predict.crisk2bart {BART} | R Documentation |
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 |
|
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 |
... |
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 an object of type crisk2bart
with predictions
corresponding to newdata
and newdata2
.
Robert McCulloch: robert.e.mcculloch@gmail.com,
Rodney Sparapani: rsparapa@mcw.edu.
Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298 <doi:10.1214/09-AOAS285>.
Chipman, H., George, E., and McCulloch R. (2006) Bayesian Ensemble Learning. Advances in Neural Information Processing Systems 19, Scholkopf, Platt and Hoffman, Eds., MIT Press, Cambridge, MA, 265-272.
Friedman, J.H. (1991) Multivariate adaptive regression splines. The Annals of Statistics, 19, 1–67.
crisk2.bart
, mc.crisk2.bart
, mc.crisk2.pwbart
, mc.cores.openmp
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) } ## 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, 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))) ## End(Not run)