Nothing
## run BART with recurrent events
recur.bart <- function(
x.train, y.train=NULL, times=NULL, delta=NULL,
x.test = matrix(0.0, 0L, 0L),
x.test.nogrid = FALSE, ## you may not need the whole grid
keepcall = FALSE, ## the call object can get rather large
k = 2.0, ## BEWARE: do NOT use k for other purposes below
power = 2.0, base = 0.95,
binaryOffset = NULL,
ntree = 50L,
ndpost = 10000L, nskip = 250L,
printevery = 100L,
keepevery = 10L, keeptrainfits = TRUE,
usequants = FALSE, numcut = 100L, printcutoffs = 0L,
verbose = TRUE,
seed = 99L, ## only used by mc.recur.bart
mc.cores = 2L, ## ditto
nice=19L ## ditto
)
{
if(length(y.train)==0) {
if(length(binaryOffset)==0) {
lambda <- sum(delta)/sum(apply(times, 1, max))
binaryOffset <- qnorm(1-exp(-lambda))
}
recur <- recur.pre.bart(times, delta, x.train, x.test)
y.train <- recur$y.train
x.train <- recur$X.train
x.test <- recur$X.test
times <- recur$times
K <- recur$K
}
else {
if(length(binaryOffset)==0) binaryOffset <- 0
times <- unique(sort(x.train[ , 1]))
K <- length(times)
}
post <- bart(x.train=x.train, y.train=y.train, x.test=x.test,
sigest = NA_real_, sigdf = 3.0, sigquant = 0.90,
k=k, keepcall=keepcall,
power=power, base=base,
binaryOffset=binaryOffset,
ntree=ntree,
ndpost=ndpost, nskip=nskip,
printevery=printevery, keepevery=keepevery, keeptrainfits=keeptrainfits,
usequants=usequants, numcut=numcut, printcutoffs=printcutoffs,
verbose=verbose)
##post$call <- NULL
post$binaryOffset <- NULL
post$times <- times
post$K <- K
post$x.train <- x.train
## if(keepevery>1L) {
## thin <- seq(1, ndpost, keepevery)
## post$yhat.train <- post$yhat.train[thin, ]
## post$varcount <- post$varcount[thin, ]
## }
post$cum.train <- pnorm(post$yhat.train)
post$haz.train <- post$cum.train
H <- nrow(x.train)
for(h in 1:H) {
j <- which(x.train[h, 1]==times) ## for grid points only
if(j==1) post$haz.train[ , h] <- post$haz.train[ , h]/times[1]
else {
post$haz.train[ , h] <- post$haz.train[ , h]/(times[j]-times[j-1])
post$cum.train[ , h] <- post$cum.train[ , h-1]+post$cum.train[ , h]
}
}
## H <- nrow(x.train)
## time <- 0
## for(h in 1:H) {
## prev <- time
## time <- x.train[h, 1]
## if(time==post$times[1]) post$haz.train[ , h] <- post$haz.train[ , h]/time
## else {
## post$haz.train[ , h] <- post$haz.train[ , h]/(time-prev)
## post$cum.train[ , h] <- post$cum.train[ , h-1]+post$cum.train[ , h]
## }
## }
if(length(x.test)>0) {
##if(keepevery>1L) post$yhat.test <- post$yhat.test[thin, ]
post$x.test <- x.test
post$haz.test <- pnorm(post$yhat.test)
if(!x.test.nogrid) {
post$cum.test <- post$haz.test
H <- nrow(x.test)
for(h in 1:H) {
j <- which(x.test[h, 1]==times) ## for grid points only
if(j==1) post$haz.test[ , h] <- post$haz.test[ , h]/times[1]
else {
post$haz.test[ , h] <- post$haz.test[ , h]/(times[j]-times[j-1])
post$cum.test[ , h] <- post$cum.test[ , h-1]+post$cum.test[ , h]
}
}
}
## H <- nrow(x.test)
## time <- 0
## for(h in 1:H) {
## prev <- time
## time <- x.test[h, 1]
## if(time==post$times[1]) post$haz.test[ , h] <- post$haz.test[ , h]/time
## else {
## post$haz.test[ , h] <- post$haz.test[ , h]/(time-prev)
## post$cum.test[ , h] <- post$cum.test[ , h-1]+post$cum.test[ , h]
## }
## }
}
return(post)
}
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