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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
mbart <- function(
x.train, y.train,
x.test=matrix(0,0,0), type='pbart',
ntype=as.integer(
factor(type,
levels=c('wbart', 'pbart', 'lbart'))),
sparse=FALSE, theta=0, omega=1,
a=0.5, b=1, augment=FALSE, rho=NULL,
xinfo=matrix(0,0,0), usequants=FALSE,
rm.const=TRUE,
k=2, power=2, base=0.95,
##sigest=NA, sigdf=3, sigquant=0.90, lambda=NA,
tau.num=c(NA, 3, 6)[ntype],
offset=NULL, ##w=rep(1, length(y.train)),
ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
ndpost=1000L, nskip=100L,
keepevery=c(1L, 10L, 10L)[ntype],
printevery=100L, transposed=FALSE,
hostname=FALSE,
mc.cores = 2L, nice = 19L, seed = 99L
)
{
if(type=='wbart' || is.na(ntype))
stop("type argument must be set to either 'pbart' or 'lbart'")
cats <- unique(sort(y.train))
K <- length(cats)
if(K<2)
stop("there must be at least 2 categories")
L=length(offset)
if(!(L %in% c(0, K)))
stop(paste0("length of offset argument must be 0 or ", K))
if(!transposed) {
temp = bartModelMatrix(x.train, numcut, usequants=usequants,
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)
}
post <- list()
post$K <- K
post$cats <- cats
N <- length(y.train)
P <- nrow(x.train) ## transposed
## post$yhat.train <- matrix(nrow=ndpost, ncol=N*K)
## post$prob.train <- matrix(nrow=ndpost, ncol=N*K)
## post$comp.train <- matrix(nrow=ndpost, ncol=N*K)
if(length(x.test)) {
Q <- ncol(x.test) ## transposed
post$yhat.test <- matrix(nrow=ndpost, ncol=Q*K)
post$prob.test <- matrix(nrow=ndpost, ncol=Q*K)
post$comp.test <- matrix(nrow=ndpost, ncol=Q*K)
}
else Q <- 0
L <- K-1
post$varcount <- as.list(1:L)
post$varprob <- as.list(1:L)
##post$varcount <- array(dim=c(ndpost, P, L))
post$varcount.mean <- matrix(nrow=L, ncol=P)
##post$varprob <- array(dim=c(ndpost, P, L))
post$varprob.mean <- matrix(nrow=L, ncol=P)
post$offset <- 0
post$treedraws <- list()
post$treedraws$trees <- as.list(1:L)
##post$rm.const <- as.list(1:L)
post.list <- as.list(1:L)
for(h in 1:K) {
cond <- which(y.train>=cats[h])
if(h<K) {
post.list[[h]] <-
gbart(x.train=x.train[ , cond],
y.train=(y.train[cond]==h)*1,
x.test=x.test,
type=type, ntype=ntype,
sparse=sparse, theta=theta, omega=omega,
a=a, b=b, augment=augment, rho=rho,
xinfo=xinfo, usequants=usequants,
rm.const=rm.const,
k=k, power=power, base=base,
tau.num=tau.num,
offset=offset[h],
ntree=ntree, numcut=numcut,
ndpost=ndpost, nskip=nskip,
keepevery=keepevery,
printevery=printevery, transposed=TRUE,
hostname=hostname)
if(attr(post.list[[h]], 'class')!=type)
return(post.list[[h]])
}
## for(i in 1:N) {
## j <- (i-1)*K+h
## if(i %in% cond) {
## l <- which(i==cond)
## post$yhat.train[ , j] <- post.list[[h]]$yhat.train[ , l]
## if(h==1) {
## post$comp.train[ , j] <-
## 1-post.list[[h]]$prob.train[ , l]
## post$prob.train[ , j] <- post.list[[h]]$prob.train[ , l]
## } else {
## post$comp.train[ , j] <- post$comp.train[ , j-1]*
## (1-post.list[[h]]$prob.train[ , l])
## post$prob.train[ , j] <- post$comp.train[ , j-1]*
## post.list[[h]]$prob.train[ , l]
## }
## }
## else {
## post$yhat.train[ , j] <- -Inf
## post$prob.train[ , j] <- 0
## post$comp.train[ , j] <- 1
## }
## }
if(Q>0)
for(i in 1:Q) {
j <- (i-1)*K+h
if(h==K)
post$prob.test[ , j] <- post$comp.test[ , j-1]
else {
post$yhat.test[ , j] <- post.list[[h]]$yhat.test[ , i]
if(h==1) {
post$comp.test[ , j] <-
1-post.list[[h]]$prob.test[ , i]
post$prob.test[ , j] <- post.list[[h]]$prob.test[ , i]
}
else {
post$comp.test[ , j] <- post$comp.test[ , j-1]*
(1-post.list[[h]]$prob.test[ , i])
post$prob.test[ , j] <- post$comp.test[ , j-1]*
post.list[[h]]$prob.test[ , i]
}
}
}
if(h<K) {
post$varcount[[h]] <- post.list[[h]]$varcount
post$varprob[[h]] <- post.list[[h]]$varprob
for(j in 1:P) {
##post$varcount[ , j, h] <- post.list[[h]]$varcount[ , j]
post$varcount.mean[h, j] <- post.list[[h]]$varcount.mean[j]
##post$varprob[ , j, h] <- post.list[[h]]$varprob[ , j]
post$varprob.mean[h, j] <- post.list[[h]]$varprob.mean[j]
}
post$offset[h] <- post.list[[h]]$offset
##post$rm.const[[h]] <- post.list[[h]]$rm.const
post$treedraws$trees[[h]] <- post.list[[h]]$treedraws$trees
}
}
post$treedraws$cutpoints <- post.list[[1]]$treedraws$cutpoints
dimnames(post$varcount.mean)[[2]] <- dimnames(post$varcount[[1]])[[2]]
dimnames(post$varprob.mean)[[2]] <- dimnames(post$varprob[[1]])[[2]]
post$rm.const <- post.list[[1]]$rm.const
post$type <- type
post$comp.test <- NULL
## post$prob.train.mean <- apply(post$prob.train, 2, mean)
## post$comp.train.mean <- apply(post$comp.train, 2, mean)
if(Q>0) {
post$prob.test.mean <- apply(post$prob.test, 2, mean)
##post$comp.test.mean <- apply(post$comp.test, 2, mean)
}
attr(post, 'class') <- 'mbart'
return(post)
}
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