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#new predict.segmented
predict.segmented<-function(object, newdata, se.fit=FALSE, interval=c("none","confidence", "prediction"),
type = c("link", "response"), na.action=na.omit,# "terms"),
level=0.95, .coef=NULL, ...){
blockdiag <- function(...) {
args <- list(...)
nc <- sapply(args,ncol)
cumnc <- cumsum(nc)
## nr <- sapply(args,nrow)
## NR <- sum(nr)
NC <- sum(nc)
rowfun <- function(m,zbefore,zafter) {
cbind(matrix(0,ncol=zbefore,nrow=nrow(m)),m,
matrix(0,ncol=zafter,nrow=nrow(m)))
}
ret <- rowfun(args[[1]],0,NC-ncol(args[[1]]))
for (i in 2:length(args)) {
ret <- rbind(ret,rowfun(args[[i]],cumnc[i-1],NC-cumnc[i]))
}
ret
}
dummy.matrix<-function(x.values, x.name, obj.seg, psi.est=TRUE, isV=FALSE, .coef=NULL){
#given the segmented fit 'obj.seg' and a segmented variable x.name with corresponding values x.values,
#this function simply returns a matrix with columns (x, (x-psi)_+, -b*I(x>psi))
#if obj.seg does not include the coef for the linear "x", the returned matrix is ((x-psi)_+, -b*I(x>psi))
f.U<-function(nomiU, term=NULL){
#trasforma i nomi dei coeff U (o V) nei nomi delle variabili corrispondenti
#and if 'term' is provided (i.e. it differs from NULL) the index of nomiU matching term are returned
k<-length(nomiU)
nomiUsenzaU<-strsplit(nomiU, "\\.")
nomiU.ok<-vector(length=k)
for(i in 1:k){
nomi.i<-nomiUsenzaU[[i]][-1]
if(length(nomi.i)>1) nomi.i<-paste(nomi.i,collapse=".")
nomiU.ok[i]<-nomi.i
}
if(!is.null(term)) nomiU.ok<-(1:k)[nomiU.ok%in%term]
return(nomiU.ok)
}
estcoef <- if(is.null(.coef)) coef(obj.seg) else .coef
if(length(isV)==1) isV<-c(FALSE,isV)
n<-length(x.values)
#le seguenti righe selezionavano (ERRONEAMENTE) sia "U1.x" sia "U1.neg.x" (se "x" e "neg.x" erano segmented covariates)
#nameU<- grep(paste("\\.",x.name,"$", sep=""), obj.seg$nameUV$U, value = TRUE)
#nameV<- grep(paste("\\.",x.name,"$", sep=""), obj.seg$nameUV$V, value = TRUE)
nameU<-obj.seg$nameUV$U[f.U(obj.seg$nameUV$U,x.name)]
nameV<-obj.seg$nameUV$V[f.U(obj.seg$nameUV$V,x.name)] #grep(x.name, obj.seg$nameUV$V, value = TRUE)
if(is.null(obj.seg$constr)){
diffSlope<-estcoef[nameU]
} else {
diffSlope<-drop(obj.seg$constr$invA.RList[[match(x.name, obj.seg$nameUV$Z)]]%*%estcoef[nameU])[-1]
}
est.psi<-obj.seg$psi[nameV,"Est."]
se.psi<-obj.seg$psi[nameV, "St.Err"]
k<-length(est.psi)
PSI <- matrix(rep(est.psi, rep(n, k)), ncol = k)
SE.PSI <- matrix(rep(se.psi, rep(n, k)), ncol = k)
newZ<-matrix(x.values, nrow=n,ncol=k, byrow = FALSE)
dummy1<-if(isV[1]) (newZ-PSI)*pnorm((newZ-PSI)/SE.PSI) else (newZ-PSI)*(newZ>PSI) #pmax(newZ-PSI,0)
if(psi.est){
V<-if(isV[2]) -pnorm((newZ-PSI)/SE.PSI) else -(newZ>PSI) #ifelse(newZ>PSI,-1,0)
dummy2<- if(k==1) V*diffSlope else V%*%diag(diffSlope) #t(diffSlope*t(-I(newZ>PSI)))
newd<-cbind(x.values,dummy1,dummy2)
#colnames(newd)[1]<- x.name
colnames(newd)<-c(x.name,sub("psi","U", nameV), nameV)
} else {
newd<-cbind(x.values,dummy1)
#colnames(newd)[1]<- x.name
colnames(newd)<-c(x.name, sub("psi","U", nameV))
}
#if(!x.name%in%names(coef(obj.seg))) newd<-newd[,-1,drop=FALSE] #restituisce sempre il termine principale..
