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selgmented <-function(olm, seg.Z, Kmax=2, type=c("score", "bic", "davies", "aic"), alpha=0.05,
control=seg.control(), refit=FALSE, stop.if=5, return.fit=TRUE, bonferroni=FALSE, #improve.after.G=FALSE,
msg=TRUE, plot.ic=FALSE, th=NULL, G=1, check.dslope=TRUE){ #}, a=1){
#vedere bene il fatto del th!!
#ruolo di alpha??? e se la ricerca si vuole restringere a un intervallo? perndere seg.control()$alpha?
#check.dslope: if TRUE, a check on slope difference parameters is done to spot the "non-significant" ones. Then
# the changepoints corresponding to such slopes are removed from the last fit.
#refit: if TRUE (and return.fit=TRUE), the last (selected) model is re-fitted using the 'control' controlling options (e.g. with n.boot>0)
#stop.if. If type="bic" or "aic", the search of number of break can be stopped when the last 'stop.if' fits provide higher aic/bic value
#th: When the distance between 2 estimated breakpoints is <=th, only one is retained. Default is th = drop(diff(rangeZ)/100)
#mettere l'opzione di gdl=n.changepoint e poi (df*log(n))^alpha dove alpha=1.01 (vedi...)
#sel1() si usa per G>1
#===
if(stop.if<=0) stop("'stop.if' should be an integer (at least 4, probably)")
stop.if<-ceiling(stop.if)
f<-function(x, soglia){
#restituisce l'indice del vettore x t.c. il valore e' il piu' piccolo
#tra quelli che sono minori della soglia
id <- (x<=soglia)
xx <- x[id]
ind <- (1:length(x))[id]
id.ok <- which.min(xx)
ind[id.ok]
}
#===
sel1 <- function(y, x, G, Kmax, type="bic",th=th,refit=FALSE,check.dslope=TRUE,
msg=TRUE, bonferroni=FALSE, olm0, control){ #, a=1
#BIC<-function(obj){
# n <-length(obj$residuals)
# r <- n*log(sum(obj$residuals^2)/n) + (n-obj$df.residual)*(log(n)^a) #- 1
# r
#}
ICname<- if(type=="bic") "BIC" else "AIC"
control1<-control
control1$n.boot = 0
control1$tol <- .001 #
BIC.f<-if(type=="bic") BIC else AIC
#---
drop.close <-function(all.psi, th){
if(length(all.psi)==1) return(all.psi)
all.psi <- c(m1, sort(all.psi[!is.na(all.psi)]), m2)
id<- which(diff(all.psi)<=th)[1]
while(!is.na(id) && length(id)>0){
all.psi <- all.psi[-(id+1)]
id<- which(diff(all.psi)<=th)[1]
}
#all.psi<- all.psi[-c(1, length(all.psi))]
all.psi <- setdiff(all.psi,c(m1, m2))
all.psi
}
#start..
n<-length(y)
K1<-ceiling(Kmax/G)
#browser()
#x<-1:n
#n1<-ceiling(n/G)
#cutvalues <- c(seq(1,n,by=n1),n+1)
cutvalues <- c(min(x), cumsum(rep(sum(range(x))/G, G)))
id<-cut(x, cutvalues, right=FALSE, labels=FALSE)
r<-vector("list",G)
#browser()
for(i in 1:G){
#if(i==4) browser()
yy<-y[id==i]
xx<-x[id==i]
olm<-lm(yy~xx) #, data=d)
.a <- capture.output(r[[i]] <-try(suppressWarnings(selgmented(olm, ~xx, type=type, Kmax=K1,
refit=FALSE, msg=FALSE, G=1, control=control, check.dslope=FALSE)
), silent=TRUE))
if(inherits(r[[i]],"try-error")) r[[i]]<- olm
n.psi<- if(is.null(r[[i]]$psi) || any(is.na(r[[i]]$psi[,"Est."]))) 0 else nrow(r[[i]]$psi)
n.psi<- if(n.psi<10) paste("", n.psi) else paste(n.psi)
if(msg) {
cat("\n##### subset ", paste(i, ": ...",sep=""))
cat(" ",n.psi,"selected breakpoints \n")
}
}
#browser()
all.psi <-unlist(sapply(r, function(.x) .x$psi[,"Est."]))
