BARTModel | R Documentation |
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 )
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. |
factor
, numeric
, Surv
Default values and further model details can be found in the source links below.
MLModel
class object.
gbart
, mbart
,
surv.bart
, fit
, resample
## Requires prior installation of suggested package BART to run fit(sale_amount ~ ., data = ICHomes, model = BARTModel)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.