BADS: Fit a bayesian additive decision stump model for regression

In DongyueXie/bCART: Explore variants of Bayesian trees.

Description

Bayesian additive decision stump(BADS) is a Bayesian sum of two-leaf-node trees model.

Usage

BADS(X, y, x.test, sigdf = 3, sigquant = 0.9, k = 2, lambda = NA,
  sigest = NA, sigmaf = NA, ntree = 50, ndpost = 200,
  nskip = 100, Tmin = 2, printevery = 100, save_trees = F,
  rule = "bart", pre_train = T, n_pre_train = 100)

Arguments

X	samples by features matrix
y	response
x.test	test samples by feature matrix
sigdf, sigquant, k, lambda, sigest, sigmaf	see ?BART::wbart
ntree	number of decison stumps
nskip, ndpost	number of burn-in and posterior draws
Tmin	minimum number of samples in a leaf node allowed
printevery	print progress for every 'printevery' iterations
save_trees	whether save all the trees from each iteration as a list
rule	The splitting rule of a node. Choices are: 1. "grp": Gaussian random projection, randomly draw a length p vector from standard normal as the linear combination coefficients of p variables; 2. sgrp: sparse Gaussian random projection, which generates sparse linear combination coefficients; 3. bart: originla bart splits, which are axis-aligned splits; 4. hyperplane: randomly connect two points from the node as the partiton of node space.
pre_train	whether pre-train the model using 'bart' rule before switching to another splitting rule.
n_pre_train	number of iterations of pre-train

Value

BADS returns a list of the following elements.

yhat.train	A matrix with ndpost rows and nrow(X) columns.
yhat.test	A matrix with ndpost rows and nrow(x.test) columns.
yhat.train.mean	Posterior mean of MCMC draws of traning data fits
yhat.test.mean	Posterior mean of MCMC draws of testing data fits
sigma	draws of random error vairaince, length = nskip+ndpost
tree_history	If save_trees = TRUE, then a list of all trees

Author(s)

Dongyue Xie: [e-mail]

References

Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298 <doi:10.1214/09-AOAS285>.

DongyueXie/bCART documentation built on Feb. 4, 2020, 12:26 a.m.