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RHtests.R
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RHtests.R
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#**** Copyright agreement ****
# All users of any code in this package agree to the terms and conditions described in the
#file Copyright_RHtests__RClimDex_SoftwarePackages.pdf, which is also included in this package.
#*****************************
# last updated at 2019-03-01
# in QMadjGaussian.wRef(), change osmean estimation from the way QMadjGaussian to just take the
# mean value of adjusted segments, since there has extra adjustments for the difference b/w
# QMadj and mean-adj
# last updated at 2018-03-01
# in QMadjGaussian.wRef(), added check sample size during EBa calculation, and reduce Mq once
# sample size too small detected (the same consequence as EBb estimation)
# last updated at 2016-12-28
# set all QMadj output as missing once Mq==1
# last updated at 2016-10-05
# changed FindU.wRef(), FindUD.wRef(), StepSize.wRef(), switched LSmultipleRed to LSmultiRedCycle
# for trend estimation on original and mean-adjusted series
# last updated at 2014-02-03
# 1) Fixed Read.wRef(), changed merge() parameter sort=F to sort=T for itable.nmb, otherwise,
# non-match date for Bseries will be at the end of itable.nmb
# 2) Changed all pdf(), added paper='letter'
# 3) Changed OnQMadjGaussian.wRef(), make output filename correctly involved reference file name.
# last updated on 2013-06-28
# Changed all as.real() to as.numeric(), since R 2.15 no longer support as.real()
# last updated on 2013-04-03
# Changed Mq upper limit in StartGUI(), QMadjGaussian(), QMadjGaussian.wRef(), from 20 to 100,
# changed button locations on main manu.
# last updated on 2013-04-03
# Changed plots for QMadjust distribution
# last updated on 2013-03-19
# Added function QMadjGaussian.wRef(), changed FindU.wRef(), FindUD.wRef(),
# StepSize.wRef(), replaced QMadjGaussian() with QMadjGaussian.wRef().
# Modified Read.wRef(), append ref series to bdata matrix.
# Added a button 'QMadj.wRef' at StartGUI(), corresponding subroutine: QMadj.GaussianDLY.wRef
# last updated on 2012-09-25
# fixed a bug at StepSize() and StepSize.wRef() regarding to fitted value for Feb. 29th
# last updated on 2012-04-26
# at FindU() FindUD() StepSize() FindU.wRef() FindUD.wRef() and
# StepSize.wRef(), add fitted value for Feb. 29th to fitted
# value data files.
# last updated on 2011-12-11
# Change output format for *Ustat.txt in *.wRef(), from "PMF" -> "PMT"
# Change QMadj.GuassianDLY() and ReadDLY.g(), add vector olflg which indicate
# non-missing data only, but contains Feb 29th data.
# last updated on 2010-06-08
# In FindU(), FindUD(), StepSize() and 3 corresponding .wRef() functions,
# added a text indication "Yes ", "YifD", "No " or "? " in changepoint list
# output file, also changed corresponding read-in part.
# last updated on 2010-10-29
# add a sample format for 1Cs when there has no changepoint
# last updated on 2010-05-06
# in QMadjGaussian(), add Nadj option, set empirical prob. segment as length
# Nadj instead of whole segment.
#
# Bug fixed at 2010-02-08
# In FindU.wRef(), FindUD.wRef(), if 0 changepoint found, set meanhatD with
# fitted value, but not 0. Output *_U.dat or *_UD.dat affected.
# NT=1, 12, and 365 for annual, monthly, and daily series, respectively
# p.lev is the nominal level of confidence; (1 - p.lev) is the nominal level
# of significance
# choose one of the following 6 p.lev values: 0.75, 0.80, 0.90, 0.95, 0.99,
# 0.9999
# Mq (=0, 1, 2, ..., 20) is the number of points (categories) on which
# the empirical cumulative distribution function are estimated.
# If Mq=0, the actual Mq is determined by the length of the shortest segment
# Mq=1 corresponds to mean adjustments (one adjustment for all data in
# the same segment)
# In the output: Ns >= 0 is the number of changepoints identified;
# changepoint positions are Ip(1), Ip(2),...,Ip(Ns)
# If Iadj = 0, the data series is adjusted to the longest segment;
# otherwise the data series is adjusted to the chosen segment Iadj (if the
# given integer Iadj > Ns+1, we re-set Iadj=Ns+1, which corresponds to
# adjusting the series to the last segment). Set Iadj = 10000 if you want
# to ensure that the series is adjusted to the last segment
FindU<-function(InSeries,output,MissingValueCode,GUI=FALSE,p.lev=0.95,
