predict.pbart {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 'pbart' predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
object |
|
newdata |
Matrix of covariates to predict the distribution of t. |
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 pbart
with predictions corresponding to newdata
.
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.
surv.bart
, mc.surv.bart
, surv.pwbart
, mc.surv.pwbart
, mc.cores.openmp
## load the advanced lung cancer example data(lung) group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno times <- lung[group, 2] ##lung$time delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead ##delta: 0=censored, 1=dead ## this study reports time in days rather than months like other studies ## coarsening from days to months will reduce the computational burden times <- ceiling(times/30) summary(times) table(delta) x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates ## lung$age: Age in years ## lung$sex: Male=1 Female=2 ## lung$ph.karno: Karnofsky performance score (dead=0:normal=100:by=10) ## rated by physician dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)') summary(x.train[ , 1]) table(x.train[ , 2]) table(x.train[ , 3]) x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios dimnames(x.test)[[2]] <- dimnames(x.train)[[2]] i <- 1 for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) { x.test[i, ] <- c(age, sex, ph.karno) i <- i+1 } ## this x.test is relatively small, but often you will want to ## predict for a large x.test matrix which may cause problems ## due to consumption of RAM so we can predict separately ## mcparallel/mccollect do not exist on windows if(.Platform$OS.type=='unix') { ##test BART with token run to ensure installation works set.seed(99) post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1) pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test) pred <- predict(post, pre$tx.test) ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset) } ## Not run: ## run one long MCMC chain in one process set.seed(99) post <- surv.bart(x.train=x.train, times=times, delta=delta) ## run "mc.cores" number of shorter MCMC chains in parallel processes ## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta, ## mc.cores=5, seed=99) pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test) pred <- predict(post, pre$tx.test) ## let's look at some survival curves ## first, a younger group with a healthier KPS ## age 50 with KPS=90: males and females ## males: row 17, females: row 23 x.test[c(17, 23), ] low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring low.risk.females <- 22*post$K+1:post$K plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue', main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1)) points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red') ## End(Not run)