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## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017 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
crisk.bart <- function(
x.train=matrix(0,0,0), y.train=NULL,
x.train2=x.train, y.train2=NULL,
times=NULL, delta=NULL, K=NULL,
x.test=matrix(0,0,0), x.test2=x.test, cond=NULL,
sparse=FALSE, theta=0, omega=1,
a=0.5, b=1, augment=FALSE, rho=NULL, rho2=NULL,
xinfo=matrix(0,0,0), xinfo2=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, offset2 = NULL,
tau.num=c(NA, 3, 6)[ntype], ##tau.num2=c(NA, 3, 6)[ntype],
##binaryOffset = NULL, binaryOffset2 = NULL,
ntree = 50L, numcut = 100L,
ndpost = 1000L, nskip = 250L,
keepevery = 10L,
##nkeeptrain=ndpost, nkeeptest=ndpost,
##nkeeptestmean=ndpost,
##nkeeptreedraws=ndpost,
printevery=100L,
##treesaslists=FALSE,
##keeptrainfits=TRUE,
id = NULL,
seed=99, ## mc.crisk.bart only
mc.cores=2, ## mc.crisk.bart only
nice=19L ## mc.crisk.bart only
)
{
if(is.na(ntype) || ntype==1)
stop("type argument must be set to either 'pbart' or 'lbart'")
x.train2 <- bartModelMatrix(x.train2)
x.test2 <- bartModelMatrix(x.test2)
x.train <- bartModelMatrix(x.train)
x.test <- bartModelMatrix(x.test)
if(length(rho)==0) rho=ncol(x.train)
if(length(rho2)==0) rho2=ncol(x.train2)
if(length(y.train)==0) {
pre <- crisk.pre.bart(times, delta, x.train, x.test,
x.train2, x.test2, K=K)
y.train <- pre$y.train
x.train <- pre$tx.train
x.test <- pre$tx.test
y.train2 <- pre$y.train2
x.train2 <- pre$tx.train2
x.test2 <- pre$tx.test2
times <- pre$times
K <- pre$K
if(length(cond)==0) cond <- pre$cond
##if(length(binaryOffset)==0) binaryOffset <- pre$binaryOffset
##if(length(binaryOffset2)==0) binaryOffset2 <- pre$binaryOffset2
}
else {
if(length(x.train)>0 & length(x.train2)>0 & nrow(x.train)!=nrow(x.train2))
stop('number of rows in x.train and x.train2 must be equal')
if(length(x.test)>0 & length(x.test2)>0 & nrow(x.test)!=nrow(x.test2))
stop('number of rows in x.test and x.test2 must be equal')
##if(length(binaryOffset)==0) binaryOffset <- 0
##if(length(binaryOffset2)==0) binaryOffset2 <- 0
times <- unique(sort(x.train[ , 1]))
K <- length(times)
}
if(length(xinfo)==0) {
temp = bartModelMatrix(x.train2[cond, ], numcut, usequants=usequants,
##cont=cont,
xinfo=xinfo, rm.const=rm.const)
x.train2 = t(temp$X)
numcut2 = temp$numcut
xinfo2 = temp$xinfo
## if(length(x.test2)>0)
## x.test2 = t(bartModelMatrix(x.test2[ , temp$rm.const]))
if(length(x.test2)>0) {
x.test2 = bartModelMatrix(x.test2)
x.test2 = t(x.test2[ , temp$rm.const])
}
rm.const2 <- temp$rm.const
rm(temp)
temp = bartModelMatrix(x.train, numcut, usequants=usequants,
##cont=cont,
xinfo=xinfo, rm.const=rm.const)
x.train = t(temp$X)
numcut = temp$numcut
xinfo = temp$xinfo
## if(length(x.test)>0)
## x.test = t(bartModelMatrix(x.test[ , temp$rm.const]))
if(length(x.test)>0) {
x.test = bartModelMatrix(x.test)
x.test = t(x.test[ , temp$rm.const])
}
rm.const <- temp$rm.const
rm(temp)
xinfo2[1, ] <- xinfo[1, ] ## same time grid
transposed <- TRUE
}
else {
x.train2=as.matrix(x.train2[cond, ])
rm.const <- 1:ncol(x.train)
rm.const2 <- 1:ncol(x.train2)
transposed <- FALSE
}
