gbart {BART}  R Documentation 
Generalized BART for continuous and binary outcomes
Description
BART is a Bayesian “sumoftrees” model.
For a numeric response y
, we have
y = f(x) + \epsilon
,
where \epsilon \sim 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.
Usage
gbart(
x.train, y.train,
x.test=matrix(0,0,0), type='wbart',
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,
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(y.train)),
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 = 1L, ## mc.gbart only
nice = 19L, ## mc.gbart only
seed = 99L ## mc.gbart only
)
mc.gbart(
x.train, y.train,
x.test=matrix(0,0,0), type='wbart',
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,
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(y.train)),
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, nice = 19L, seed = 99L
)
Arguments
x.train 
Explanatory variables for training (in sample)
data. 
y.train 
Continuous or binary dependent variable for training (in sample) data. 
x.test 
Explanatory variables for test (out of sample)
data. Should have same structure as 
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. 
sigest 
The prior for the error variance
( 
sigdf 
Degrees of freedom for error variance prior.
Not used if 
sigquant 
The quantile of the prior that the rough estimate
(see 
k 
For numeric 
power 
Power parameter for tree prior. 
base 
Base parameter for tree prior. 
lambda 
The scale of the prior for the variance. If 
tau.num 
The numerator in the 
offset 
Continous BART operates on 
w 
Vector of weights which multiply the standard deviation.
Not used if 
ntree 
The number of trees in the sum. 
numcut 
The number of possible values of 
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 
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). 
Details
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
.
For x.train
/x.test
with missing data elements, gbart
will singly impute them with hot decking. For one or more missing
covariates, recordlevel hotdecking imputation deWaPann11 is
employed that is biased towards the null, i.e., nonmissing values
from another record are randomly selected regardless of the
outcome. Since mc.gbart
runs multiple gbart
threads in
parallel, mc.gbart
performs multiple imputation with hot
decking, i.e., a separate imputation for each thread. This
recordlevel hotdecking imputation is biased towards the null, i.e.,
nonmissing values from another record are randomly selected
regardless of y.train
.
Value
gbart
returns an object of type gbart
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 
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 ( 
See Also
Examples
##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3].5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0 #y = f(x) + sigma*z , z~N(0,1)
n = 100 #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
Ey = f(x)
y=Ey+sigma*rnorm(n)
lmFit = lm(y~.,data.frame(x,y)) #compare lm fit to BART later
##test BART with token run to ensure installation works
set.seed(99)
bartFit = wbart(x,y,nskip=5,ndpost=5)
## Not run:
##run BART
set.seed(99)
bartFit = wbart(x,y)
##compare BART fit to linear matter and truth = Ey
fitmat = cbind(y,Ey,lmFit$fitted,bartFit$yhat.train.mean)
colnames(fitmat) = c('y','Ey','lm','bart')
print(cor(fitmat))
## End(Not run)