predict.crisk2bart {BART}R Documentation

Predicting new observations with a previously fitted BART model


BART is a Bayesian “sum-of-trees” 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.


## S3 method for class 'crisk2bart'
predict(object, newdata, newdata2, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)



object returned from previous BART fit with crisk2.bart or mc.crisk2.bart.


Matrix of covariates to predict the distribution of t1.


Matrix of covariates to predict the distribution of t2.


Number of threads to utilize.


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 mc.cores.openmp.


Other arguments which will be passed on to pwbart.


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 an object of type crisk2bart with predictions corresponding to newdata and newdata2.

See Also

crisk2.bart, mc.crisk2.bart, mc.crisk2.pwbart, mc.cores.openmp



delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk2.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk2.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk2.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk2.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

## check <- mc.crisk2.pwbart(post$tx.test, post$tx.test,
##                           post$treedraws, post$treedraws2,
##                           post$binaryOffset,
##                           post$binaryOffset2, mc.cores=8)
check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))

## End(Not run)

[Package BART version 2.9 Index]