predict.wbart {BART}  R Documentation 
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.
## S3 method for class 'wbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
object 

newdata 
Matrix of covariates to predict 
mc.cores 
Number of threads to utilize. 
openmp 
Logical value dictating whether OpenMP is utilized for parallel
processing. Of course, this depends on whether OpenMP is available
on your system which, by default, is verified with 
... 
Other arguments which will be passed on to 
BART is an 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).
Returns a matrix of predictions corresponding to newdata
.
wbart
, mc.wbart
,
pwbart
, mc.pwbart
,
mc.cores.openmp
##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
y=f(x)
##test BART with token run to ensure installation works
set.seed(99)
post = wbart(x,y,nskip=5,ndpost=5)
x.test = matrix(runif(500*10),500,10)
## Not run:
##run BART
set.seed(99)
post = wbart(x,y)
x.test = matrix(runif(500*10),500,10)
pred = predict(post, x.test, mu=mean(y))
plot(apply(pred, 2, mean), f(x.test))
## End(Not run)