Iadj=10000,Mq=10,Ny4a=0){
ErrorMSG<-NA
assign("ErrorMSG",ErrorMSG,envir=.GlobalEnv)
Nmin<-10
if(Ny4a>0&Ny4a<=5) Ny4a<-5
if(!p.lev%in%c(0.75,0.8,0.9,0.95,0.99,0.9999)){
ErrorMSG<<-paste("FindU: input p.lev",p.lev,"error\n",
get("ErrorMSG",env=.GlobalEnv),"\n")
if(!GUI) cat(ErrorMSG)
return(-1)
}
plev<-p.lev
pkth<-match(p.lev,c(0.75,0.8,0.9,0.95,0.99,0.9999))
assign("Nmin",Nmin,envir=.GlobalEnv)
itmp<-Read(InSeries,MissingValueCode)
if(itmp==(-1)){
ErrorMSG<<-paste("FindU: Error in read data from",InSeries,"\n",
get("ErrorMSG",env=.GlobalEnv),"\n")
if(!GUI) cat(ErrorMSG)
return(-1)
}
ofileAout<-paste(output,"_U.dat",sep="")
ofilePdf<-paste(output,"_U.pdf",sep="")
ofileSout<-paste(output,"_Ustat.txt",sep="")
file.create(ofileAout)
file.create(ofilePdf)
file.create(ofileSout)
N<-length(Y0); Nadj<-Ny4a*Nt
cat(paste("The nominal level of confidence (1-alpha)=",plev,"\n"),file=ofileSout)
cat(paste("Input data filename:", InSeries, "N=",N, "\n"),file=ofileSout,append=T)
readPFtable(N,pkth)
Pk0<-Pk.PMFT(N)
oout<-rmCycle(itable)
Y1<-oout$Base
EB<-oout$EB
assign("EB",EB,envir=.GlobalEnv)
if(length(EB)!=length(Icy)) {
ErrorMSG<<-paste("Annual cycle length (from non-missing data) differ from original dataset",
"\n",get("ErrorMSG",env=.GlobalEnv),"\n")
if(GUI==F) print(ErrorMSG)
return(-1)
}
Ip0<-N
otmp<-LSmultiRedCycle(Y1,Ti,Ip0,1)
beta0<-otmp$trend
meanhat0<-otmp$meanhat
Ehat0<-mean(meanhat0)
cat(file=ofileSout,paste(" Ignore changepoints -> trend0 =",
round(beta0,6),"(",round(otmp$betaL,6),",",round(otmp$betaU,6),")",
"(p=",round(otmp$p.tr,4),"); cor=",
round(otmp$cor,4),"(", round(otmp$corl,4),",",
round(otmp$corh,4),")\n"),append=T)
oout<-PMFT(Y1,Ti,Pk0)
I0<-0
I2<-oout$KPx
I4<-N
oout1<-PMFxKxI0I2(Y1,Ti,I0,I2)
I1<-oout1$Ic
oout2<-PMFxKxI0I2(Y1,Ti,I2,I4)
I3<-oout2$Ic
oout3<-PMFxKxI0I2(Y1,Ti,I1,I3)
I2<-oout3$Ic
Ns<-1
Ips<-c(I1,N)
if(I1>0){
otmp<-LSmultiple(Y1,Ti,Ips)
resi<-otmp$resi
fitted<-otmp$fitted
otmp<-Rphi(resi,Ips,Ns)
cor1<-otmp$cor
corL1<-otmp$corl
corU1<-otmp$corh
W<-otmp$W+fitted
otmp<-PMFxKc(Y1,Ti,I0,I4,I1)
PFx1<-otmp$PFc
otmp<-PMFxKc(W,Ti,I0,I4,I1)
prob1<-otmp$prob
}
else{
prob1<-0
PFx1<-0
cor1<-0
corL1<-0
}
Ips<-c(I2,N)
if(I2>0){
otmp<-LSmultiple(Y1,Ti,Ips)
resi<-otmp$resi
fitted<-otmp$fitted
otmp<-Rphi(resi,Ips,Ns)
cor2<-otmp$cor
corL2<-otmp$corl
corU2<-otmp$corh
W<-otmp$W+fitted
otmp<-PMFxKc(Y1,Ti,I0,I4,I2)
PFx2<-otmp$PFc
otmp<-PMFxKc(W,Ti,I0,I4,I2)
prob2<-otmp$prob
}
else{
prob2<-0
PFx2<-0
cor2<-0
corL2<-0
}
Ips<-c(I3,N)
if(I3>0){
otmp<-LSmultiple(Y1,Ti,Ips)
resi<-otmp$resi
fitted<-otmp$fitted
otmp<-Rphi(resi,Ips,Ns)
cor3<-otmp$cor
corL3<-otmp$corl
corU3<-otmp$corh
W<-otmp$W+fitted
otmp<-PMFxKc(Y1,Ti,I0,I4,I3)
PFx3<-otmp$PFc
otmp<-PMFxKc(W,Ti,I0,I4,I3)
prob3<-otmp$prob
}
else{
prob3<-0
PFx3<-0
cor3<-0
corL3<-0
}
ofileIout<-paste(output,"_1Cs.txt",sep="")
ofileMout<-paste(output,"_mCs.txt",sep="")
file.create(ofileIout)
tmp<-sort(c(PFx1,PFx2,PFx3),decreasing=T,index.return=T)
PFx.mx<-tmp$x[1]
prob.mx<-c(prob1,prob2,prob3)[tmp$ix[1]]
Imx<-c(I1,I2,I3)[tmp$ix[1]]
cor.mx<-c(corL1,corL2,corL3)[tmp$ix[1]]
PFx95L<-getPFx95(cor.mx,N)
if(PFx.mx<PFx95L){
Ns<-0
Ips<-N
cat(paste(Ns,"changepoints in Series", InSeries,
paste("sample:(",sprintf("%1.0f",1)," ",sprintf("%-4.4s","YifD"),
sprintf("%10.0f",19000101),")",sep=""),"\n"),file=ofileIout)
cat("PMF finds no Type-1 changepoints in the series!\n")
# return(0)
}
else{
Ns<-1
Ips<-c(Imx,N)
# if(Debug) cat(file=flog,c("First Ips:",Ips,"\n"))
tt<-TRUE
Niter<-0
while(tt){ # condition on is there any more Bps to insert in Ips?
