BARTr: Fit a bayesian additive regression tree(BART) model for...
In DongyueXie/bCART: Explore variants of Bayesian trees.

Description Usage Arguments Value Author(s) References

Fit a bayesian additive regression tree(BART) model for regression

BARTr(X, y, x.test, sigdf = 3, sigquant = 0.9, k = 2, lambda = NA,
  sigest = NA, sigmaf = NA, power = 2, base = 0.95,
  p_split = "CGM", r = 2, ntree = 50, ndpost = 700, nskip = 300,
  Tmin = 2, printevery = 100, p_modify = c(2.5, 2.5, 4)/9,
  save_trees = F, rule = "bart", pre_train = T, n_pre_train = 100)

`X`	training samples by features matrix
`y`	response
`x.test`	testing samples by feature matrix
`sigdf, sigquant, k, lambda, sigest, sigmaf, power, base`	see ?BART::wbart
`p_split`	choice of 'CGM', 'RS'. 'CGM' splits an internal node with probability base*(1+d)^(-power); 'RS': r^(-d), 2 <= r <= n. d is the depth of an internal node.
`r`	'RS' splits an internal node with probability r^(-d), 2 <= r <= n. d is the depth of an internal node.
`ntree`	number of trees
`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
`p_modify`	proportion of three moves: grow, prune, change.
`save_trees`	whether save all the trees from each iteration as a list
`rule`	The splitting rule of an internal 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 BART model using 'bart' rule before switching to another splitting rule.
`n_pre_train`	number of iterations of pre-train

BARTr 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
`tree_proposal_total`	A ntree by length(p_modify) matrix, the (i,j)th entry is the total number of jth proposal of ith tree
`tree_proposal_accept`	A ntree by length(p_modify) matrix, the (i,j)th entry is the total number of accepted jth proposal of ith tree
`tree_leaf_count`	Number of leaf nodes in each tree

Dongyue Xie: dongyxie@gmail.com

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.