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step.ts.fit.boot <- function(y, XREG, Z, PSI, opz, n.boot=10, size.boot=NULL, jt=FALSE,
nonParam=TRUE, random=FALSE, break.boot=n.boot){
#random se TRUE prende valori random quando e' errore: comunque devi modificare qualcosa (magari con it.max)
# per fare restituire la dev in corrispondenza del punto psi-random
#nonParm. se TRUE implemneta il case resampling. Quello semiparam dipende dal non-errore di
#----------------------------------
# sum.of.squares<-function(obj.seg){
# #computes the "correct" SumOfSquares from a segmented" fit
# b<-obj.seg$obj$coef
# X<-qr.X(obj.seg$obj$qr) #X<-model.matrix(obj.seg)
# X<-X[,!is.na(b)]
# b<-b[!is.na(b)]
# rev.b<-rev(b)
# rev.b[1:length(obj.seg$psi)]<-0
# b<-rev(rev.b)
# new.fitted<-drop(X%*%b)
# new.res<- obj.seg$obj$residuals + obj.seg$obj$fitted - new.fitted
# ss<-sum(new.res^2)
# ss
# }
adj.psi <- function(psii, LIM) {
pmin(pmax(LIM[1, ], psii), LIM[2, ])
}
#--------
extract.psi<-function(lista){
#serve per estrarre il miglior psi..
dev.values<-lista[[1]][-1] #remove the 1st one referring to model without psi
psi.values<-lista[[2]][-1] #remove the 1st one (NA)
dev.ok<-min(dev.values)
id.dev.ok<-which.min(dev.values)
if(is.list(psi.values)) psi.values<-matrix(unlist(psi.values),
nrow=length(dev.values), byrow=TRUE)
if(!is.matrix(psi.values)) psi.values<-matrix(psi.values)
psi.ok<-psi.values[id.dev.ok,]
r<-list(SumSquares.no.gap=dev.ok, psi=psi.ok)
r
}
#browser()
if(is.null(opz$seed)){
mY <- mean(y)
sepDec<-if(options()$OutDec==".") "\\." else "\\,"
vv <- strsplit(paste(strsplit(paste(mY), sepDec)[[1]], collapse=""),"")[[1]]
vv<-vv[vv!="0"]
vv=na.omit(vv[1:5])
seed <-eval(parse(text=paste(vv, collapse="")))
if(is.null(seed)) seed <- 1
set.seed(seed)
} else {
if(is.na(opz$seed)) {
seed <-eval(parse(text=paste(sample(0:9, size=6), collapse="")))
set.seed(seed)
} else {
seed <-opz$seed
set.seed(opz$seed)
}
}
#-------------
#obj<- jump.fit(y, XREG=x.lin, Z=Xtrue, PSI, w=ww, offs, opz, return.all.sol=FALSE)
#--------------
visualBoot<-opz$display
opz$display<-FALSE
#opz.boot<-opz
#opz.boot$pow=c(1,1) #c(1.1,1.2)
opz1<-opz
opz1$it.max <- 0
opz0<-opz
opz0$agg<-.2
n<-length(y)
rangeZ <- apply(Z, 2, range) #serve sempre
alpha <- opz$alpha
limZ <- apply(Z, 2, quantile, names = FALSE, probs = alpha)
o0 <-try(suppressWarnings(step.ts.fit(y, XREG, Z, PSI, opz0, return.all.sol=FALSE)), silent=TRUE)
#browser()
if(!is.list(o0)) {
o0<- suppressWarnings(step.ts.fit(y, XREG, Z, PSI, opz, return.all.sol=TRUE))
o0<-extract.psi(o0)
ss00<-opz$dev0
if(!nonParam) {warning("using nonparametric boot");nonParam<-TRUE}
}
if(is.list(o0)){
est.psi00<-est.psi0<-o0$psi
ss00<-o0$SumSquares.no.gap
if(!nonParam) fitted.ok<-fitted(o0)
} else {
if(!nonParam) stop("the first fit failed and I cannot extract fitted values for the semipar boot")
if(random) {
est.psi00<-est.psi0<-apply(limZ,2,function(r)runif(1,r[1],r[2]))
PSI1 <- matrix(est.psi0, n, ncol = length(est.psi0), byrow=TRUE)
o0<-try(suppressWarnings(step.ts.fit(y, XREG, Z, PSI1, opz1)), silent=TRUE)
ss00<-o0$SumSquares.no.gap
} else {
est.psi00<-est.psi0<-apply(PSI,2,mean)
ss00<-opz$dev0
}
}
n.intDev0<-nchar(strsplit(as.character(ss00),"\\.")[[1]][1])
all.est.psi.boot<-all.selected.psi<-all.est.psi<-matrix(NA, nrow=n.boot, ncol=length(est.psi0))
all.ss<-all.selected.ss<-rep(NA, n.boot)
if(is.null(size.boot)) size.boot<-n
Z.orig<-Z
count.random<-0
agg.values<-seq(.2,.05,l=n.boot)
###INIZIO BOOT
alpha<-.1
corr=1.2
#browser()
n.boot.rev<- 3 #3 o 4?
