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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
recur.pwbart <- function(
x.test, #x matrix to predict at with time points expanded
treedraws, #$treedraws for from surv.bart/mc.surv.bart
binaryOffset=0, #mean to add on
mc.cores=1L,
type='pbart',
transposed=FALSE,
nice=19L # mc.surv.pwbart only
)
{
if(!transposed) x.test <- t(bartModelMatrix(x.test))
p <- length(treedraws$cutpoints)
if(p!=nrow(x.test))
stop(paste0('The number of columns in x.test must be equal to ', p))
pred <- list()
pred$yhat.test <- pwbart(x.test, treedraws, binaryOffset, mc.cores, TRUE)
if(class(pred$yhat.test)[1]!='matrix') return(pred$yhat.test)
x.test <- t(x.test)
pred$tx.test <- x.test
times <- unique(sort(x.test[ , 1]))
pred$times <- times
K <- length(times)
pred$K <- K
if(type=='pbart') pred$prob.test <- pnorm(pred$yhat.test)
else if(type=='lbart') pred$prob.test <- plogis(pred$yhat.test)
pred$haz.test <- pred$prob.test
pred$cum.test <- pred$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) pred$haz.test[ , h] <- pred$haz.test[ , h]/times[1]
else {
pred$haz.test[ , h] <- pred$haz.test[ , h]/(times[j]-times[j-1])
pred$cum.test[ , h] <- pred$cum.test[ , h-1]+pred$cum.test[ , h]
}
}
pred$prob.test.mean <- apply(pred$prob.test, 2, mean)
pred$haz.test.mean <- apply(pred$haz.test, 2, mean)
pred$cum.test.mean <- apply(pred$cum.test, 2, mean)
pred$binaryOffset <- binaryOffset
attr(pred, 'class') <- 'recurbart'
return(pred)
}
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