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## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017-2018 Robert McCulloch and Rodney Sparapani
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
## You should have received a copy of the GNU General Public License
## along with this program; if not, a copy is available at
## https://www.R-project.org/Licenses/GPL-2
surv.bart <- function(
x.train = matrix(0,0,0),
y.train=NULL, times=NULL, delta=NULL,
x.test = matrix(0,0,0),
K=NULL, events=NULL, ztimes=NULL, zdelta=NULL,
sparse=FALSE, theta=0, omega=1,
a=0.5, b=1, augment=FALSE, rho=NULL,
xinfo=matrix(0,0,0), usequants=FALSE,
## cont=FALSE,
rm.const=TRUE, type='pbart',
ntype=as.integer(
factor(type, levels=c('wbart', 'pbart', 'lbart'))),
k = 2, ## BEWARE: do NOT use k for other purposes below
power = 2, base = 0.95,
offset = NULL, tau.num=c(NA, 3, 6)[ntype],
##binaryOffset = NULL,
ntree = 50L, numcut = 100L,
ndpost = 1000L, nskip = 250L, keepevery = 10L,
##nkeeptrain=ndpost, nkeeptest=ndpost,
##nkeeptreedraws=ndpost,
printevery=100L,
##keeptrainfits=TRUE,
id = NULL, ## only used by surv.bart
seed = 99L, ## only used by mc.surv.bart
mc.cores = 2L, ## ditto
nice=19L ## ditto
)
{
if(is.na(ntype) || ntype==1)
stop("type argument must be set to either 'pbart' or 'lbart'")
x.train <- bartModelMatrix(x.train)
x.test <- bartModelMatrix(x.test)
if(length(rho)==0) rho=ncol(x.train)
if(length(y.train)==0) {
pre <- surv.pre.bart(times, delta, x.train, x.test, K=K,
events=events, ztimes=ztimes, zdelta=zdelta)
y.train <- pre$y.train
x.train <- pre$tx.train
x.test <- pre$tx.test
times <- pre$times
K <- pre$K
##if(length(binaryOffset)==0) binaryOffset <- pre$binaryOffset
}
else {
if(length(unique(sort(y.train)))>2)
stop('y.train has >2 values; make sure you specify times=times & delta=delta')
##if(length(binaryOffset)==0) binaryOffset <- 0
times <- unique(sort(x.train[ , 1]))
K <- length(times)
}
##if(length(binaryOffset)==0) binaryOffset <- qnorm(mean(y.train))
## if(type=='pbart') call <- pbart
## else if(type=='lbart') {
## ##binaryOffset <- 0
## call <- lbart
## }
post <- gbart(x.train=x.train, y.train=y.train,
x.test=x.test, type=type,
sparse=sparse, theta=theta, omega=omega,
a=a, b=b, augment=augment, rho=rho,
xinfo=xinfo, usequants=usequants,
##cont=cont,
rm.const=rm.const,
k=k, power=power, base=base,
offset=offset, tau.num=tau.num,
##binaryOffset=binaryOffset,
ntree=ntree, numcut=numcut,
ndpost=ndpost, nskip=nskip, keepevery=keepevery,
##nkeeptrain=nkeeptrain, nkeeptest=nkeeptest,
##nkeeptestmean=nkeeptestmean,
##nkeeptreedraws=nkeeptreedraws,
printevery=printevery)
if(type!=attr(post, 'class')) return(post)
##post$binaryOffset <- binaryOffset
post$id <- id
post$times <- times
post$K <- K
post$tx.train <- x.train
post$type <- type
## if(keeptrainfits) {
## post$surv.train <- 1-post$prob.train
## H <- nrow(x.train)/K ## the number of different settings
## for(h in 1:H)
## for(j in 2:K) {
## l <- K*(h-1)+j
## post$surv.train[ , l] <-
## post$surv.train[ , l-1]*post$surv.train[ , l]
## }
## post$surv.train.mean <- apply(post$surv.train, 2, mean)
## }
if(length(x.test)>0) {
post$tx.test <- x.test
H <- nrow(x.test)/K ## the number of different settings
post$surv.test <- 1-post$prob.test
## if(type=='pbart') post$surv.test <- 1-pnorm(post$yhat.test)
## else if(type=='lbart') post$surv.test <- 1-plogis(post$yhat.test)
for(h in 1:H)
for(j in 2:K) {
l <- K*(h-1)+j
post$surv.test[ , l] <-
post$surv.test[ , l-1]*post$surv.test[ , l]
}
post$surv.test.mean <- apply(post$surv.test, 2, mean)
}
attr(post, 'class') <- 'survbart'
return(post)
}
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