Niter<-Niter+1
tt<-FALSE
Ips0<-NULL
for(i in 1:(Ns+1)){ # search for new break points
I0<- if(i==1) 0 else Ips[i-1]
I2<-Ips[i]
otmp<-PMFxKxI0I2(Y1,Ti,I0,I2)
if(otmp$prob>0) Ips0<-sort(c(Ips0,otmp$Ic))
}
# if(Debug) {
# cat(file=flog,paste("Niter",Niter,"new Ips:",length(Ips0),"\n"),append=T)
# cat(file=flog,c(Ips0,"\n"),append=T)
# cat(file=flog,paste("Niter",Niter,"old Ns",Ns," "),append=T)
# }
# finished search for possible new changepoints, start estimate the p-value
# of the Ips0 series and find the most significant changepoint Iseg.mx
tt1<-TRUE
while(tt1){
PFx.mx<-(-9999)
Iseg.mx<-0
PFx95L.mx<-0
if(length(Ips0)==0) tt1<-FALSE
else{
for(i in 1:length(Ips0)){
Ips1<-sort(c(Ips,Ips0[i]))
ith<-match(Ips0[i],Ips1)
otmp<-PMFxIseg(Y1,Ti,Ips1,ith)
if(otmp$PFx<otmp$PFx95L) Ips0[i]<-0
else
if(otmp$PFx>PFx.mx){
PFx.mx<-otmp$PFx
Iseg.mx<-Ips0[i]
PFx95L.mx<-otmp$PFx95L
}
}
if(PFx.mx>=PFx95L.mx){
Ips<-sort(c(Ips,Iseg.mx))
Ns<-Ns+1
Ips0<-Ips0[Ips0!=Iseg.mx]
tt<-TRUE
}
else tt1<-FALSE
Ips0<-Ips0[Ips0!=0]
}
}
# if(Debug) cat(file=flog,paste("new Ns:", Ns,"\n"),append=T)
}
# finish finding any possible new changepoints
# start to delete the least significant changepoint if it is insignificant
tt<-TRUE
while(tt){
tt<-FALSE
Iseg.mn<-0
PFx.mn<-99999
PFx95L.mn<-99999
for(i in 1:Ns){
otmp<-PMFxIseg(Y1,Ti,Ips,i)
if(otmp$PFx<PFx.mn){
Iseg.mn<-i
PFx.mn<-otmp$PFx
PFx95L.mn<-otmp$PFx95L
}
}
if(Iseg.mn>0&PFx.mn<PFx95L.mn){
Ips<-Ips[-Iseg.mn]
Ns<-Ns-1
if(Ns>0) tt<-TRUE
}
}
}
# end of detection
if(Ns>0) {
Nsegs<-Ips-c(0,Ips[1:Ns])
Iseg.longest<-sort(Nsegs,index=T,decreasing=T)$ix[1]
}
else Iseg.longest<-0
if(Iadj>(Ns+1)|Iseg.longest==0) Iseg.adj<-Ns+1
else if(Iadj==0)Iseg.adj<-Iseg.longest
else Iseg.adj<-Iadj
oout<-LSmultiRedCycle(Y1,Ti,Ips,Iseg.adj)
Y1<-oout$Y0
cor<-oout$cor
corl<-oout$corl
corh<-oout$corh
df<-(N-2-Nt-Ns)
pcor<-pt(abs(cor)*sqrt(df/(1-cor^2)),df)
W<-oout$W
WL<-oout$WL
WU<-oout$WU
EB1<-oout$EB
itmp1<-cbind(EB1,Icy)
itmp2<-cbind(1:N,Imd)
colnames(itmp2)<-c("idx","Icy")
itmp<-merge(itmp1,itmp2,by="Icy")
EBfull<-itmp[order(itmp[,"idx"]),"EB1"]
EEB<-mean(EB1,na.rm=T)
if(Ns>0){
Rb<-Y1-oout$trend*Ti+EBfull
QMout<-QMadjGaussian(Rb,Ips,Mq,Iseg.adj,Nadj)
B<-QMout$PA
cat(paste("Nseg_shortest =",QMout$Nseg.mn,"; Mq = ",QMout$Mq,"; Ny4a = ",Ny4a,"\n"),
file=ofileSout,append=T)
cat(paste("\n Adjust to segment", Iseg.adj,": from",
if(Iseg.adj==1) 1 else Ips[Iseg.adj-1]+1,
"to",Ips[Iseg.adj],"\n"),file=ofileSout,append=T)
# cat("#Fcat, DP (CDF and Differnces in category mean)\n",file=ofileSout,
# append=T)
if(Mq>1){
oline<-paste('#Fcat: frequency category boundaries\n',
'#DP: Difference in the category means\n#',sep='')
for(i in 1:Ns) oline<-paste(oline,paste('Fcat.Seg',i,sep=''),paste('DP.Seg',i,sep=''))
oline<-paste(oline,'\n')
cat(oline,file=ofileSout,append=T)
write.table(round(QMout$osmean,4),file=ofileSout,append=T,
row.names=F,col.names=F)
for(i in 1:(Ns+1)){
I1<-if(i==1) 1 else Ips[i-1]+1
I2<-Ips[i]
if(i!=Iseg.adj)
cat(paste("Seg. ",i,": mean of QM-adjustments =",round(mean(QMout$W[I1:I2]),4),
"\n",sep=""),file=ofileSout,append=T)
}
}
}
