BARTModel: Bayesian Additive Regression Trees Model

In MachineShop: Machine Learning Models and Tools

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 argument values and further model details can be found in the source See Also links below.

Value

MLModel class object.

See Also

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. 11, 2024, 6:28 p.m.