changepoint-new: Methods for Changepoint Detection

single.meanvar.gamma.calc <-
function(data,shape=1,extrainf=TRUE,minseglen){
  singledim=function(data,shape,extrainf=TRUE,minseglen){
    n=length(data)
    y=c(0,cumsum(data))
    null=2*n*shape*log(y[n+1])-2*n*shape*log(n*shape)
    taustar=minseglen:(n-minseglen)
    tmp=2*taustar*shape*log(y[taustar+1]) -2*taustar*shape*log(taustar*shape) + 2*(n-taustar)*shape*log((y[n+1]-y[taustar+1]))-2*(n-taustar)*shape*log((n-taustar)*shape)
    
    tau=which(tmp==min(tmp,na.rm=T))[1]
    taulike=tmp[tau]
    tau=tau+minseglen-1 # correcting for the fact that we are starting at minseglen
    if(extrainf==TRUE){
      out=c(tau,null,taulike)
      names(out)=c('cpt','null','alt')
      return(out)
    }
    else{
      return(tau)
    }
  }
    

  if(is.null(dim(data))==TRUE){
    # single data set
    cpt=singledim(data,shape,extrainf,minseglen)
    return(cpt)
  }
  else{
    rep=nrow(data)
    n=ncol(data)
    cpt=NULL
    if(length(shape)==1){
	shape=rep(shape,rep)
    }
    if(extrainf==FALSE){
      for(i in 1:rep){
        cpt[i]=singledim(data[i,],shape[i],extrainf,minseglen)
      }
    }
    else{
      cpt=matrix(0,ncol=3,nrow=rep)
      for(i in 1:rep){
        cpt[i,]=singledim(data[i,],shape[i],extrainf,minseglen)
      }
      colnames(cpt)=c('cpt','null','alt')
    }
    return(cpt)
  }
}

single.meanvar.gamma<-function(data,shape=1,penalty="MBIC",pen.value=0,class=TRUE,param.estimates=TRUE,minseglen){
	if(sum(data<=0)>0){stop('Gamma test statistic requires positive data')}
	if(is.null(dim(data))==TRUE){
	  # single dataset
	  n=length(data)
	}
	else{
	  n=ncol(data)
	}
	if(n<4){stop('Data must have at least 4 observations to fit a changepoint model.')}
  if(n<(2*minseglen)){stop('Minimum segment length is too large to include a change in this data')}
  
	pen.value = penalty_decision(penalty, pen.value, n, diffparam=1, asymcheck="meanvar.gamma", method="AMOC")
	if(is.null(dim(data))==TRUE){
		tmp=single.meanvar.gamma.calc(coredata(data),shape,extrainf=TRUE,minseglen)
		if(penalty=="MBIC"){
		  tmp[3]=tmp[3]+log(tmp[1])+log(n-tmp[1]+1)
		}
		ans=decision(tmp[1],tmp[2],tmp[3],penalty,n,diffparam=1,pen.value)
		if(class==TRUE){
      return(class_input(data, cpttype="mean and variance", method="AMOC", test.stat="Gamma", penalty=penalty, pen.value=ans$pen, minseglen=minseglen, param.estimates=param.estimates, out=c(0,ans$cpt), shape=shape))
		}
		else{ 
      return(ans$cpt)
		}
	}
	else{ 
		tmp=single.meanvar.gamma.calc(data,shape,extrainf=TRUE,minseglen)
		if(penalty=="MBIC"){
		  tmp[,3]=tmp[,3]+log(tmp[,1])+log(n-tmp[,1]+1)
		}
		ans=decision(tmp[,1],tmp[,2],tmp[,3],penalty,n,diffparam=1,pen.value)
		if(class==TRUE){
			rep=nrow(data)
			out=list()
			for(i in 1:rep){
        out[[i]]=class_input(data[i,], cpttype="mean and variance", method="AMOC", test.stat="Gamma", penalty=penalty, pen.value=ans$pen, minseglen=minseglen, param.estimates=param.estimates, out=c(0,ans$cpt[i]), shape=shape)
			}
			return(out)
		}
		else{ return(ans$cpt)}
	}
}

# PELT.meanvar.gamma=function(data,shape=1,pen=0,nprune=FALSE){
#   mll.meanvar.EFK=function(x,n,shape){
#     return( 2*n*shape*log(x)-2*n*shape*log(n*shape))
#   }
#   n=length(data)
#   y=c(0,cumsum(data))
# 
#   lastchangecpts=matrix(NA,nrow=n,ncol=2)
#   lastchangelike=matrix(NA,nrow=n,ncol=2)
#   checklist=NULL
#   lastchangelike[1,]=c(mll.meanvar.EFK(y[2],1,shape),mll.meanvar.EFK(y[n+1]-y[2],n-1,shape)+pen)
#   lastchangecpts[1,]=c(0,1)
#   lastchangelike[2,]=c(mll.meanvar.EFK(y[3],2,shape),mll.meanvar.EFK(y[n+1]-y[3],n-2,shape)+pen)
#   lastchangecpts[2,]=c(0,2)
#   lastchangelike[3,]=c(mll.meanvar.EFK(y[4],3,shape),mll.meanvar.EFK(y[n+1]-y[4],n-3,shape)+pen)
#   lastchangecpts[3,]=c(0,3)
#   noprune=NULL
#   for(tstar in 4:n){
#     tmplike=NULL
#     tmpt=c(checklist, tstar-2)
#     tmplike=lastchangelike[tmpt,1]+mll.meanvar.EFK(y[tstar+1]-y[tmpt+1],tstar-tmpt,shape)+pen
#     if(tstar==n){
#       lastchangelike[tstar,]=c(min(c(tmplike,mll.meanvar.EFK(y[tstar+1]-y[1],tstar,shape)),na.rm=TRUE),0)
#     }
#     else{
#       lastchangelike[tstar,]=c(min(c(tmplike,mll.meanvar.EFK(y[tstar+1]-y[1],tstar,shape)),na.rm=TRUE),mll.meanvar.EFK(y[n+1]-y[tstar+1],n-tstar,shape)+pen)
#     }
#     if(lastchangelike[tstar,1]==mll.meanvar.EFK(y[tstar+1]-y[1],tstar,shape)){
#       lastchangecpts[tstar,]=c(0,tstar)
#     }
#     else{
#       cpt=tmpt[tmplike==lastchangelike[tstar,1]][1]
#       lastchangecpts[tstar,]=c(cpt,tstar)
#     }
#     checklist=tmpt[tmplike<=lastchangelike[tstar,1]+pen]
#     if(nprune==TRUE){
#       noprune=c(noprune,length(checklist))
#     }
#   }
#   if(nprune==TRUE){
#     return(nprune=noprune)
#   }
#   else{
#     fcpt=NULL
#     last=n
#     while(last!=0){
# 	fcpt=c(fcpt,lastchangecpts[last,2])
# 	last=lastchangecpts[last,1]
#     }
#     return(cpt=sort(fcpt))
#   }
# }