psi.fromG <- drop.close(all.psi,th)
psi.removed<- setdiff(all.psi, psi.fromG)
psi.removed<- psi.removed[!is.na(psi.removed)]
if(length(psi.removed)>=1 && msg){
cat(paste("\n", length(psi.removed), "breakpoint(s) removed for closeness (see argument 'th')\n"))
}
all.psi <- psi.fromG
olm <- olm0 #lm(y~x)
newpsi <- cutvalues[-c(1, length(cutvalues))]
if(msg){
cat(paste("\n => Assessing the", length(newpsi), "cutoff(s) as breakpoint(s). Computing the", ICname, "values.. \n"))
}
all.psi<-sort(c(all.psi, newpsi)) #tutti i psi
#elimina quelli vicini..
psi.withCut <- all.psi <-drop.close(all.psi,th)
#browser()
######## ORA stima modelli con un numero decrescente di psi...
tvalueU <- psi0 <- all.psi
list.psi <- list.fit<-NULL
#browser()
#se nella riduzione del numero di psi il modello non viene stimato, allora piuttosto che passargli i valori di psi
# da cui ha arbitrariamente eliminato il primo, passagli il numero di psi..
#fit.ok<-TRUE
idbreak<-FALSE
conv <- bicVa <- NULL
#browser()
if(msg) cat(" no. breakpoints: ", length(all.psi))
control2<-control1
control2$n.boot=6
while(length(tvalueU)>=1){
.a <- capture.output(os0 <- try(suppressWarnings(
segmented(olm, ~x, psi=all.psi, control=control2)), silent=TRUE))
if(!inherits(os0, "segmented")) {
.a <- capture.output(os0 <- try(suppressWarnings(
segmented(olm, ~x, npsi=length(all.psi), control=control1)), silent=TRUE))
}
if(inherits(os0, "segmented")) {
conv[length(conv)+1]<- 1
bicVa[length(bicVa)+1] <- BIC.f(os0)
list.fit[[length(list.fit)+1]] <- os0
list.psi[[length(list.psi)+1]]<- os0$psi[,"Est."]
if(length(os0$psi[,"Est."])==1) break
tvalueU<- abs(summary(os0)$coefficients[os0$nameUV$U,3])
idU <- which.min(abs(tvalueU))
#man mano che rimuovi i psi, se il t della diffSlope del psi che stai rimuovendo e' > soglia, fermati!
## allora non continuare a toglierli..
#===>>>NOOOOO!! puo' accadere poi che toglie qualche psi, si ferma perche' tutti i t sono grandi, ma il bic e' piu' basso
#in un modello precedente.. Quindi alla fine il criterio non e' ne bic e neanche tutti i t significativi.
#Allora, coerentemente con il caso di G=1, la "verifica delle slope nonsignif, va fatta sul modello
#selezionato con il BIC!! Quindi commentiamo le righe di sotto (il idbreak si potrebbe pure eliminare)
#e il controllo checkdslope lo facciamo dopo sul modello selezionato dal bic.
#browser()
#if(check.dslope){
# soglia <- if(!bonferroni) qnorm(1-alpha/2) else qnorm(1-alpha/(2* length(os0$nameUV$U)))
# if(abs(tvalueU[idU])>soglia) {idbreak=TRUE; break}
#}
all.psi <- os0$psi[-idU,"Est."]
} else {
conv[length(conv)+1]<- 0
list.psi[[length(list.psi)+1]]<-list.fit[[length(list.fit)+1]] <- NA
tvalueU<-all.psi<-all.psi[-1]
bicVa[length(bicVa)+1] <- if(is.numeric(bicVa[length(bicVa)])) {bicVa[length(bicVa)]+1} else {1e4}
}
if(msg) cat(" ..", length(all.psi))
#browser()
if(length(bicVa)>stop.if && all( na.omit(rev(diff(bicVa))[1:stop.if])>0) && (sum(na.omit(rev(conv)[1:stop.if]))>0)) {idbreak<-TRUE; break}
#na.omit() sta per eliminare gli NA che si creano se diff(bicVa) ha dimensione < stop.if
#la sum(na.omit(rev(conv)[1:stop.if]))>0 indica che il controllo sui valori del bic va fatto solo
#se gli ultimi fit non sono tutti insuccessi.
