abart {BART}  R Documentation 
BART is a Bayesian “sumoftrees” model.
For a numeric response y, we have
y = f(x) + e,
where e ~ N(0,sigma^2).
f is the sum of many tree models. The goal is to have very flexible inference for the uknown function f.
In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.
abart( x.train, times, delta, x.test=matrix(0,0,0), K=100, type='abart', ntype=1, sparse=FALSE, theta=0, omega=1, a=0.5, b=1, augment=FALSE, rho=NULL, xinfo=matrix(0,0,0), usequants=FALSE, rm.const=TRUE, sigest=NA, sigdf=3, sigquant=0.90, k=2, power=2, base=0.95, lambda=NA, tau.num=c(NA, 3, 6)[ntype], offset=NULL, w=rep(1, length(times)), ntree=c(200L, 50L, 50L)[ntype], numcut=100L, ndpost=1000L, nskip=100L, keepevery=c(1L, 10L, 10L)[ntype], printevery=100L, transposed=FALSE, mc.cores = 1L, ## mc.abart only nice = 19L, ## mc.abart only seed = 99L ## mc.abart only ) mc.abart( x.train, times, delta, x.test=matrix(0,0,0), K=100, type='abart', ntype=1, sparse=FALSE, theta=0, omega=1, a=0.5, b=1, augment=FALSE, rho=NULL, xinfo=matrix(0,0,0), usequants=FALSE, rm.const=TRUE, sigest=NA, sigdf=3, sigquant=0.90, k=2, power=2, base=0.95, lambda=NA, tau.num=c(NA, 3, 6)[ntype], offset=NULL, w=rep(1, length(times)), ntree=c(200L, 50L, 50L)[ntype], numcut=100L, ndpost=1000L, nskip=100L, keepevery=c(1L, 10L, 10L)[ntype], printevery=100L, transposed=FALSE, mc.cores = 2L, nice = 19L, seed = 99L )
x.train 
Explanatory variables for training (in sample)
data. 
times 
The time of event or rightcensoring. 
delta 
The event indicator: 1 is an event while 0 is censored. 
x.test 
Explanatory variables for test (out of sample)
data. Should have same structure as 
K 
If provided, then coarsen 
type 
You can use this argument to specify the type of fit.

ntype 
The integer equivalent of 
sparse 
Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016. 
theta 
Set theta parameter; zero means random. 
omega 
Set omega parameter; zero means random. 
a 
Sparse parameter for Beta(a, b) prior: 0.5<=a<=1 where lower values inducing more sparsity. 
b 
Sparse parameter for Beta(a, b) prior; 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 
You can provide the cutpoints to BART or let BART
choose them for you. To provide them, use the 
usequants 
If 
rm.const 
Whether or not to remove constant variables. 
sigest 
The prior for the error variance
(sigma\^2) is inverted chisquared (the standard
conditionally conjugate prior). The prior is specified by choosing
the degrees of freedom, a rough estimate of the corresponding
standard deviation and a quantile to put this rough estimate at. If

sigdf 
Degrees of freedom for error variance prior. Not used if y is binary. 
sigquant 
The quantile of the prior that the rough estimate
(see 
k 
For numeric y, 
power 
Power parameter for tree prior. 
base 
Base parameter for tree prior. 
lambda 
The scale of the prior for the variance. Not used if y is binary. 
tau.num 
The numerator in the 
offset 
Continous BART operates on 
w 
Vector of weights which multiply the standard deviation. Not used if y is binary. 
ntree 
The number of trees in the sum. 
numcut 
The number of possible values of c (see

ndpost 
The number of posterior draws returned. 
nskip 
Number of MCMC iterations to be treated as burn in. 
printevery 
As the MCMC runs, a message is printed every printevery draws. 
keepevery 
Every keepevery draw is kept to be returned to the user. 
transposed 
When running 
seed 
Setting the seed required for reproducible MCMC. 
mc.cores 
Number of cores to employ in parallel. 
nice 
Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest). 
BART is a Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior (f,sigma) \ (x,y) in the numeric y case and just f in the binary y case.
Thus, unlike a lot of other modelling methods in R, we do not produce
a single model object from which fits and summaries may be extracted.
The output consists of values f*(x) (and
sigma* in the numeric case) where * denotes a
particular draw. The x is either a row from the training data,
x.train
or the test data, x.test
.
abart
returns an object of type abart
which is
essentially a list.
In the numeric y case, the list has components:
yhat.train 
A matrix with ndpost rows and nrow(x.train) columns.
Each row corresponds to a draw f* from the posterior of f
and each column corresponds to a row of x.train.
The (i,j) value is f*(x) for the i\^th kept draw of f
and the j\^th row of x.train. 
yhat.test 
Same as yhat.train but now the x's are the rows of the test data. 
yhat.train.mean 
train data fits = mean of yhat.train columns. 
yhat.test.mean 
test data fits = mean of yhat.test columns. 
sigma 
post burn in draws of sigma, length = ndpost. 
first.sigma 
burnin draws of sigma. 
varcount 
a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given. 
sigest 
The rough error standard deviation (sigma) used in the prior. 
N = 1000 P = 5 #number of covariates M = 8 set.seed(12) x.train=matrix(runif(N*P, 2, 2), N, P) mu = x.train[ , 1]^3 y=rnorm(N, mu) offset=mean(y) T=exp(y) C=rexp(N, 0.05) delta=(T<C)*1 table(delta)/N times=(T*delta+C*(1delta)) ##test BART with token run to ensure installation works set.seed(99) post1 = abart(x.train, times, delta, nskip=5, ndpost=10) ## Not run: post1 = mc.abart(x.train, times, delta, mc.cores=M, seed=99) post2 = mc.abart(x.train, times, delta, offset=offset, mc.cores=M, seed=99) Z=8 plot(mu, post1$yhat.train.mean, asp=1, xlim=c(Z, Z), ylim=c(Z, Z)) abline(a=0, b=1) plot(mu, post2$yhat.train.mean, asp=1, xlim=c(Z, Z), ylim=c(Z, Z)) abline(a=0, b=1) plot(post1$yhat.train.mean, post2$yhat.train.mean, asp=1, xlim=c(Z, Z), ylim=c(Z, Z)) abline(a=0, b=1) ## End(Not run)