pwbart: Predicting new observations with a previously fitted BART...

In BART: Bayesian Additive Regression Trees

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” 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

pwbart( x.test, treedraws, mu=0, mc.cores=1L, transposed=FALSE,
        dodraws=TRUE,
        nice=19L ## mc.pwbart only
      )

mc.pwbart( x.test, treedraws, mu=0, mc.cores=2L, transposed=FALSE,
           dodraws=TRUE,
           nice=19L ## mc.pwbart only
         )

Arguments

x.test	Matrix of covariates to predict y for.
treedraws	$treedraws returned from wbart or pbart.
mu	Mean to add on to y prediction.
mc.cores	Number of threads to utilize.
transposed	When running pwbart or mc.pwbart in parallel, it is more memory-efficient to transpose x.test prior to calling the internal versions of these functions.
dodraws	Whether to return the draws themselves (the default), or whether to return the mean of the draws as specified by dodraws=FALSE.
nice	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

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).

Value

Returns a matrix of predictions corresponding to x.test.

See Also

wbart predict.wbart

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
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 = pwbart(post$treedraws, x.test, mu=mean(y))

plot(apply(pred, 2, mean), f(x.test))


## End(Not run)

BART documentation built on June 22, 2024, 11:33 a.m.