predict.survbart function

Predicting new observations with a previously fitted BART model

Predicting new observations with a previously fitted BART model

BART is a Bayesian sum-of-trees model.

For a numeric response yy, we have y=f(x)+ey = f(x) + e, where e N(0,sigma2)e ~ N(0,sigma^2).

ff is the sum of many tree models. The goal is to have very flexible inference for the uknown function ff.

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 'survbart' predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

  • object: object returned from previous BART fit with surv.bart

    or mc.surv.bart.

  • newdata: Matrix of covariates to predict the distribution of tt.

  • 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)(f,sigma) \| (x,y) in the numeric yy case and just ff in the binary yy 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)f*(x) (and sigmasigma* in the numeric case) where * denotes a particular draw. The xx is either a row from the training data (x.train) or the test data (x.test).

Returns

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

See Also

surv.bart, mc.surv.bart, surv.pwbart, mc.surv.pwbart, mc.cores.openmp

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)
  • Maintainer: Rodney Sparapani
  • License: GPL (>= 2)
  • Last published: 2024-06-21

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