else B<-Y1-oout$trend*Ti+EBfull
# itmp1<-cbind(EB1,Icy)
# itmp2<-cbind(1:N,Imd)
# colnames(itmp2)<-c("idx","Icy")
# itmp<-merge(itmp1,itmp2,by="Icy")
# EBfull<-itmp[order(itmp[,"idx"]),"EB1"]
# EEB<-mean(EB1,na.rm=T)
adj<-oout$Y0+EBfull
# B<-B+EBfull+oout$trend*Ti
B<-B+oout$trend*Ti
cat("Common trend TPR fit to the de-seasonalized Base series:\n",
file=ofileSout,append=T)
cat(paste("#steps= ",Ns,"; trend=",round(oout$trend,6),"(",
round(oout$betaL,6),",",round(oout$betaU,6),") (p=",
round(oout$p.tr,4),"); cor=",
round(cor,4),"(",round(corl,4),",",round(corh,4),")",
round(pcor,4),"\n"),
file=ofileSout,append=T)
if(Ns>0) for(i in 1:(Ns+1)){
I1<-if(i==1) 1 else Ips[i-1]+1
I2<-Ips[i]
Delta<-oout$mu[Iseg.adj]-oout$mu[i]
adj[I1:I2]<-adj[I1:I2]+Delta
stepsize<-oout$mu[i+1]-oout$mu[i]
cat(paste(Ips[i],IY0[Ips[i]],"stepsize=",round(stepsize,4),"\n"),
file=ofileSout,append=T)
}
oR<-Y1-oout$meanhat
oR[2:N]<-oR[2:N]-oR[1:(N-1)]*cor
Ehat<-mean(oout$meanhat)
meanhat0<-meanhat0-Ehat0+Ehat
pdf(file=ofilePdf,onefile=T,paper='letter')
op <- par(no.readonly = TRUE) # the whole list of settable par's.
par(mfrow=c(2,1))
par(mar=c(3,4,3,2)+.1,cex.main=.8,cex.lab=.8,cex.axis=.8,cex=.8)
uyrs<-unique(floor(ori.itable[,1]/10))*10
labels<-NULL
ats<-NULL
for(i in 1:length(uyrs)){
if(!is.na(match(uyrs[i],ori.itable[,1]))){
labels<-c(labels,uyrs[i])
ats<-c(ats,match(uyrs[i],ori.itable[,1]))
}
}
pdata<-rep(NA,dim(ori.itable)[1])
pdata[ooflg]<-oout$Y0
plot(1:dim(ori.itable)[1],pdata,type="l",xlab="",ylab="",
ylim=c(min(oout$Y0,oout$meanhat),max(oout$Y0,oout$meanhat)),
xaxt="n",col="black",lwd=0.5,
main="Base anomaly series and regression fit")
axis(side=1,at=ats,labels=labels)
pdata[ooflg]<-oout$meanhat
lines(1:dim(ori.itable)[1],pdata,col="red")
pdata[ooflg]<-oout$Y0+EBfull
plot(1:dim(ori.itable)[1],pdata,type="l",xlab="",ylab="",
ylim=c(min(oout$Y0+EBfull,oout$meanhat+EEB),
max(oout$Y0+EBfull,oout$meanhat+EEB)),
xaxt="n",col="black",lwd=0.5,
main="Base series and regression fit")
axis(side=1,at=ats,labels=labels)
pdata[ooflg]<-oout$meanhat+EEB
lines(1:dim(ori.itable)[1],pdata,col="red")
pdata[ooflg]<-adj
plot(1:dim(ori.itable)[1],pdata,type="l",xlab="",ylab="",
ylim=c(min(c(adj,B)),max(c(adj,B))),
xaxt="n",col="black",lwd=0.5,
main="Mean-adjusted base series")
axis(side=1,at=ats,labels=labels)
if(Mq>1){
pdata[ooflg]<-B
plot(1:dim(ori.itable)[1],pdata,type="l",xlab="",ylab="",
ylim=c(min(c(adj,B)),max(c(adj,B))),
xaxt="n",col="black",lwd=0.5,
main="QM-adjusted base series")
axis(side=1,at=ats,labels=labels)
}
# test plot
if(Ns>0&Mq>1){
par(mar=c(4,5,3,2)+.1,cex=.8,mfrow=c(2,2),mgp=c(1.2,.5,0))
col=0
np<-0
osp<-QMout$osp
osmean<-QMout$osmean
for(i in 1:(Ns+1)){
Fd<-.5/QMout$Mq
I1<-if(i==1) 1 else Ips[i-1]+1
I2<-Ips[i]
ymax<-max(osp[,2:3],na.rm=T); ymin<-min(osp[,2:3],na.rm=T)
if(i!=Iseg.adj){
np<-np+1
if(col==0) {
col<-2
plot(osp[I1:I2,2],osp[I1:I2,3],xlim=c(0,1),ylim=c(ymin,ymax),
type="l",lwd=1,col=col,xlab="Cumulative Frequency",
ylab="QM Adjustment")
title(cex.main=0.9,main=paste("distribution of QM adjustments with Mq=",QMout$Mq),line=.5)
icol<-2*np
for(j in 1:QMout$Mq){
