Run the MCMC algorithm for BART model for count-valued responses using STAR. The transformation is modeled as an unknown, monotone function using I-splines. The Robust Adaptive Metropolis (RAM) sampler is used for drawing the parameter of the transformation function.
bart_star_ispline(
y,
X,
X_test = NULL,
y_test = NULL,
lambda_prior = 1/2,
y_max = Inf,
n.trees = 200,
sigest = NULL,
sigdf = 3,
sigquant = 0.9,
k = 2,
power = 2,
base = 0.95,
nsave = 5000,
nburn = 5000,
nskip = 2,
save_y_hat = FALSE,
target_acc_rate = 0.3,
adapt_rate = 0.75,
stop_adapt_perc = 0.5,
verbose = TRUE
)a list with the following elements:
fitted.values: the posterior mean of the conditional expectation of the counts y
post.fitted.values: posterior draws of the conditional mean of the counts y
post.pred.test: draws from the posterior predictive distribution at the test points X_test
post.fitted.values.test: posterior draws of the conditional mean at the test points X_test
post.pred: draws from the posterior predictive distribution of y
post.sigma: draws from the posterior distribution of sigma
post.mu.test: draws of the conditional mean of z_star at the test points
post.log.like.point: draws of the log-likelihood for each of the n observations
post.log.pred.test: draws of the log-predictive distribution for each of the n0 test cases
WAIC: Widely-Applicable/Watanabe-Akaike Information Criterion
p_waic: Effective number of parameters based on WAIC
post.g: draws from the posterior distribution of the transformation g
post.sigma.gamma: draws from the posterior distribution of sigma.gamma,
the prior standard deviation of the transformation g coefficients
n x 1 vector of observed counts
n x p matrix of predictors
n0 x p matrix of predictors for test data
n0 x 1 vector of the test data responses (used for
computing log-predictive scores)
the prior mean for the transformation g() is the Box-Cox function with
parameter lambda_prior
a fixed and known upper bound for all observations; default is Inf
number of trees to use in BART; default is 200
positive numeric estimate of the residual standard deviation (see ?bart)
degrees of freedom for error variance prior (see ?bart)
quantile of the error variance prior that the rough estimate (sigest) is placed at. The closer the quantile is to 1, the more aggresive the fit will be (see ?bart)
the number of prior standard deviations E(Y|x) = f(x) is away from +/- 0.5. The response is internally scaled to range from -0.5 to 0.5. The bigger k is, the more conservative the fitting will be (see ?bart)
power parameter for tree prior (see ?bart)
base parameter for tree prior (see ?bart)
number of MCMC iterations to save
number of MCMC iterations to discard
number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw
logical; if TRUE, compute and save the posterior draws of the expected counts, E(y), which may be slow to compute
target acceptance rate (between zero and one)
rate of adaptation in RAM sampler (between zero and one)
stop adapting at the proposal covariance at stop_adapt_perc*nburn
logical; if TRUE, print time remaining