predict.mbart: 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

## S3 method for class 'mbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'mbart2'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

object	object returned from previous BART fit with mbart or mbart2.
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 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).

Value

Returns an object of type mbart with predictions corresponding to newdata.

See Also

mbart, mbart2

Examples

## 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)

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