lines(c(osmean[(j+1),icol]-Fd,osmean[(j+1),icol]+Fd),
c(rep(osmean[(j+1),(icol+1)],2)),col=col,lty=2,lwd=.5)
if(j>=1&j<QMout$Mq) lines(rep(osmean[(j+1),icol]+Fd,2),
c(osmean[(j+1),(icol+1)],osmean[(j+2),(icol+1)]),col=col,lty=2,lwd=0.5)
}
}
else{
col<-col+1
lines(osp[I1:I2,2],osp[I1:I2,3],lwd=1,col=col)
icol<-2*np
for(j in 1:QMout$Mq){
lines(c(osmean[(j+1),icol]-Fd,osmean[(j+1),icol]+Fd),
c(rep(osmean[(j+1),(icol+1)],2)),col=col,lty=2,lwd=.5)
if(j>=1&j<QMout$Mq) lines(rep(osmean[(j+1),icol]+Fd,2),
c(osmean[(j+1),(icol+1)],osmean[(j+2),(icol+1)]),col=col,lty=2,lwd=0.5)
}
}
text(.15,ymax-np*(ymax-ymin)/(Ns*3),paste("Seg.",i))
lines(c(.25,.30),rep(ymax-np*(ymax-ymin)/(Ns*3),2),lwd=2,col=col)
}
else np<-np+1
}
}
par(op)
dev.off()
odata<-matrix(NA,dim(ori.itable)[1],10)
# odata[ooflg,1]<-Ti
odata[,1]<-c(1:dim(ori.itable)[1])
odata[,2]<-ori.itable[,1]*10000+ori.itable[,2]*100+ori.itable[,3]
# odata[ooflg,3]<-round(oout$Y0+EBfull,4)
odata[,3]<-ori.itable[,4]
odata[ooflg,4]<-round(oout$meanhat+EEB,4)
odata[ooflg,5]<-round(adj,4)
odata[ooflg,6]<-round(oout$Y0,4)
odata[ooflg,7]<-round(oout$meanhat,4)
odata[ooflg,8]<-round(oout$meanhat+EBfull,4)
if(Ns>0) if(QMout$Mq>1) odata[ooflg,9]<-round(B,4)
odata[ooflg,10]<-round(meanhat0,4)
Imd1<-ori.itable[,2]*100+ori.itable[,3]
if(sum(is.na(ori.itable[,4])==F&Imd1==229)>0){
if(Ns>0){
tdata<-ori.itable[is.na(ori.itable[,4])==F,]
IY1<-tdata[,1]*10000+tdata[,2]*100+tdata[,3]
Ips.ymd<-IY0[Ips]
Ips.1<-rep(NA,Ns+1)
for(i in 1:Ns) Ips.1[i]<-c(1:length(IY1))[IY1==Ips.ymd[i]]
Ips.1[Ns+1]<-length(IY1)
# Ips.1<-c(1:length(IY1))[Ips.ymd==IY1]
Imd2<-tdata[,2]*100+tdata[,3]
Ids.leap<-c(1:length(Imd2))[Imd2==229]
Nl<-length(Ids.leap)
Rb<-Y1-oout$trend*Ti+EBfull
Rb1<-tdata[,4]; Rb1[-Ids.leap]<-Rb
Ti1<-rep(NA,length(IY1)); Ti1[-Ids.leap]<-Ti
for(i in 1:length(Ids.leap)) {
Rb1[Ids.leap[i]]<-tdata[Ids.leap[i],4]+Rb1[Ids.leap[i]-1]-tdata[Ids.leap[i]-1,4]
Ti1[Ids.leap[i]]<-Ti1[Ids.leap[i]-1]
}
if(QMout$Mq>1){
B1<-QMadjGaussian(Rb1,Ips.1,Mq,Iseg.adj,Nadj)$PA
B1<-B1+oout$trend*Ti1
B1.leap<-B1[Ids.leap]
odata[is.na(odata[,3])==F&Imd1==229,9]<-round(B1.leap,4)
}
}
else
odata[Imd1==229,9]<-odata[Imd1==229,3]
Ids.leapo<-c(1:dim(ori.itable)[1])[is.na(ori.itable[,4])==F&Imd1==229]
for(jth in 1:length(Ids.leapo)){
kth<-Ids.leapo[jth]
if(Ns>0){
k1th<-if(odata[kth-1,2]%in%IY0[Ips]) (kth+1) else (kth-1)
}
else k1th<-kth-1
for(pth in c(4,7,8,10)) odata[kth,pth]<-odata[k1th,pth]
for(pth in c(5,6)){delta1<-odata[k1th,3]-odata[k1th,pth]; odata[kth,pth]<-odata[kth,3]-delta1}
}
}
write.table(file=ofileAout,odata,na=MissingValueCode,
col.names=F,row.names=F)
otmp<-LSmultiple(Y1,Ti,Ips)
resi<-otmp$resi
otmpW<-LSmultiple(W,Ti,Ips)
resiW<-otmpW$resi
otmpWL<-LSmultiple(WL,Ti,Ips)
resiWL<-otmpWL$resi
otmpWU<-LSmultiple(WU,Ti,Ips)
resiWU<-otmpWU$resi
if(Ns==0) {
cat(paste(Ns,"changepoints in Series", InSeries,
paste("sample:(",sprintf("%1.0f",1)," ",sprintf("%-4.4s","YifD"),
sprintf("%10.0f",19000101),")",sep=""),"\n"),file=ofileIout)
cat("PMF finds no Type-1 changepoints in the series!\n")
# return(0)
}
else{
cat(paste(Ns,"changepoints in Series", InSeries,"\n"),