}
if(msg) cat(" .. 0\n")
bicV <- sapply(list.fit, function(.x) if(inherits(.x, "segmented")) BIC.f(.x) else NA)
if(length(bicV)!=length(bicVa)) stop("Errore inatteso 1")
bicV<- bicVa
#browser()
if(idbreak){
if(msg) cat(paste(" =>", length(psi.withCut)-length(bicVa), "unevaluated model(s) due to", stop.if, "increasing A/BIC value(s)..\n"))
#se si ferma prima, significa che sono stati valutati length(bicV) modelli con numero di psi da
#"length(psi.withCut)" fino a "length(psi.withCut)-length(bicV)+1"
#length(psi.withCut)-length(bicV)+1
r <- r0 <- list.fit[[which.min(bicV)]]
npsi.ok <- (length(psi.withCut):(length(psi.withCut)-length(bicV)+1))[which.min(bicV)]
nameBIC<-paste((length(psi.withCut):(length(psi.withCut)-length(bicV)+1)))
} else {
if(length(bicV)!= length(psi.withCut)) stop("Errore nella dim!!")
list.fit[[length(list.fit)+1]] <- olm
bicV<- c(bicV, BIC.f(olm))
r <- r0 <- list.fit[[which.min(bicV)]]
npsi.ok <- ((length(psi.withCut)):0)[which.min(bicV)]
if(npsi.ok!=length(r$psi[,"Est."])) stop("Unexpected error..")
nameBIC<-paste((length(psi.withCut)):0)
}
names(bicV)<-nameBIC
#DUBBIO: il controllo ed eliminazione dei psi lo facciamo all'interno del "while(length(tvalueU)>1)"?
# ed inoltre al modello selezionato un ultimo fit con boot bisognerebbe darlo...
#browser()
if(inherits(r, "segmented")){
#ALTRO CONTROLLO SULLA VICINANZA DEI psi..
all.psi <- sort(r$psi[,"Est."])
#all.psi <- drop.close(all.psi,th)
#r$psi<-all.psi
#browser()
cont1 <- length(all.psi)>0 && (length(all.psi)!=length(drop.close(all.psi,th)))
while(cont1){
start.psi<-drop.close(all.psi,th)
r0$call$psi<- start.psi
.a <- capture.output(r <- suppressWarnings(try(update(r0))), type="message")
all.psi<- if(inherits(r, "segmented")) r$psi[,"Est."] else start.psi[-1]
cont1<- !(inherits(r, "segmented") && (length(all.psi)==length(drop.close(all.psi,th))))
cont1 <- cont1 && length(all.psi)>0
}
if(inherits(r, "segmented")){
# all.psi<- r$psi[,"Est."]
# soglia <- if(!bonferroni) qnorm(1-alpha/2) else qnorm(1-alpha/(2* length(r$nameUV$U)) )
# rm.id <- which(abs(summary(r)$coefficients[r$nameUV$U, 3]) <= soglia)
# #===============
# if(check.dslope && length(rm.id)>0){
# # se devi controllare le slopeDiff
# # #ALTRO CONTROLLO SULLA SIGNIF DELLE SLOPE-DIFF
# #Non puoi eliminarli tutti assieme, perche' una volta che il piu' piccolo e stato eliminato
#gli altri possono cambiare..
#browser()
rm.after.check <- 0
all.psi<- r$psi[,"Est."]
#browser()
if(check.dslope){
soglia <- if(!bonferroni) qnorm(1-alpha/2) else qnorm(1-alpha/(2* length(r$nameUV$U)))
tU <- abs(summary(r)$coefficients[r$nameUV$U, 3])
rm.id <- f(tU, soglia)
while(length(rm.id)>0){
rm.after.check <- rm.after.check+1
all.psi <- all.psi[-rm.id]
if(length(all.psi)<=0) break
r0$call$psi=quote(all.psi)
.a <- capture.output(r <- suppressWarnings(try(update(r0), silent=TRUE))) #senza boot
if(!inherits(r,"segmented")){ #facciamo un altro tentativo..
#r0$call$psi=all.psi
#control$alpha <- .005
r0$call$control<-control #con boot
.a <- capture.output(r<-suppressWarnings(try(r<-update(r0), silent=TRUE)))
}
if(inherits(r,"segmented")){
tU <- abs(summary(r)$coefficients[r$nameUV$U, 3])
#rm.id <- which.min(tU[tU<=soglia])
rm.id <- f(tU, soglia)
all.psi<-r$psi[,"Est."]