for(k in seq(n.boot)){
#if(k==2) browser()
#browser()
diff.selected.ss <- rev(diff(na.omit(all.selected.ss)))
if(length(diff.selected.ss)>=(n.boot.rev-1) && all(round(diff.selected.ss[1:(n.boot.rev-1)],6)==0)){
qpsi <- sapply(1:ncol(Z),function(i)mean(est.psi0[i]>=Z[,i]))
qpsi.cor <- sapply(1:ncol(Z),function(i)mean((corr*est.psi0[i])>=Z[,i]))
qpsi <- ifelse(abs(qpsi-.5)<=.2, qpsi.cor, alpha)
alpha<-1-alpha
corr<-1/corr
est.psi0 <- sapply(1:ncol(Z),function(i)quantile(Z[,i], probs=qpsi[i],names=FALSE))
est.psi0 <- adj.psi(est.psi0, limZ)
#est.psi0<- jitter(est.psi0, amount=min(diff(est.psi0)))
}
########################### 25/7/24 #####
est.psi0 <- unlist(tapply(est.psi0, opz$id.psi.group, sort))
#########################################
PSI <- matrix(est.psi0, n, ncol = length(est.psi0), byrow=TRUE)
if(jt) Z<-apply(Z.orig,2,jitter)
if(nonParam){
id<-sample(n, size=size.boot, replace=TRUE)
o.boot<-try(suppressWarnings(step.ts.fit(y[id], XREG[id,,drop=FALSE], Z[id,,drop=FALSE], PSI[id,,drop=FALSE],
opz)), silent=TRUE)
} else {
yy<-fitted.ok+sample(residuals(o0),size=n, replace=TRUE)
o.boot<-try(suppressWarnings(step.ts.fit(yy, XREG, Z.orig, PSI, opz)), silent=TRUE)
}
if(is.list(o.boot)){
all.est.psi.boot[k,]<-est.psi.boot<-o.boot$psi
} else {
est.psi.boot<-apply(limZ,2,function(r)runif(1,r[1],r[2]))
est.psi.boot<- unlist(tapply(est.psi.boot, opz$id.psi.group, sort))
}
PSI <- matrix(est.psi.boot, n, ncol = length(est.psi.boot), byrow=TRUE)
#opz$h<-max(opz$h*.9, .2)
opz$it.max<-opz$it.max+1
opz$agg<-agg.values[k]
o <-try(suppressWarnings(step.ts.fit(y, XREG, Z.orig, PSI, opz, return.all.sol=TRUE)), silent=TRUE)
if(!is.list(o) && random){
est.psi0<-apply(limZ,2,function(r)runif(1,r[1],r[2]))
PSI1 <- matrix(rep(est.psi0, rep(nrow(Z), length(est.psi0))), ncol = length(est.psi0))
o <-try(suppressWarnings(step.ts.fit(y, XREG, Z, PSI1, opz1)), silent=TRUE)
count.random<-count.random+1
}
#se il modello e' stato stimato controlla se la soluzione e' migliore..
if(is.list(o)){
if(!"coefficients"%in%names(o$obj)) o<-extract.psi(o)
all.est.psi[k,]<-o$psi
all.ss[k]<-o$SumSquares.no.gap
if(o$SumSquares.no.gap<=ifelse(is.list(o0), o0$SumSquares.no.gap, 10^12)) o0<-o
est.psi0<-o0$psi
all.selected.psi[k,] <- est.psi0
all.selected.ss[k]<-o0$SumSquares.no.gap #min(c(o$SumSquares.no.gap, o0$SumSquares.no.gap))
}
if (visualBoot) {
flush.console()
# spp <- if (it < 10) " " else NULL
# cat(paste("iter = ", spp, it,
# " dev = ",sprintf('%8.5f',L1), #formatC(L1,width=8, digits=5,format="f"), #era format="fg"
#n.intDev0<-nchar(strsplit(as.character(dev.values[2]),"\\.")[[1]][1])
unlpsi<- unlist(est.psi0)
Lp<-length(unlpsi)
cat(paste("boot sample = ", sprintf("%2.0f",k),
" opt.dev = ", sprintf(paste("%", n.intDev0+6, ".5f",sep=""), o0$SumSquares.no.gap), #formatC(L1,width=8, digits=5,format="f"), #era format="fg"
" n.psi = ",formatC(Lp,digits=0,format="f"),
" est.psi = ",paste(formatC(unlpsi[1:min(Lp,5)],digits=3,format="f"), collapse=" "), #sprintf('%.2f',x)
sep=""), "\n")
}
#conta i valori ss uguali.. cosi puoi fermarti prima..
asss<-na.omit(all.selected.ss)
if(length(asss)>break.boot){
if(all(rev(round(diff(asss),6))[1:(break.boot-1)]==0)) break
}
} #end n.boot
all.selected.psi<-rbind(est.psi00,all.selected.psi)
all.selected.ss<-c(ss00, all.selected.ss)
#SS.ok<-min(all.selected.ss)
#id.accept<- ((abs(all.ss-SS.ok)/SS.ok )<= 0.05)
#psi.mean<-apply(all.est.psi[id.accept,,drop=FALSE], 2, mean)
#est.psi0<-psi.mean
# #devi ristimare il modello con psi.mean
# PSI1 <- matrix(rep(est.psi0, rep(nrow(Z), length(est.psi0))), ncol = length(est.psi0))
# o0<-try(seg.lm.fit(y, XREG, Z, PSI1, w, offs, opz1), silent=TRUE)
ris<-list(all.selected.psi=drop(all.selected.psi),all.selected.ss=all.selected.ss, all.psi=all.est.psi, all.ss=all.ss)
if(is.null(o0$obj)){
PSI1 <- matrix(est.psi0, n, ncol = length(est.psi0), byrow=TRUE)
o0 <- try(step.ts.fit(y, XREG, Z, PSI1, opz1), silent=TRUE)
warning("The final fit can be unreliable (possibly mispecified segmented relationship)", call.=FALSE, immediate.=TRUE)
}
if(!is.list(o0)) return(0)
o0$boot.restart<-ris
o0$seed<-seed
#rm(.Random.seed, envir=globalenv())
return(o0)
}
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