file=ofileIout)
for(i in 1:Ns){
I1<- if(i==1) 1 else Ips[i-1]+1
Ic<-Ips[i]
I3<-Ips[i+1]
Nseg<-I3-I1+1
PFx95<-getPFx95(cor,Nseg)
PFx95l<-getPFx95(corl,Nseg)
PFx95h<-getPFx95(corh,Nseg)
SSEf.Iseg<-sum(resi[I1:I3]^2)
Ips1<-Ips[-i]
otmp1<-LSmultiple(Y1,Ti,Ips1)
SSE0.Iseg<-sum(otmp1$resi[I1:I3]^2)
Fx<-(SSE0.Iseg-SSEf.Iseg)*(Nseg-3)/SSEf.Iseg
Pk1<-Pk.PMFT(Nseg)
PFx<-Fx*Pk1[Ic-I1+1]
SSEf.Iseg<-sum(resiW[I1:I3]^2)
otmp1<-LSmultiple(W,Ti,Ips1)
SSE0.Iseg<-sum(otmp1$resi[I1:I3]^2)
Fx<-(SSE0.Iseg-SSEf.Iseg)*(Nseg-3)/SSEf.Iseg
if(Fx>0) prob<-pf(Fx,1,Nseg-3)
else{
Fx<-0
prob<-0
PFx<-0
}
SSEf.Iseg<-sum(resiWL[I1:I3]^2)
otmp1<-LSmultiple(WL,Ti,Ips1)
SSE0.Iseg<-sum(otmp1$resi[I1:I3]^2)
FxL<-(SSE0.Iseg-SSEf.Iseg)*(Nseg-3)/SSEf.Iseg
if(FxL>0) probL<-pf(Fx,1,Nseg-3)
else probL<-0
SSEf.Iseg<-sum(resiWU[I1:I3]^2)
otmp1<-LSmultiple(WU,Ti,Ips1)
SSE0.Iseg<-sum(otmp1$resi[I1:I3]^2)
FxU<-(SSE0.Iseg-SSEf.Iseg)*(Nseg-3)/SSEf.Iseg
if(FxU>0) probU<-pf(Fx,1,Nseg-3)
else probU<-0
# else if(Id==1) { # type-1 changepoints
if(PFx<PFx95l) Idc<-"No "
else if(PFx>=PFx95l&PFx<PFx95h) Idc<-"? "
else if(PFx>=PFx95h) Idc<-"Yes "
# }
cat(paste(sprintf("%1.0f",1)," ",
sprintf("%-4.4s",Idc),
sprintf("%10.0f", IY0[Ic])," (",
sprintf("%6.4f",probL),"-",
sprintf("%6.4f",probU),")",
sprintf("%6.3f",plev),
sprintf("%10.4f",PFx)," (",
sprintf("%10.4f",PFx95l),"-",
sprintf("%10.4f",PFx95h),")\n",sep=""),
file=ofileIout,
append=TRUE)
cat(paste("PMF : c=", sprintf("%4.0f",Ic),
"; (Time ", sprintf("%10.0f",IY0[Ic]),
"); Type= 1; p=",sprintf("%10.4f",prob),"(",
sprintf("%6.4f",probL),"-",
sprintf("%6.4f",probU),")",
"; PFmax=", sprintf("%10.4f",PFx),
"; CV95=", sprintf("%10.4f",PFx95),
"(", sprintf("%10.4f",PFx95l),
"-", sprintf("%10.4f",PFx95h),
"); Nseg=", sprintf("%4.0f",Nseg),"\n",sep=""),
file=ofileSout, append=T)
}
}
if(GUI)
return(0)
else{
file.copy(from=ofileIout,to=ofileMout,overwrite=TRUE)
cat("FindU finished successfully...\n")
}
}
FindUD<-function(InSeries,InCs,output,MissingValueCode,GUI=FALSE,p.lev=0.95,
Iadj=10000,Mq=10,Ny4a=0){
Debug<-TRUE
ErrorMSG<-NA
assign("ErrorMSG",ErrorMSG,envir=.GlobalEnv)
flog<-paste(output,".log",sep="")
Nmin<-10
if(Ny4a>0&Ny4a<=5) Ny4a<-5
if(!p.lev%in%c(0.75,0.8,0.9,0.95,0.99,0.9999)){
ErrorMSG<<-paste("FindU: input p.lev",p.lev,"error\n",
get("ErrorMSG",env=.GlobalEnv),"\n")
if(!GUI) cat(ErrorMSG)
return(-1)
}
plev<-p.lev
pkth<-match(p.lev,c(0.75,0.8,0.9,0.95,0.99,0.9999))
assign("Nmin",Nmin,envir=.GlobalEnv)
itmp<-Read(InSeries,MissingValueCode)
if(itmp<0){
ErrorMSG<<-paste("FindUD: Error in read data from",InSeries,"\n",
get("ErrorMSG",env=.GlobalEnv),"\n")
if(!GUI) cat(ErrorMSG)
return(-1)
}
N<-length(Y0); Nadj<-Ny4a*Nt
readPFtable(N,pkth)
itmp<-readLines(InCs)
Pk0<-Pk.PMFT(N)
oout<-rmCycle(itable)
Y1<-oout$Base
EB<-oout$EB
assign("EB",EB,envir=.GlobalEnv)
if(length(EB)!=length(Icy)) {
ErrorMSG<<-paste("Annual cycle length (from non-missing data) differ from original dataset",
"\n",get("ErrorMSG",env=.GlobalEnv),"\n")
if(!GUI) cat(ErrorMSG)
return(-1)
}
ofileIout<-paste(output,"_pCs.txt",sep="")
ofileMout<-paste(output,"_mCs.txt",sep="")
