Description Usage Arguments Details Value See Also Examples
Flexible nonparametric modeling of covariates for continuous, binary, categorical and timetoevent outcomes.
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
)

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 for the NULL
arguments and further model details can be
found in the source links below.
MLModel
class object.
gbart
, mbart
,
surv.bart
, fit
, resample
1 
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