} else {
rm.id<-1
}
}
if(length(all.psi)<=0 || is.null(r$psi) ) {
if(msg) warning("All found psi had a non-significant slope diff and have been removed", call.=TRUE, immediate.=TRUE)
n.psi.ok<-0
r<- olm
} else {
if(msg) cat(paste(" => ", rm.after.check, " breakpoint(s) removed due to 'small' slope difference\n", sep=""))
if(refit){
all.psi<- r$psi[,"Est."]
r$call$psi=quote(all.psi)
r$call$control<- quote(control)
.a <- capture.output(r <- try(suppressWarnings(update(r0))))
n.psi.ok <- if(is.null(r$psi)) 0 else nrow(r$psi)
} else {
all.psi<- r$psi[,"Est."]
n.psi.ok <- nrow(r$psi)
}
}
} else {#end if(check.dslope)
if(refit){
all.psi<- r$psi[,"Est."]
r$call$psi=quote(all.psi)
r$call$control<- quote(control)
.a <- capture.output(r <- try(suppressWarnings(update(r))))
n.psi.ok <- if(is.null(r$psi)) 0 else nrow(r$psi)
} else {
all.psi<- r$psi[,"Est."]
n.psi.ok <- nrow(r$psi)
}
}
} else {
#stop("Errore inatteso 1")
r<-olm
n.psi.ok<-0
}
} else {
#stop("Errore inatteso 2")
r<-olm
n.psi.ok<-0
}
if(msg) cat("\n####### Overall: ... ", n.psi.ok, "selected breakpoint(s) \n\n")
#cat(" ##### Overall ", nrow(r$psi),"selected breakpoints \n")
#if(!is.list(r)) r<- olm
bic.values=rbind(as.numeric(names(bicV)), bicV)
rownames(bic.values)<-c("no. breakpoints", ICname)
r$selection.psi <-list(bic.values=bic.values, npsi=n.psi.ok, cutvalues=cutvalues) ##cutvalues #including the extremes
if(refit) r$selection.psi$psi.before.refit <-all.psi
if(plot.ic) plot(t(bic.values), type="o"); points(n.psi.ok, min(bic.values[2,]), pch=19, cex=1.1)
r
#r<-segmented(olm, ~x, psi=r$psi[,"Est."], control=seg.control(n.boot=10,alpha=.01)) #fix.npsi=FALSE
#r
} #fine sel1()
#=====================================================================
# BIC<-function(obj, a=1){
# #Se a=1 questo e' il BIC classico (a meno di una costante)
# n <-length(obj$residuals)
# r <- n*log(sum(obj$residuals^2)/n) + (n-obj$df.residual)*(log(n)^a) #- 1
# r
# }
#=====================================================================
type<-match.arg(type)
if(!type%in%c("bic","aic") && Kmax!=2) stop("Kmax>2 is not (yet?) allowed with hypothesis testing procedures. Set type='bic' or 'aic'", call.=FALSE)
if(!type%in%c("bic","aic") && G>1) stop("G>1 is allowed only with type='bic' or 'aic' ", call.=FALSE)
#=====================================================================
if(is.numeric(olm)){
y<-olm
if(missing(seg.Z)){
Z<-x<- 1:length(y)
} else {
nomeX<-all.vars(seg.Z)
if(length(nomeX)==1) {
Z<- eval(parse(text=nomeX))
} else {
stop("a single segmented variable should be specified in 'seg.Z' ")
}
}
olm <- lm(y~x)
} else {
if(!inherits(olm,"lm")) stop("'olm' does not appear a (g)lm fit")
y<- model.response(model.frame(olm))
if(is.matrix(y)){
nomeY<-colnames(y)
} else {
nomeY<- all.vars(formula(olm))[1]
}
#browser()
if(missing(seg.Z)){
nomeX <- setdiff(all.vars(formula(olm)), nomeY)
if(length(nomeX)==0 || length(nomeX)>1 || any(is.na(nomeX))) stop("I cannot determine the segmented variable")
seg.Z<- as.formula(paste("~", nomeX ))
Z <- olm$model[[nomeX]]
if(!is.numeric(Z)) stop("The variable in the starting model does not appear to be numeric")
} else {
if(length(all.vars(seg.Z))>1) stop("Multiple variables are not allowed in seg.Z")
nomeX<-all.vars(seg.Z)
Z <- if(nomeX%in%all.vars(formula(olm))) olm$model[[nomeX]] else eval(parse(text=nomeX))
}
}
m1 <-min(Z)
m2 <-max(Z)
if(is.null(th)) th <- diff(range(Z))/100
if(G==1){
build.mf<-function(o, data=NULL){
#returns the dataframe including the possibly untransformed variables,
#including weight and offset
fo<-formula(o)
if(!is.null(o$weights))
fo<-update.formula(fo,paste("~.+",all.vars(o$call$weights), sep=""))
if(!is.null(o$call$offset))
fo<-update.formula(fo,paste("~.+",all.vars(o$call$offset), sep=""))
if(!is.null(o$call$subset))
fo<-update.formula(fo,paste("~.+",all.vars(o$call$subset), sep=""))
#o$call$formula<-fo
if(is.null(o$call$data)) {
R<-get_all_vars(fo)
} else {
R<-get_all_vars(fo, data=eval(o$call$data))
}
R
}
#browser()
if(type%in%c("bic","aic")){
control1<-control
control1$n.boot = 0
control1$tol <- .001 #default e' .00001
control1$alpha<-.01 #se lo aumenti puo' non funzionare bene se ci sono molti psi da selezionare..