file.create(ofileIout)
ofileAout<-paste(output,"_UD.dat",sep="")
ofilePdf<-paste(output,"_UD.pdf",sep="")
ofileSout<-paste(output,"_UDstat.txt",sep="")
file.create(ofileAout)
file.create(ofilePdf)
file.create(ofileSout)
cat(paste("The nominal level of confidence (1-alpha)=",plev,"\n"),file=ofileSout)
cat(paste("Input data filename:", InSeries,"N=",N,"\n"),file=ofileSout,append=T)
Ip0<-N
oout<-LSmultiRedCycle(Y1,Ti,Ip0,1)
beta0<-oout$trend
betaL0<-oout$betaL
betaU0<-oout$betaU
meanhat0<-oout$meanhat
Ehat0<-mean(meanhat0)
p.tr0<-oout$p.tr
corD<-oout$cor
corDL<-oout$corl
corDU<-oout$corh
if(length(itmp)<2){ # no input changepoints
oout<-PMFT(Y1,Ti,Pk0)
I0<-0
I2<-oout$KPx
I4<-N
oout1<-PMFxKxI0I2(Y1,Ti,I0,I2)
I1<-oout1$Ic
oout2<-PMFxKxI0I2(Y1,Ti,I2,I4)
I3<-oout2$Ic
oout3<-PMFxKxI0I2(Y1,Ti,I1,I3)
I2<-oout3$Ic
Ns<-1
Ips<-c(I1,N)
if(I1>0){
otmp<-LSmultiple(Y1,Ti,Ips)
resi<-otmp$resi
fitted<-otmp$fitted
otmp<-Rphi(resi,Ips,Ns)
cor1<-otmp$cor
corL1<-otmp$corl
corU1<-otmp$corh
W<-otmp$W+fitted
otmp<-PMFxKc(Y1,Ti,I0,I4,I1)
PFx1<-otmp$PFc
otmp<-PMFxKc(W,Ti,I0,I4,I1)
prob1<-otmp$prob
}
else{
prob1<-0
PFx1<-0
}
Ips<-c(I2,N)
if(I2>0){
otmp<-LSmultiple(Y1,Ti,Ips)
resi<-otmp$resi
fitted<-otmp$fitted
otmp<-Rphi(resi,Ips,Ns)
cor2<-otmp$cor
corL2<-otmp$corl
corU2<-otmp$corh
W<-otmp$W+fitted
otmp<-PMFxKc(Y1,Ti,I0,I4,I2)
PFx2<-otmp$PFc
otmp<-PMFxKc(W,Ti,I0,I4,I2)
prob2<-otmp$prob
}
else{
prob2<-0
PFx2<-0
}
Ips<-c(I3,N)
if(I3>0){
otmp<-LSmultiple(Y1,Ti,Ips)
resi<-otmp$resi
fitted<-otmp$fitted
otmp<-Rphi(resi,Ips,Ns)
cor3<-otmp$cor
corL3<-otmp$corl
corU3<-otmp$corh
W<-otmp$W+fitted
otmp<-PMFxKc(Y1,Ti,I0,I4,I3)
PFx3<-otmp$PFc
otmp<-PMFxKc(W,Ti,I0,I4,I3)
prob3<-otmp$prob
}
else{
prob3<-0
PFx3<-0
}
tmp<-sort(c(PFx1,PFx2,PFx3),decreasing=T,index.return=T)
PFx.mx<-tmp$x[1]
prob.mx<-c(prob1,prob2,prob3)[tmp$ix[1]]
Imx<-c(I1,I2,I3)[tmp$ix[1]]
if(prob.mx<plev){
# cat("PMF finds the series to be homogeneous!\n",file=ofileIout)
cat(paste(0,"changepoints in Series", InSeries,"\n"),file=ofileIout)
cat("PMF finds the series to be homogeneous!\n")
# return()
Ns<-0
Ips<-N
Ids<-c(0)
}
else{
Ns<-1
Ips<-c(Imx,N)
Ids<-c(0,1)
}
}
else{
Ns<-length(itmp)-1
Ips<-c(rep(0,Ns),N)
Ids<-rep(0,Ns)
for(i in 1:Ns){ # using YYYYMMDD as index, searching for the largest
# date less or equal to given YYYYMMDD
ymdtmp<-as.numeric(substr(itmp[i+1],7,16))
it<-match(ymdtmp,IY0)
if(!is.na(it)) Ips[i]<-it
else Ips[i]<-max(c(1:N)[IY0<=ymdtmp])
Ids[i]<-as.numeric(substr(itmp[i+1],1,1))
}
if(sum(is.na(Ips))>0|!identical(Ips,sort(Ips))){
ErrorMSG<<-paste("FindUD: Ips read in from ",InCs,"error:")
for(i in 1:Ns)
ErrorMSG<<-paste(get("ErrorMSG",env=.GlobalEnv),Ips[i])
ErrorMSG<<-paste(get("ErrorMSG",env=.GlobalEnv),"\n")
if(!GUI) cat(ErrorMSG)
return(-1)
}
}
if(Ns>0){
Ips.i<-Ips
Niter<-0
# start search for all possible changepoints
tt<-TRUE
while(tt){
Niter<-Niter+1
tt<-FALSE
Ips0<-NULL
for(i in 1:(Ns+1)){
I0<- if(i==1) 0 else Ips[i-1]
I2<-Ips[i]
otmp<-PMFxKxI0I2(Y1,Ti,I0,I2)
if(otmp$prob>0) Ips0<-sort(c(Ips0,otmp$Ic))
}
# estimate p-value of each changepoint in series Ips0, find the most significant
# changepoint Iseg.mx
tt1<-TRUE