## if(length(binaryOffset)==0)
## binaryOffset <- qnorm(mean(y.train))
## if(length(binaryOffset2)==0)
## binaryOffset2 <- qnorm(mean(y.train2[cond]))
## if(type=='pbart') call <- pbart
## else if(type=='lbart') {
## ##binaryOffset <- 0
## ##binaryOffset2 <- 0
## call <- lbart
## }
post <- gbart(x.train=x.train, y.train=y.train,
x.test=x.test, type=type,
sparse=sparse, theta=theta, omega=omega,
a=a, b=b, augment=augment, rho=rho,
xinfo=xinfo, usequants=usequants,
##cont=cont,
rm.const=rm.const,
k=k, power=power, base=base,
offset=offset, tau.num=tau.num,
##binaryOffset=binaryOffset,
ntree=ntree, numcut=numcut,
ndpost=ndpost, nskip=nskip,
keepevery=keepevery, ##nkeeptrain=0,
##nkeeptest=nkeeptest, #nkeeptestmean=nkeeptestmean,
##nkeeptreedraws=nkeeptreedraws,
printevery=printevery,
transposed=transposed) #, treesaslists=treesaslists)
if(type!=attr(post, 'class')) return(post)
##post2 <- call(x.train=as.matrix(x.train2[cond, ]),
post2 <- gbart(x.train=x.train2, y.train=y.train2[cond],
x.test=x.test2, type=type,
sparse=sparse, theta=theta, omega=omega,
a=a, b=b, augment=augment, rho=rho2,
xinfo=xinfo2, usequants=usequants,
##cont=cont,
rm.const=rm.const,
k=k, power=power, base=base,
offset=offset2, tau.num=tau.num,
##binaryOffset=binaryOffset2,
ntree=ntree, numcut=numcut2,
ndpost=ndpost, nskip=nskip,
keepevery=keepevery, ##nkeeptrain=0,
##nkeeptest=nkeeptest, #nkeeptestmean=nkeeptestmean,
##nkeeptreedraws=nkeeptreedraws,
printevery=printevery,
transposed=transposed) #, treesaslists=treesaslists)
if(type!=attr(post2, 'class')) return(post2)
##post$binaryOffset <- binaryOffset
post$offset2 <- post2$offset
##post$binaryOffset2 <- post2$binaryOffset
post$id <- id
post$times <- times
post$K <- K
if(!transposed) {
post$tx.train <- x.train
post$tx.train2 <- x.train2
} else {
post$tx.train <- t(x.train)
post$tx.train2 <- t(x.train2)
}
post$type <- type
post$cond <- cond
post$treedraws2 <- post2$treedraws
post$varcount2 <- post2$varcount
post$varcount2.mean <- post2$varcount.mean
post$varprob2 <- post2$varprob
post$varprob2.mean <- post2$varprob.mean
post$rm.const <- rm.const
post$rm.const2 <- rm.const2
post$yhat.train <- NULL
post$yhat.train.mean <- NULL
if(length(x.test)>0) {
if(!transposed) {
post$tx.test <- x.test
post$tx.test2 <- x.test2
} else {
post$tx.test <- t(x.test)
post$tx.test2 <- t(x.test2)
}
H <- nrow(post$tx.test)/K ## the number of different settings
post$yhat.test2 <- post2$yhat.test
post$prob.test2 <- post2$prob.test
## if(type=='pbart') {
## post$prob.test <- pnorm(post$yhat.test)
## post$prob.test2 <- pnorm(post$yhat.test2)
## }
## else if(type=='lbart') {
## post$prob.test <- plogis(post$yhat.test)
## post$prob.test2 <- plogis(post$yhat.test2)
## }
post$surv.test <- (1-post$prob.test)*(1-post$prob.test2)
post$prob.test2 <- (1-post$prob.test)*post$prob.test2
post$cif.test <- post$prob.test
post$cif.test2 <- post$prob.test2
for(h in 1:H)
for(j in 2:K) {
l <- K*(h-1)+j
post$cif.test[ , l] <- post$cif.test[ , l-1]+post$surv.test[ , l-1]*post$cif.test[ , l]
post$cif.test2[ , l] <- post$cif.test2[ , l-1]+post$surv.test[ , l-1]*post$cif.test2[ , l]
post$surv.test[ , l] <- post$surv.test[ , l-1]*post$surv.test[ , l]
}
post$cif.test.mean <- apply(post$cif.test, 2, mean)
post$cif.test2.mean <- apply(post$cif.test2, 2, mean)
post$surv.test.mean <- apply(post$surv.test, 2, mean)
}
attr(post, 'class') <- 'criskbart'
return(post)
}
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