#aggiungi (eventualmente) le colonne relative ai psi noti
all.psi<-obj.seg$indexU[[x.name]]
if(length(all.psi)!=k){
nomi.psi.noti<-setdiff(names(all.psi),nameU)
psi.noti<-setdiff(all.psi, est.psi)
PSI.noti <- matrix(rep(psi.noti, rep(n, length(psi.noti))), ncol = length(psi.noti))
nomi<-c(colnames(newd),nomi.psi.noti)
newd<-cbind(newd, (newZ-PSI.noti)*(newZ>PSI.noti))
colnames(newd)<-nomi
}
return(newd)
}
estcoef <- if(is.null(.coef)) coef(object) else .coef
if(is.null(names(estcoef))) stop("the coef estimates should be named")
nLin<- length(setdiff(names(coef(object)), c(object$nameUV$U,object$nameUV$V)))
nSeg<- length(object$nameUV$Z)
type<-match.arg(type)
interval<-match.arg(interval)
#browser()
if(inherits(object, "glm") && object$family$family!="gaussian" && interval=="prediction")
stop("prediction intervals are not allowed with non-gaussian glm")
nameU<-object$nameUV$U
nameV<-object$nameUV$V
nameZ<-object$nameUV$Z
#browser()
if(missing(newdata)){
X <- model.matrix(object)
idNA<- rep(FALSE, nrow(X))
} else {
#browser()
#nomiLin <- setdiff(all.vars(formula(object))[-1], c(object$nameUV$U,object$nameUV$V))
nomiLin <- setdiff(all.vars(as.formula(paste("~",paste(formula(object))[3]))), c(object$nameUV$U,object$nameUV$V))
if(any(is.na(match(nomiLin, names(newdata))))) stop(" 'newdata' should includes all variables")
#devi trasformare la variabili segmented attraverso dummy.matrix()
na.arg <- deparse(substitute(na.action))
idNA<- !complete.cases(newdata)
if(any(idNA)){
newdata<-na.omit(newdata)
}
if(!na.arg%in%c("na.omit","na.pass")) stop("na.action should be 'na.omit' or 'na.pass'")
n<-nrow(newdata)
r<-NULL
if(length(object$call$obj)>0){ #se l'ogg e' stato ottenuto da segmented.*
# Fo<- formula(delete.response(terms(formula(eval(object$call$obj)))))
# idSeg<- object$nameUV$Z %in% all.vars(Fo)
# if(any(!idSeg)){
# Fo<- update.formula(Fo, as.formula(paste("~.+", paste(object$nameUV$Z[!idSeg], collapse="+"))))
# }
#nomiTerms, a differenza di nomiLin, include eventuali poly(w,2)
nomiTerms<-setdiff(attr(terms(formula(object)),"term.labels"),c(object$nameUV$U,object$nameUV$V))
idSeg<- object$nameUV$Z %in% nomiLin #potresti mettere anche "nomiTerms"
if(any(!idSeg)){
nomiTerms <- c(nomiTerms, object$nameUV$Z[!idSeg])
}
Fo<-as.formula(paste("~.+", paste(nomiTerms, collapse="+")))
M<-model.matrix(Fo, data=newdata,
contrasts=object$contrasts, xlev = object$xlevels)
} else { #se l'ogg e' stato ottenuto da segreg
#browser()
Fo<-as.formula(object$nameUV$formulaSegAllTerms)
if(any(all.vars(Fo)%in%names(object$xlevels))){
M<-model.matrix(Fo, data=newdata,
contrasts = object$contrasts, xlev=object$xlevels)
} else {
M<-model.matrix(Fo, data=newdata)
}
#nomiLin<- all.vars(object$formulaLin)[-1] #non funziona se la rispo e' cbind(y,n-y)
nomiLin <- all.vars(as.formula(paste("~",paste(object$formulaLin)[3])))
if(any(!nomiLin%in%all.vars(Fo))){
#nomiLinOK<- nomiLin[!nomiLin%in%all.vars(Fo)]