while(tt1){
if(length(Ips0)==0) tt1<-FALSE
else{
Iseg.mx<-0
prob.mx<-(-1)
probL.mx<-(-1)
PFx.mx<-(-1)
for(i in 1:length(Ips0)){
Ips1<-sort(c(Ips,Ips0[i]))
ith<-match(Ips0[i],Ips1)
otmp<-PMFxIseg(Y1,Ti,Ips1,ith)
probL<-min(c(otmp$probL,otmp$probU,otmp$prob))
probU<-max(c(otmp$probL,otmp$probU,otmp$prob))
PFx<-otmp$PFx
if(probU<plev) Ips0[i]<-0
else
if(PFx>PFx.mx){
prob.mx<-otmp$prob
probL.mx<-probL
Iseg.mx<-Ips0[i]
PFx.mx<-PFx
}
}
if(probL.mx>=plev){
Ips<-sort(c(Ips,Iseg.mx))
Ns<-Ns+1
Ips0<-Ips0[Ips0!=Iseg.mx]
tt<-TRUE
}
else tt1<-FALSE
Ips0<-Ips0[Ips0!=0]
}
}
}
Ids0<-rep(NA,length(Ips))
for(i in 1:length(Ips)){
if(Ips[i]%in%Ips.i) Ids0[i]<-Ids[Ips.i==Ips[i]]
else Ids0[i]<-0
}
Ids<-Ids0
# Ids<-as.integer(Ips%in%Ips.i)
tt<-TRUE
while(tt){
tt<-FALSE
probL.mn<-9999
Iseg.mn<-0
for(i in 1:Ns){
if(Ids[i]==0){ # check those un-documented
Ips0<-Ips[-i]
otmp<-PMFxIseg(Y1,Ti,Ips,i)
probL<-min(otmp$probL,otmp$probU)
if(probL<probL.mn){
Iseg.mn<-i
probL.mn<-probL
}
} # end if documented
}
if(Iseg.mn>0&probL.mn<plev){
Ips<-Ips[-Iseg.mn]
Ids<-Ids[-Iseg.mn]
Ns<-Ns-1
if(Ns>0) tt<-TRUE
}
}
}
# all changepoints are significant, calculate stats and output
if(Ns>0) {
Nsegs<-Ips-c(0,Ips[1:Ns])
Iseg.longest<-sort(Nsegs,index=T,decreasing=T)$ix[1]
}
else Iseg.longest<-0
if(Iadj>(Ns+1)|Iseg.longest==0) Iseg.adj<-Ns+1
else if(Iadj==0)Iseg.adj<-Iseg.longest
else Iseg.adj<-Iadj
otmp<-LSmultiRedCycle(Y1,Ti,Ips,Iseg.adj)
Y1<-otmp$Y0
cor<-otmp$cor
corl<-otmp$corl
corh<-otmp$corh
df<-(N-2-Nt-Ns)
pcor<-pt(abs(cor)*sqrt(df/(1-cor^2)),df)
Rf<-otmp$resi
W<-otmp$W
WL<-otmp$WL
WU<-otmp$WU
EB1<-otmp$EB
itmp1<-cbind(EB1,Icy)
itmp2<-cbind(1:N,Imd)
colnames(itmp2)<-c("idx","Icy")
itmp<-merge(itmp1,itmp2,by="Icy")
EBfull<-itmp[order(itmp[,"idx"]),"EB1"]
EEB<-mean(EB1,na.rm=T)
if(Ns>0){
Rb<-Y1-otmp$trend*Ti+EBfull
QMout<-QMadjGaussian(Rb,Ips,Mq,Iseg.adj,Nadj)
B<-QMout$PA
cat(paste("Nseg_shortest =",QMout$Nseg.mn,"; Mq = ",QMout$Mq,"; Ny4a = ",Ny4a,"\n"),
file=ofileSout,append=T)
cat(paste("\n Adjust to segment", Iseg.adj,": from",
if(Iseg.adj==1) 1 else Ips[Iseg.adj-1]+1,
"to",Ips[Iseg.adj],"\n"),file=ofileSout,append=T)
# cat("#Fcat, DP (CDF and Differnces in category mean)\n",file=ofileSout,
# append=T)
if(QMout$Mq>1){
oline<-paste('#Fcat: frequency category boundaries\n',
'#DP: Difference in the category means\n#',sep='')
for(i in 1:Ns) oline<-paste(oline,paste('Fcat.Seg',i,sep=''),paste('DP.Seg',i,sep=''))
oline<-paste(oline,'\n')
cat(oline,file=ofileSout,append=T)
write.table(round(QMout$osmean,4),file=ofileSout,append=T,
row.names=F,col.names=F)
for(i in 1:(Ns+1)){
I1<-if(i==1) 1 else Ips[i-1]+1
I2<-Ips[i]
if(i!=Iseg.adj)
cat(paste("Seg. ",i,": mean of QM-adjustments =",round(mean(QMout$W[I1:I2]),4),
"\n",sep=""),file=ofileSout,append=T)
}
}
}
else B<-Y1-otmp$trend*Ti+EBfull
adj<-otmp$Y0+EBfull
# B<-B+EBfull+otmp$trend*Ti
B<-B+otmp$trend*Ti
cat(file=ofileSout,paste(" Ignore changepoints -> trend0 =",
round(beta0,6),"(",round(betaL0,6),",",round(betaU0,6),") (p=",
round(p.tr0,4),"); cor=", round(corD,4),"(",round(corDL,4),