# BARTModel: Bayesian Additive Regression Trees Model In MachineShop: Machine Learning Models and Tools

 BARTModel R Documentation

## Bayesian Additive Regression Trees Model

### Description

Flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.

### Usage

```BARTModel(
K = integer(),
sparse = FALSE,
theta = 0,
omega = 1,
a = 0.5,
b = 1,
rho = numeric(),
augment = FALSE,
xinfo = matrix(NA, 0, 0),
usequants = FALSE,
sigest = NA,
sigdf = 3,
sigquant = 0.9,
lambda = NA,
k = 2,
power = 2,
base = 0.95,
tau.num = numeric(),
offset = numeric(),
ntree = integer(),
numcut = 100,
ndpost = 1000,
nskip = integer(),
keepevery = integer(),
printevery = 1000
)
```

### Arguments

 `K` if provided, then coarsen the times of survival responses per the quantiles 1/K, 2/K, ..., K/K to reduce computational burdern. `sparse` logical indicating whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016. `theta, omega` theta and omega parameters; zero means random. `a, b` sparse parameters for Beta(a, b) prior: 0.5 <= a <= 1 where lower values induce more sparsity and typically b = 1. `rho` sparse parameter: typically rho = p where p is the number of covariates under consideration. `augment` whether data augmentation is to be performed in sparse variable selection. `xinfo` optional matrix whose rows are the covariates and columns their cutpoints. `usequants` whether covariate cutpoints are defined by uniform quantiles or generated uniformly. `sigest` normal error variance prior for numeric response variables. `sigdf` degrees of freedom for error variance prior. `sigquant` quantile at which a rough estimate of the error standard deviation is placed. `lambda` scale of the prior error variance. `k` number of standard deviations f(x) is away from +/-3 for categorical response variables. `power, base` power and base parameters for tree prior. `tau.num` numerator in the tau definition, i.e., tau = tau.num / (k * sqrt(ntree)). `offset` override for the default offset of F^-1(mean(y)) in the multivariate response probability P(y[j] = 1 | x) = F(f(x)[j] + offset[j]). `ntree` number of trees in the sum. `numcut` number of possible covariate cutoff values. `ndpost` number of posterior draws returned. `nskip` number of MCMC iterations to be treated as burn in. `keepevery` interval at which to keep posterior draws. `printevery` interval at which to print MCMC progress.

### Details

Response types:

`factor`, `numeric`, `Surv`

Default values and further model details can be found in the source links below.

### Value

`MLModel` class object.

`gbart`, `mbart`, `surv.bart`, `fit`, `resample`

### Examples

```
## Requires prior installation of suggested package BART to run

fit(sale_amount ~ ., data = ICHomes, model = BARTModel)

```

MachineShop documentation built on Sept. 5, 2022, 5:08 p.m.