Flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.
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
)if provided, then coarsen the times of survival responses per the quantiles \(1/K, 2/K, ..., K/K\) to reduce computational burdern.
logical indicating whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
\(theta\) and \(omega\) parameters; zero means random.
sparse parameters for \(Beta(a, b)\) prior: \(0.5 <= a <= 1\) where lower values induce more sparsity and typically \(b = 1\).
sparse parameter: typically \(rho = p\) where \(p\) is the number of covariates under consideration.
whether data augmentation is to be performed in sparse variable selection.
optional matrix whose rows are the covariates and columns their cutpoints.
whether covariate cutpoints are defined by uniform quantiles or generated uniformly.
normal error variance prior for numeric response variables.
degrees of freedom for error variance prior.
quantile at which a rough estimate of the error standard deviation is placed.
scale of the prior error variance.
number of standard deviations \(f(x)\) is away from +/-3 for categorical response variables.
power and base parameters for tree prior.
numerator in the \(tau\) definition, i.e., \(tau = tau.num / (k * sqrt(ntree))\).
override for the default \(offset\) of \(F^-1(mean(y))\) in the multivariate response probability \(P(y[j] = 1 | x) = F(f(x)[j] + offset[j])\).
number of trees in the sum.
number of possible covariate cutoff values.
number of posterior draws returned.
number of MCMC iterations to be treated as burn in.
interval at which to keep posterior draws.
interval at which to print MCMC progress.
MLModel class object.
factor, numeric, Surv
Default values and further model details can be found in the source links below.
# NOT RUN {
## Requires prior installation of suggested package BART to run
fit(sale_amount ~ ., data = ICHomes, model = BARTModel)
# }
# NOT RUN {
# }
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