Nothing
library(BART)
B <- getOption('mc.cores', 1)
figures = getOption('figures', default='NONE')
## load survival package for the advanced lung cancer example
data(lung)
N <- length(lung$status)
table(lung$ph.karno, lung$pat.karno)
## if physician's KPS unavailable, then use the patient's
h <- which(is.na(lung$ph.karno))
lung$ph.karno[h] <- lung$pat.karno[h]
times <- lung$time
delta <- lung$status-1 ##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead
## this study reports time in days rather than weeks or months
## coarsening from days to weeks or months will reduce the computational burden
##times <- ceiling(times/30)
times <- ceiling(times/7) ## weeks
##table(times)
table(delta)
## matrix of observed covariates
x.train <- cbind(lung$sex, lung$age, lung$ph.karno)
## lung$sex: Male=1 Female=2
## lung$age: Age in years
## lung$ph.karno: Karnofsky performance score (dead=0:normal=100:by=10)
## rated by physician
dimnames(x.train)[[2]] <- c('M(1):F(2)', 'age(39:82)', 'ph.karno(50:100:10)')
table(x.train[ , 1])
summary(x.train[ , 2])
table(x.train[ , 3])
## run one long MCMC chain in one process
## set.seed(99)
## post <- surv.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)
## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
mc.cores=B, seed=99, K=100)
pre <- surv.pre.bart(times=times, delta=delta, x.train=x.train,
x.test=x.train, K=100)
K <- pre$K
M <- post$ndpost
NK <- N*K
pre$tx.test <- rbind(pre$tx.test, pre$tx.test)
pre$tx.test[ , 2] <- c(rep(1, N*K), rep(2, N*K))
## sex pushed to col 2, since time is always in col 1
pred <- predict(post, newdata=pre$tx.test, mc.cores=B)
for(i in seq(1, N, by=5)) {
##for(i in 1:N) {
h=(i-1)*K+1:K
if(i==1)
plot(c(0, pre$times), c(1, pred$surv.test.mean[h]),
type='s', col=4, lty=2,
ylim=0:1, ylab='S(t, x)', xlab='t (weeks)',)
else lines(c(0, pre$times), c(1, pred$surv.test.mean[h]),
type='s', col=4, lty=2)
lines(c(0, pre$times), c(1, pred$surv.test.mean[h+NK]),
type='s', col=2, lty=3)
}
if(figures!='NONE')
dev.copy2pdf(file=paste(figures, 'lung-ice.pdf', sep='/'))
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