predict.crisk2bart: 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

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

Value

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

Arguments

object: object returned from previous BART fit with crisk2.bart or mc.crisk2.bart.
newdata: Matrix of covariates to predict the distribution of \(t1\).
newdata2: Matrix of covariates to predict the distribution of \(t2\).
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 mc.cores.openmp.
...: Other arguments which will be passed on to pwbart.

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).

Examples

Run this code


data(transplant)

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
table(times)

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,
                               keepevery=1)

        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)
}

if (FALSE) {

## 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,
                 mc.cores=8)

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)))

}