

`shallow` |
A logical determining if the copy should retain the underlying data of the sampler ( |

`control` |
An object inheriting from |

`model` |
An object inheriting from |

`data` |
An object inheriting from |

`y` |
A numeric response vector of length equal to that with which the sampler was created. |

`x` |
A numeric predictor vector of length equal to that with which the sampler was created. Can be of a distinct number of rows for |

`x.test` |
A new matrix of test predictors, of the number of columns equal to that in the current model. |

`offset` |
A numeric vector of length equal to that with which the sampler was created, or |

`updateScale` |
Logical indicating whether BART's internal scale should update with the new offset. Should only be |

`offset.test` |
A numeric vector of length equal to that of the test matrix, or |

`sigma` |
Numeric vector of residual standard deviations, one for each chain. |

`column` |
An integer or character string vector specifying which column/columns of the predictor matrix is to be replaced. If missing, the entire matrix is substituted. |

`treeNums` |
An integer vector listing the indices of the trees to print. |

`treeNum` |
An integer listing the indices of the tree to plot. |

`treePlotPars` |
A named numeric vector containing the quantities |

`...` |
Extra arguments to |

A `dbartsSampler`

is a mutable object which contains information pertaining to fitting a Bayesian additive regression tree model. The sampler is first created and then, in a separate instruction, run or modified. In this way, MCMC samplers can be constructed with BART components filling arbitrary roles.

`save`

-ing and `load`

ing a `dbarts`

sampler for future use requires that R's serialization mechanism be able to access the state of the sampler which, for memory purposes, is only made available to R on request. To do this, one must “touch” the sampler's state object before saving, e.g. for the object `sampler`

, execute `invisible(sampler$state)`

. This is in addition to guaranteeing that the `state`

object is not `NULL`

, which can be done by setting the sampler's control to an object with `updateState`

as `TRUE`

or passing `TRUE`

as the `updateState`

argument to any of the sampler's applicable methods.

For `run`

, a named-list with contents `sigma`

, `train`

, `test`

, and `varcount`

.

For `setPredictor`

, `TRUE`

/`FALSE`

depending on whether or not the operation was successful. The operation can fail if the new predictor results in a tree with an empty leaf-node. If only single columns were replaced, on the update is rolled-back so that the sampler remains in a valid state.

`predict`

keeps the current test matrix in place and uses the current set of tree splits. This function has two use cases. The first is when `keepTrees`

of `dbartsControl`

is `TRUE`

, in which case the sampler should be run to completion and the function can be used to interrogate the existing fit. When `keepTrees`

is `FALSE`

, the function can be used to obtain the likelihood as part of a proposed new set of covariates in a Metropolis-Hastings step in a full-Bayes sampler. This would typically be followed by a call to `setPredictor`

if the step is accepted.

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