ICname<- if(type=="bic") "BIC" else "AIC"
BIC.f<-if(type=="bic") BIC else AIC
bicM0 <- BIC.f(olm)
Kmax <- min(floor((olm$df.residual-1)/2), Kmax)
npsi<-1:Kmax
startpsi<-vector("list", length(npsi))
conv<-bic.values<- rep(NA, length(npsi))
if(!is.null(olm$call$data)) assign(paste(olm$call$data), eval(olm$call$data, envir=parent.frame() ))
npsiVeri<-0
#fit with 1 breakpoint
.a<-capture.output(os<- suppressWarnings(try(segmented(olm, seg.Z, npsi=1, control=control1), silent=TRUE)))
ris<- NULL
ris[[1]] <- os #<- suppressWarnings(try(segmented(olm, seg.Z, npsi=1, control=control1), silent=TRUE))
#if fails try boot restating
if(inherits(os, "try-error")) {
.a <- capture.output(os<- suppressWarnings(try(segmented(olm, seg.Z, npsi=1, control=control), silent=TRUE)))
ris[[1]]<- os
}
if(inherits(os, "segmented")){
#if(is.null(th)) th <- drop(diff(os$rangeZ)/100)
bic.values[1]<- BIC.f(os)
Z<- os$model[,os$nameUV$Z]
estpsi <- os$psi[,"Est."]
M<-matrix(c(m1 ,rep(estpsi, each=2), m2), ncol=2, nrow=length(estpsi)+1, byrow=TRUE)
psi0 <- sum(M[which.max(apply(M,1,diff)),])/2
startpsi[[1]] <- sort(c(estpsi, psi0))
conv[1] <- 1
npsiVeri[length(npsiVeri)+1]<- length(estpsi)
} else {
estpsi <- (m1+m2)/2
M<-matrix(c(m1 ,rep(estpsi, each=2), m2), ncol=2, nrow=length(estpsi)+1, byrow=TRUE)
#psi0 <- sum(M[which.max(apply(M,1,diff)),])/2
startpsi[[1]] <- estpsi
bic.values[1]<- BIC.f(olm)+1
conv[1] <- 0
npsiVeri[length(npsiVeri)+1]<- 1
}
i=1 #ponilo =1
if(Kmax>=2){
if(msg) {
flush.console()
cat(paste("No. of breakpoints: "))
}
earlyStop<- FALSE
#==========================================================================================
#= inizio for
#browser()
for(i in 2:Kmax){
#source("C:/dati/lavori/segmented/segIntermedio/segmented/R/selgmented.R")
#if(i==3) browser()
.a <- capture.output(os<-suppressWarnings(try(segmented(olm, seg.Z, psi=startpsi[[i-1]], control=control1), silent=TRUE)))
if(msg) {
flush.console()
cat(paste(i,".. "))
}
if(inherits(os, "segmented")) {
conv[i]<-1
estpsi <- os$psi[,"Est."]
bic.values[i]<- BIC.f(os) #-2*logLik(ris[[i]]))+ edf*log(n)*Cn
ris[[length(ris)+1]]<- os
npsiVeri[length(npsiVeri)+1]<-length(estpsi)
id<- which(diff(estpsi)<=th)+1
if(length(id)>0){ #se ci sono psi troppo vicini..