# binseg.meanvar.gamma=function(data,shape=1,Q=5,pen=0){
#   mll.meanvar=function(x,n,shape){
#     return(n*shape*log(n*shape)-n*shape*log(x))
#   }
#   n=length(data)
#   y=c(0,cumsum(data))
#   tau=c(0,n)
#   cpt=matrix(0,nrow=2,ncol=Q)
#   oldmax=1000
# 
#   for(q in 1:Q){
#     lambda=rep(0,n-1)
#     i=1
#     st=tau[1]+1;end=tau[2]
#     null=mll.meanvar(y[end+1]-y[st],end-st+1,shape)
#     for(j in 1:(n-1)){
#       if(j==end){
#         st=end+1;i=i+1;end=tau[i+1]
#         null=mll.meanvar(y[end+1]-y[st],end-st+1,shape)
#       }else{
# 	if((j-st)<2){lambda[j]=-1*10^(100)}
# 	else if((end-j)<2){lambda[j]=-1*10^(100)}
# 	else{lambda[j]=mll.meanvar(y[j+1]-y[st],j-st+1,shape)+mll.meanvar(y[end+1]-y[j+1],end-j,shape)-null}
#       }
#     }
#     k=which.max(lambda)[1]
#     cpt[1,q]=k;cpt[2,q]=min(oldmax,max(lambda))
#     oldmax=min(oldmax,max(lambda))
#     tau=sort(c(tau,k))
#   }
#   op.cps=NULL
#   p=1:(Q-1)
#   for(i in 1:length(pen)){
#     criterion=(2*cpt[2,])>=pen[i]
#     if(sum(criterion)==0){
#       op.cps=0
#     }
#     else{
#       op.cps=c(op.cps,max(which((criterion)==TRUE)))
#     }
#   }
#   return(list(cps=cpt,op.cpts=op.cps,pen=pen))
# }


multiple.meanvar.gamma=function(data,shape=1,mul.method="PELT",penalty="MBIC",pen.value=0,Q=5,class=TRUE,param.estimates=TRUE,minseglen){
  if(sum(data<=0)>0){stop('Gamma test statistic requires positive data')}
	if(!((mul.method=="PELT")||(mul.method=="BinSeg"))){
		stop("Multiple Method is not recognised, must be PELT or BinSeg.")
	}
  costfunc = "meanvar.gamma"
  if(penalty=="MBIC"){
	  costfunc = "meanvar.gamma.mbic"
	}
	diffparam=1
	if(is.null(dim(data))==TRUE){
		# single dataset
		n=length(data)
		shape=shape[1]
	}
	else{
		n=ncol(data)
	}
	if(n<(2*minseglen)){stop('Minimum segment length is too large to include a change in this data')}
	
	pen.value = penalty_decision(penalty, pen.value, n, diffparam, asymcheck = costfunc, method=mul.method)
	
	if(is.null(dim(data))==TRUE){
		# single dataset
	  out = data_input(data=data,method=mul.method,pen.value=pen.value,costfunc=costfunc,minseglen=minseglen,Q=Q,shape=shape)
	 
		if(class==TRUE){
      return(class_input(data, cpttype="mean and variance", method=mul.method, test.stat="Gamma", penalty=penalty, pen.value=pen.value, minseglen=minseglen, param.estimates=param.estimates, out=out, Q=Q, shape=shape))
		}
		else{ return(out[[2]])}
	}
	else{
		rep=nrow(data)
		out=list()
		if(length(shape)!=rep){
			shape=rep(shape,rep)
		}
    for(i in 1:rep){
      out[[i]]=data_input(data[i,],method=mul.method,pen.value=pen.value,costfunc=costfunc,minseglen=minseglen,Q=Q, shape=shape)
    }

    cpts=lapply(out, '[[', 2)

		if(class==TRUE){
			ans=list()
			for(i in 1:rep){
        ans[[i]]=class_input(data[i,], cpttype="mean and variance", method=mul.method, test.stat="Gamma", penalty=penalty, pen.value=pen.value, minseglen=minseglen, param.estimates=param.estimates, out=out[[2]], Q=Q, shape=shape[[i]])
			}
			return(ans)
		}
		else{return(cpts)}
	}
}