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## BART: Bayesian Additive Regression Trees
## Copyright (C) 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
mc.crisk2.pwbart <- function(
x.test, #x.test for an event from either cause
x.test2, #x.test to discriminate between cause 1 and
treedraws, #$treedraws for an event from crisk2.bart/mc.crisk2.bart
treedraws2, #$treedraws for to discriminate
binaryOffset=0, #mean offset to add for an event
binaryOffset2=0, #mean offset to add to discriminate
mc.cores=2L,
type='pbart',
transposed=FALSE,
nice=19L
)
{
if(.Platform$OS.type!='unix')
stop('parallel::mcparallel/mccollect do not exist on windows')
if(length(x.test)==0 | length(x.test2)==0)
stop('both x.test and x.test2 must be provided')
if(nrow(x.test)!=nrow(x.test2))
stop('number of rows in x.test and x.test2 must be equal')
if(!transposed) {
x.test <- t(bartModelMatrix(x.test))
x.test2 <- t(bartModelMatrix(x.test2))
}
p <- length(treedraws$cutpoints)
if(p!=nrow(x.test))
stop(paste0('The number of columns in x.test must be equal to ', p))
p <- length(treedraws2$cutpoints)
if(p!=nrow(x.test2))
stop(paste0('The number of columns in x.test2 must be equal to ', p))
K <- ncol(x.test)
k <- K%/%mc.cores
j <- K
for(i in 1:mc.cores) {
if(i==mc.cores) h <- 1
else h <- j-k
parallel::mcparallel({psnice(value=nice);
pwbart(x.test[ , h:j], treedraws, binaryOffset, 1, TRUE)},
silent=(i!=1))
j <- h-1
}
yhat.test.list <- parallel::mccollect()
j <- K
for(i in 1:mc.cores) {
if(i==mc.cores) h <- 1
else h <- j-k
parallel::mcparallel({psnice(value=nice);
pwbart(x.test2[ , h:j], treedraws2, binaryOffset2, 1, TRUE)},
silent=(i!=1))
j <- h-1
}
yhat.test2.list <- parallel::mccollect()
pred <- list()
pred$binaryOffset <- binaryOffset
pred$binaryOffset2 <- binaryOffset2
x.test <- t(x.test)
pred$tx.test <- x.test
pred$tx.test2 <- t(x.test2)
times <- unique(sort(x.test[ , 1]))
pred$times <- times
K <- length(times)
pred$K <- K
pred$yhat.test <- yhat.test.list[[1]]
if(class(pred$yhat.test)[1]!='matrix') return(pred$yhat.test)
pred$yhat.test2 <- yhat.test2.list[[1]]
if(class(pred$yhat.test)[1]!='matrix') return(pred$yhat.test)
if(mc.cores>1)
for(i in 2:mc.cores) {
pred$yhat.test <- cbind(pred$yhat.test, yhat.test.list[[i]])
pred$yhat.test2 <- cbind(pred$yhat.test2, yhat.test2.list[[i]])
}
H <- nrow(x.test)/K ## the number of different settings
if(type=='pbart') {
pred$prob.test <- pnorm(pred$yhat.test)
pred$prob.test2 <- pnorm(pred$yhat.test2)
}
else if(type=='lbart') {
pred$prob.test <- plogis(pred$yhat.test)
pred$prob.test2 <- plogis(pred$yhat.test2)
}
pred$surv.test <- 1-pred$prob.test
#pred$prob.test2 <- (1-pred$prob.test)*pred$prob.test2
pred$cif.test <- pred$prob.test*pred$prob.test2
pred$cif.test2 <- pred$prob.test*(1-pred$prob.test2)
for(h in 1:H)
for(j in 2:K) {
l <- K*(h-1)+j
pred$cif.test[ , l] <- pred$cif.test[ , l-1]+
pred$surv.test[ , l-1]*pred$cif.test[ , l]
pred$cif.test2[ , l] <- pred$cif.test2[ , l-1]+
pred$surv.test[ , l-1]*pred$cif.test2[ , l]
pred$surv.test[ , l] <- pred$surv.test[ , l-1]*
pred$surv.test[ , l]
}
pred$cif.test.mean <- apply(pred$cif.test, 2, mean)
pred$cif.test2.mean <- apply(pred$cif.test2, 2, mean)
pred$surv.test.mean <- apply(pred$surv.test, 2, mean)
attr(pred, 'class') <- 'crisk2bart'
return(pred)
}
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