mbart2 {BART}  R Documentation 
BART is a Bayesian “sumoftrees” model.
For numeric response y
, we have y = f(x) +\epsilon
y = f(x) + e, where \epsilon \sim N(0, 1)
.
For a multinomial response y
, P(Y=y  x) = F(f(x))
,
where F
denotes the standard Normal CDF (probit link) or the
standard Logistic CDF (logit link).
In both cases, 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.
mbart2(
x.train, y.train,
x.test=matrix(0,0,0), type='lbart',
ntype=as.integer(
factor(type,
levels=c('wbart', 'pbart', 'lbart'))),
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,
k=2, power=2, base=0.95,
tau.num=c(NA, 3, 6)[ntype],
offset=NULL,
ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
ndpost=1000L, nskip=100L,
keepevery=c(1L, 10L, 10L)[ntype],
printevery=100L, transposed=FALSE,
hostname=FALSE,
mc.cores = 2L, ## mc.bart only
nice = 19L, ## mc.bart only
seed = 99L ## mc.bart only
)
mc.mbart2(
x.train, y.train,
x.test=matrix(0,0,0), type='lbart',
ntype=as.integer(
factor(type,
levels=c('wbart', 'pbart', 'lbart'))),
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,
k=2, power=2, base=0.95,
tau.num=c(NA, 3, 6)[ntype],
offset=NULL,
ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
ndpost=1000L, nskip=100L,
keepevery=c(1L, 10L, 10L)[ntype],
printevery=100L, transposed=FALSE,
hostname=FALSE,
mc.cores = 2L, ## mc.bart only
nice = 19L, ## mc.bart only
seed = 99L ## mc.bart only
)
x.train 
Explanatory variables for training (in sample) data. 
y.train 
Categorical dependent variable for training (in sample) data. 
x.test 
Explanatory variables for test (out of sample) data. 
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 
omega 
Set 
a 
Sparse parameter for 
b 
Sparse parameter for 
rho 
Sparse parameter: typically 
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. 
k 
For categorical 
power 
Power parameter for tree prior. 
base 
Base parameter for tree prior. 
tau.num 
The numerator in the 
offset 
With Multinomial
BART, the centering is 
ntree 
The number of trees in the sum. 
numcut 
The number of possible values of c (see usequants).
If a single number if given, this is used for all variables.
Otherwise a vector with length equal to ncol(x.train) is required,
where the 
ndpost 
The number of posterior draws returned. 
nskip 
Number of MCMC iterations to be treated as burn in. 
keepevery 
Every keepevery draw is kept to be returned to the user. 
printevery 
As the MCMC runs, a message is printed every printevery draws. 
transposed 
When running 
hostname 
When running on a cluster occasionally it is useful
to track on which node each chain is running; to do so
set this argument to 
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 an Bayesian MCMC method.
At each MCMC interation, we produce a draw from
f
in the categorical 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)
where * denotes a particular draw.
The x
is either a row from the training data (x.train).
mbart2
returns an object of type mbart2
which is
essentially a list.
yhat.train 
A matrix with 
yhat.train.mean 
train data fits = mean of 
varcount 
a matrix with 
In addition, the list
has a offset
vector giving the value used.
Note that in the multinomial y
case yhat.train
is
f(x) + offset[j]
.
N=500
set.seed(12)
x1=runif(N)
x2=runif(N, max=1x1)
x3=1x1x2
x.train=cbind(x1, x2, x3)
y.train=0
for(i in 1:N)
y.train[i]=sum((1:3)*rmultinom(1, 1, x.train[i, ]))
table(y.train)/N
##test mbart2 with token run to ensure installation works
set.seed(99)
post = mbart2(x.train, y.train, nskip=1, ndpost=1)
## Not run:
set.seed(99)
post=mbart2(x.train, y.train, x.train)
##mc.post=mbart2(x.train, y.train, x.test, mc.cores=8, seed=99)
K=3
i=seq(1, N*K, K)1
for(j in 1:K)
print(cor(x.train[ , j], post$prob.test.mean[i+j])^2)
## End(Not run)