mc.surv.pwbart: 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

surv.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=1L,
                type='pbart',
                transposed=FALSE, nice=19L
              )

mc.surv.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=2L,
                type='pbart',
                transposed=FALSE, nice=19L
              )

mc.recur.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=2L, 
                type='pbart',
                transposed=FALSE, nice=19L
               )

Arguments

x.test	Matrix of covariates to predict y for.
binaryOffset	Mean to add on to y prediction.
treedraws	$treedraws returned from surv.bart, mc.surv.bart, recur.bart or mc.recur.bart.
mc.cores	Number of threads to utilize.
type	Whether to employ Albert-Chib, 'pbart', or Holmes-Held, 'lbart'.
transposed	When running pwbart or mc.pwbart in parallel, it is more memory-efficient to transpose x.test prior to calling the internal versions of these functions.
nice	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

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 survbart which is essentially a list with components:

yhat.test	A matrix with ndpost rows and nrow(x.test) columns. Each row corresponds to a draw f^* from the posterior of f and each column corresponds to a row of x.train. The (i,j) value is f^*(x) for the i^{th} kept draw of f and the j^{th} row of x.train. Burn-in is dropped.
surv.test	test data fits for survival probability: not available for mc.recur.pwbart.
surv.test.mean	mean of surv.test over the posterior samples: not available for mc.recur.pwbart.
haz.test	test data fits for hazard: available for mc.recur.pwbart only.
haz.test.mean	mean of haz.test over the posterior samples: available for mc.recur.pwbart only.
cum.test	test data fits for cumulative hazard: available for mc.recur.pwbart only.
cum.test.mean	mean of cum.test over the posterior samples: available for mc.recur.pwbart only.

See Also

pwbart

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 <- mc.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=8, seed=99)

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

pred <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)

## 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.