# BARTModel: Bayesian Additive Regression Trees Model In brian-j-smith/MachineShop: Machine Learning Models and Tools

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```BARTModel( K = NULL, sparse = FALSE, theta = 0, omega = 1, a = 0.5, b = 1, rho = NULL, augment = FALSE, xinfo = NULL, usequants = FALSE, sigest = NA, sigdf = 3, sigquant = 0.9, lambda = NA, k = 2, power = 2, base = 0.95, tau.num = NULL, offset = NULL, ntree = NULL, numcut = 100, ndpost = 1000, nskip = NULL, keepevery = NULL, 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 for the `NULL` arguments and further model details can be found in the source links below.

## Value

`MLModel` class object.

`gbart`, `mbart`, `surv.bart`, `fit`, `resample`
 `1` ```fit(sale_amount ~ ., data = ICHomes, model = BARTModel) ```