terminLin<-attr(terms(object$formulaLin),"term.labels")[!nomiLin%in%all.vars(Fo)]
Fo <- as.formula(paste("~.-1+",paste(terminLin,collapse="+")))
#Fo <- update.formula(Fo, as.formula(paste("~.+",paste(terminLin,collapse="+"))))
M1<-model.matrix(Fo, data=newdata,
contrasts = object$contrasts, xlev=object$xlevels)
M<-cbind(M, M1) #[,nomiLinOK,drop=FALSE])
}
}
for(i in 1:length(nameZ)){
x.values <- M[,nameZ[i]]
DM<-dummy.matrix(x.values, nameZ[i], object)
r[[i]]<-DM
}
#browser()
X <-data.matrix(matrix(unlist(r), nrow=n, byrow = FALSE))
colnames(X)<- unlist(sapply(r, colnames))
X<-cbind(M,X)
X<-X[,unique(colnames(X)),drop=FALSE]
if("(Intercept)" %in% names(estcoef)) X<-cbind("(Intercept)"=1,X)
}
if(!is.null(object$constr)){
for(i in 1:length(nameZ)){
nomeU.i<-grep(object$nameUV$Z[i], object$nameUV$U, value=TRUE)
idU.i <- match(nomeU.i, names(estcoef))
coef.new<-drop(object$constr$invA.RList[[i]]%*%estcoef[nomeU.i])
names(coef.new)<-c(object$nameUV$Z[i],
paste("U",1:(length(coef.new)-1),".",object$nameUV$Z[i],sep="" ))
estcoef<-append(estcoef[-idU.i], coef.new, after=idU.i[1]-1)
}
}
X<-X[,names(estcoef),drop=FALSE]
if(length(setdiff(colnames(X),names(estcoef)))>0) stop("error in the names (of the supplied newdata)")
#browser()
colnomi<- colnames(X)
colnomi.noV <- setdiff(colnomi, nameV)
X.noV <- X[, colnomi.noV, drop=FALSE]
estcoef.noV<- estcoef[colnomi.noV]
#ignora eventuali altre variabili contenute in newdata
#nomiOK<- intersect(names(estcoef.noV), colnames(X.noV))
#X.noV<- X.noV[, nomiOK, drop=FALSE]
#estcoef.noV<-estcoef.noV[nomiOK]
mu <- eta<- drop(X.noV%*% estcoef.noV)
if(!is.null(object$offset)) mu<- eta<- eta+ object$offset
X <- X[,c(colnomi.noV, nameV),drop=FALSE]
if(inherits(object, "glm") && type=="response") {
mu<-object$family$linkinv(mu)
}
#browser()
if(interval!="none" || se.fit){
V <- vcov(object)
if(!is.null(object$constr)){
B=if(nLin>0) append(list(diag(nLin)), object$constr$invA.RList, 1) else object$constr$invA.RList
B=append(B, list(diag(length(nameV))), 2)
B= do.call(blockdiag, B)
V <- B %*% V %*% t(B)
} else {
X <- X[,colnames(V)]
}
se <- sqrt(rowSums((X %*% V) * X))
if(inherits(object, "glm")) {
if(type=="response") se <- abs(object$family$mu.eta(eta))*se
z<-abs(qnorm((1-level)/2))
s2<-summary(object)$dispersion
} else {
z <- abs(qt((1-level)/2, df=object$df.residual))
s2<- summary(object)$sigma^2
}
if(any(idNA) && na.arg=="na.pass"){
mu0<-mu
se0<-se
mu<-se<- rep(NA, length(idNA))
mu[!idNA]<-mu0
se[!idNA]<-se0
}
if(interval=="confidence"){
mu<-cbind(fit=mu, lwr=mu-z*se, upr=mu+z*se)
}
if(interval=="prediction"){
mu<-cbind(fit=mu, lwr=mu-z*sqrt(se^2+s2), upr=mu+z*sqrt(se^2+s2))
}
} else {
if(any(idNA)&& na.arg=="na.pass"){
mu0<-mu
mu<- rep(NA, length(idNA))
mu[!idNA]<-mu0
}
}
if(se.fit) {
mu <- list(fit=mu, se.fit=se, df= object$df.residual, residual.scale=sqrt(s2))
if(!inherits(object, "glm")) mu$df<- object$df.residual
}
return(mu)
}
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