#elimina quelli "vicini" aggiungine altri in modo da stimare un altro modello con lo stesso numero di psi
#Quindi alla fine dovresti avere 2 o piu' bic per uno stesso numero di breakpoints
estpsi <- estpsi[-id]
M<-matrix(c(m1 ,rep(estpsi, each=2), m2), ncol=2, nrow=length(estpsi)+1, byrow=TRUE)
diffpsi <- apply(M,1,diff)
psi0<-NULL
for(j in 1:(length(id))) {
psi0[length(psi0)+1] <- sum(M[which(diffpsi==rev(sort(diffpsi))[j]),])/2
}
startpsi[[i]] <- sort(c(estpsi, psi0))
} else {
#aggiungi uno starting psi
M<- matrix(c(m1 ,rep(estpsi, each=2), m2), ncol=2, nrow=length(estpsi)+1, byrow=TRUE)
diffpsi <- apply(M,1,diff)
id.max.diffpsi <- which.max(diffpsi)
psi0 <- sum(M[id.max.diffpsi,])/2
startpsi[[i]] <- sort(c(estpsi, psi0))
}
} else {
#Vuoi provare a ri-stimarlo con il boot rest?
#se non e' arrivato a convergenza:
# 1) prima prova a cambiare gli starting psi
# 2) prova con il boot restart
M<-matrix(c(m1 ,rep(startpsi[[i-1]], each=2), m2),
ncol=2, nrow=length(startpsi[[i-1]])+1, byrow=TRUE)
diffpsi <- apply(M,1,diff)
psi0 <- sum(M[which.max(diffpsi),])/2
start.ora<- sort(c(psi0, M[-c(which.min(diffpsi), nrow(M)),2]))
startpsi[[i-1]] <- start.ora
.a <- capture.output(os<-suppressWarnings(try(segmented(olm, seg.Z, psi=start.ora, control=control1), silent=TRUE)))
if(!inherits(os, "segmented")) { #vai con il boot restrat
.a <- capture.output(os<-suppressWarnings(try(segmented(olm, seg.Z, psi=start.ora, control=control), silent=TRUE)))
}
if(inherits(os, "segmented")) {
conv[i]<-1
estpsi <- os$psi[,"Est."]
bic.values[i]<- BIC.f(os) #-2*logLik(ris[[i]]))+ edf*log(n)*Cn
ris[[length(ris)+1]]<- os
npsiVeri[length(npsiVeri)+1]<-length(estpsi)
#aggiungi uno starting psi
M<-matrix(c(m1 ,rep(estpsi, each=2), m2), ncol=2, nrow=length(estpsi)+1, byrow=TRUE)
diffpsi <- apply(M,1,diff)
id.max.diffpsi <- which.max(diffpsi)
psi0 <- sum(M[id.max.diffpsi,])/2
startpsi[[i]] <- sort(c(estpsi, psi0))
} else {
#se dopo 3 tentativi non e' arrivato a conv comunque aggiungi uno starting psi,
# il bic e' bic.precedente+1 e ris[[i]] sara' NA
#control1$alpha<-.005
ris[[length(ris)+1]]<- NA
conv[i]<-0
bic.values[i]<- bic.values[i-1]+1
M<-matrix(c(m1 ,rep(startpsi[[i-1]], each=2), m2),
ncol=2, nrow=length(startpsi[[i-1]])+1, byrow=TRUE)
diffpsi <- apply(M,1,diff)
psi0 <- sum(M[which.max(diffpsi),])/2
#aggiunge un psi ai precedenti
startpsi[[i]] <- sort(c(startpsi[[i-1]], psi0))
npsiVeri[length(npsiVeri)+1]<- length(startpsi[[i-1]])
}
}
#if(i==8) browser()
#un controllo su bic values.. fermarsi SE gli ultimi K sono NA oppure sono crescenti!!
#bic.values<-bic.values[!is.na(bic.values)]
#ind1 e' se gli ultimi modelli non sono arrivati a convergenza per cui il bic e' stato aumentato di 1 rispetto al precedente..
#poiche' ci possono essere piu' bic per uno stesso numero di break, ne devi considerare solo uno (il minimo)
#altrimenti -se i bic per uno stesso numero di break sono decrescenti- la valutazione del bic crescente e' sballata
#if(i==5) browser()
bicValuesTest<-tapply(na.omit(c(bicM0,bic.values)), npsiVeri, min)
ind1<-(i>=stop.if && all(diff(rev(na.omit(bicValuesTest)))[1:3]==-1))
ind2<-(i>=stop.if&& length(bicValuesTest)>stop.if && all(rev(na.omit(diff(bicValuesTest)))[1:stop.if]>0))
if(ind1 || ind2) {earlyStop<-TRUE;break}
} #end in for(2 in Kmax)
#browser()
#if(msg) cat(paste(" =>", length(psi.withCut)-length(bicVa), "unevaluated model(s) due to", stop.if, "increasing A/BIC value(s)..\n"))
if(msg) cat("\n")
} else { #se Kmax=1
earlyStop<-FALSE
}
#npsiVeri <-sapply(ris, function(.x) if(is.list(.x))nrow(.x$psi) else NA ) #[1:i]
#npsiVeri <- npsiVeri[!is.na(npsiVeri)]
#if(length(npsiVeri)!=max(npsiVeri)) {
# id.psi.repl<-which(diff(npsiVeri)==0)
# sapply(id.psi.repl, function(.x) which(npsiVeri==.x))
#}
bic.valuesOrig <- bic.values
#bic.values<- bic.values#[1:i] #bic.values[!is.na(bic.values)]
bic.values <- c(bicM0, bic.values)
bic.values <- bic.values[1:length(npsiVeri)]
names(bic.values)<-npsiVeri
n.psi.ok<- npsiVeri[which.min(bic.values)]
#browser()
if(n.psi.ok==0){
m<-matrix(NA,1,1, dimnames=list(NULL, "Est."))
olm$selection.psi<- list(bic.values=bic.values, npsi=n.psi.ok)
olm$psi<-m
if(msg){
if(earlyStop) cat(paste("(search truncated at ", i, " breakpoints due to increasing values of ", ICname ,") \n", sep=""))
cat(paste("\n",ICname, " to detect no. of breakpoints:\n",sep=""))
print(bic.values)
cat(paste("\nNo. of selected breakpoints: ", n.psi.ok, " \n"))
}
bic.values=rbind(npsiVeri, bic.values)
rownames(bic.values)<-c("npsi", paste(ICname, "value",sep=""))
selection.psi <- list(bic.values=bic.values, npsi=n.psi.ok)
if(return.fit) {
#browser()
olm$selection.psi <- selection.psi
return(olm)
} else {
return(list(selection.psi=selection.psi))
#return(list(bic.values=bic.values, npsi=n.psi.ok))
}
}
#browser()
if(Kmax==n.psi.ok && msg) warning(paste("The best",ICname, "value at the boundary. Increase 'Kmax'?"), call.=FALSE, immediate. = TRUE)
id.best<- which.min(bic.values[-1])
#browser()
r<- r0 <- ris[[id.best]]
#browser()
rm.after.check <- 0
all.psi<- r$psi[,"Est."]
if(check.dslope){
soglia <- if(!bonferroni) qnorm(1-alpha/2) else qnorm(1-alpha/(2* length(r$nameUV$U)))
tU <- abs(summary(r)$coefficients[r$nameUV$U, 3])
#if(length(tU[tU<=soglia])==length(tU)) #anche se tutti i t<= soglia fai comunque la procedura, perche'
#riducendo i psi, i tU potrebbero cambiare
#rm.id <- which.min(tU[tU<=soglia])
rm.id <- f(tU, soglia)
while(length(rm.id)>0){
rm.after.check <- rm.after.check+1
all.psi <- all.psi[-rm.id]
if(length(all.psi)<=0) break
r0$call$psi=quote(all.psi)
.a <- capture.output(r <- suppressWarnings(try(update(r0), silent=TRUE))) #senza boot
if(!inherits(r,"segmented")){ #facciamo un altro tentativo..
#r0$call$psi=all.psi
#control$alpha <- .005
r0$call$control<-control #con boot
.a <- capture.output(r<-suppressWarnings(try(r<-update(r0), silent=TRUE)))
}
if(inherits(r,"segmented")){
tU <- abs(summary(r)$coefficients[r$nameUV$U, 3])
#rm.id <- which.min(tU[tU<=soglia])
rm.id <- f(tU, soglia)
all.psi<-r$psi[,"Est."]
} else {
rm.id<-1
}
}
#browser()
if(length(all.psi)<=0 || is.null(r$psi) ) {
n.psi.ok<-0
r<- olm
} else {
n.psi.ok <- nrow(r$psi)
}
} else { #end if(check.dslope)
#ATTENZIONE: SE NON HA SELEZIONATO BREAKPOINTS???
n.psi.ok<-length(all.psi) #in realta' gia' c'e' "n.psi.ok"
}
if(refit && length(all.psi)>0){
r$call$psi<- all.psi
#control$alpha <- .005
r$call$control<-quote(control) #con boot
r <- update(r)
}
if(plot.ic) {
if(rm.after.check!=0){
warning(paste("Some psi have been removed due to check on the slope difference; the", ICname, "plot could be misleading"), call.=FALSE)
}
plot(npsiVeri, bic.values, xlab=" No. of breakpoints", ylab=ICname, type="o")
points(n.psi.ok, min(bic.values), pch=19, cex=1.1)
}
#browser()
if(msg){
if(earlyStop) cat(paste("(search truncated at ", i, " breakpoints due to ", stop.if, " increasing values of ", ICname ,") \n", sep=""))
cat(paste("\n",ICname, " to detect no. of breakpoints:\n",sep=""))
print(bic.values)
add.msg <- if(rm.after.check==0) " \n" else paste(" (", rm.after.check, " breakpoint(s) removed due to small slope diff)\n", sep="")
cat(paste("\nNo. of selected breakpoints:", n.psi.ok, add.msg))
}
if(!return.fit) {
bic.values=rbind(npsiVeri, bic.values)
rownames(bic.values)<-c("npsi", paste(ICname, "value",sep=""))
r<-list(selection.psi=list(bic.values=bic.values, npsi=n.psi.ok))
return(r)
}
bic.values=rbind(npsiVeri, bic.values)
rownames(bic.values)<-c("npsi", paste(ICname, "value",sep=""))
r$selection.psi <- list(bic.values=bic.values, npsi=n.psi.ok)
if(refit) r$selection.psi$psi.before.refit <-all.psi
return(r)
} else {
#end aic/bic. Quindi se score o davies
alpha.adj<-alpha/Kmax
p1<- if(type=="score") pscore.test(olm, seg.Z, n.break=2)$p.value else davies.test(olm)$p.value
p1.label<-"p-value '0 vs 2' "
if(p1>alpha.adj){
p2.label<-"p-value '0 vs 1' "
p2<- if(type=="score") pscore.test(olm, seg.Z, n.break=1)$p.value else p1 #davies.test(olm)$p.value
if(!bonferroni) alpha.adj<- alpha
if(p2>alpha.adj) {
out<-olm
} else {
out<-segmented(olm, seg.Z, npsi=1, control=control)
}
} else {
p2.label<-"p-value '1 vs 2' "
#################
#browser()
#MF<-build.mf(olm)
#olm<-update(olm, data=MF)
#olm$call$data<-quote(MF)
#olm<-update(olm, data=model.frame(olm)) #questo e' necessario per far funzionare davies.test() sotto..
################
if(type=="score") {
o1<-segmented(olm, seg.Z, npsi=1, control=control)
p2<-pscore.test(o1, seg.Z, more.break=TRUE)$p.value
} else {
#KK<-new.env()
#olm1<-update(olm, data=model.frame(o1))
#o1<- update(o1, obj=olm1)
MF<-build.mf(olm)
olm<-update(olm, data=MF)
#olm$call$data<-quote(MF)
#olm<-update(olm, data=model.frame(olm)) #questo e' necessario per far funzionare davies.test() sotto..
o1 <- segmented(olm, seg.Z, npsi = 1, control = control)
p2<- davies.test(o1, seg.Z)$p.value
}
if(!bonferroni) alpha.adj<-alpha
if(p2>alpha.adj) {
o1<-segmented(olm, seg.Z, npsi=1, control=control)
#cat("One breakpoint detected\n")
out<-o1
} else {
o2<-segmented(olm, seg.Z, npsi=2, control=control)
#cat("Two breakpoint detected\n")
out<-o2
}
}
n.psi.ok<-length(out$psi[,"Est."])
x2<- -2*sum(log(c(p1,p2)))
p<-1-pchisq(x2, df=2*2)
r<-list(pvalues=c(p1=p1, p2=p2, p=p), npsi=n.psi.ok)
attr(r, "label")<- p2.label
if(!return.fit) {
return(r)
}
if(msg){
cat("Hypothesis testing to detect no. of breakpoints\n")
type <- chartr(strsplit(type,"")[[1]][1], toupper(strsplit(type,"")[[1]][1]), type) #serve per render maiuscola la prima lettera..
cat(paste("statistic:", type," level:", alpha, " Bonferroni correction:", bonferroni, "\n"))
cat(paste(p1.label, "= ", format.pval(p1,4), " ", p2.label, "= ", format.pval(p2,4) ,
" \nOverall p-value = ", format.pval(p,4),"\n",sep=""))
cat(paste("No. of selected breakpoints: ", n.psi.ok, "\n"))
}
out$selection.psi<-r
return(out)
}#end if(score o davies)
} else { #se G>1
r <- sel1(y=y, x=Z, G=G, Kmax=Kmax, type=type, th=th, refit=refit, check.dslope = check.dslope,
msg=msg, bonferroni=bonferroni, olm=olm, control=control)
r
}
}
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