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# SubSubsection 2.1.2 (Function for construction of A-optimal or near-optimal row-column design using array exhange algorithm)
Aoptrcd.maeA<-function(trt.N,col.N,theta,nrep,itr.cvrgval) {
#House keeping
arrays.1=t(combn(trt.N,2))
arrays=rbind(arrays.1,t(rbind(arrays.1[,2],arrays.1[,1])))
na=dim(arrays)[1]
del.1<-matrix(1000,na,3)
desbest.1<-matrix(0,nrep*2,col.N)
aoptbest.1<-matrix(0,nrep,2)
#Start iteration
for(irep in 1:nrep){
#Initial design with its corresponding Ascore value
des<-intcrcd.mae(trt.N,col.N)
if(trt.N==col.N&trt.N>3&irep<(trt.N-1)) {in.desns=matrix(0,(trt.N-3)*2,col.N)
in.desns0=rbind(seq(1,trt.N),c(seq(1,trt.N)[2:trt.N],1))
for(i in 1:(trt.N-3)) {in.desns01=cbind(rbind(seq(1,(trt.N-i)),c(seq(1,(trt.N-i))[2:(trt.N-i)],1)),rbind(rep(1,i),((trt.N-i+1):trt.N))); in.desns[c((i-1)*2+1,i*2),]=in.desns01}
in.desns=rbind(rbind(seq(1,trt.N),c(seq(1,trt.N)[2:trt.N],1)),in.desns)
des=in.desns[c((irep-1)*2+1,irep*2),]}
cmat<-cmatrcd.mae(trt.N,col.N,theta,des)
aopt=sum(diag(ginv(cmat)))
acold=aopt
descold=t(des)
cdel=100
while( abs(cdel)>=0.000000001){
i=1;
ivalacold={}
for(i in 1:col.N){
for(j in 1:na){
temp=descold[i,]
if(all(descold[i,]==arrays[j,])) {aopt=acold; del.1[j,]<-c(j,(acold-aopt),aopt); next}
descold[i,]=arrays[j,]
trtin<-contrasts(as.factor(t(descold)),contrasts=FALSE)[as.factor(t(descold)),]
R.trt<-t(trtin)%*%trtin
if (rankMatrix(R.trt)[1]<trt.N) {aopt=1000; del.1[j,]<-c(j,(acold-aopt),aopt); next}
cmato=cmatrcd.mae(trt.N,col.N, 0,t(descold))
egv<-sort(eigen(cmato)$values)
if(egv[2]<0.000001) {aopt=1000; del.1[j,]<-c(j,(acold-aopt),aopt); next}
cmat=cmatrcd.mae(trt.N,col.N,theta,t(descold))
aopt=sum(diag(ginv(cmat)))
del.n<-del.1[j,]<-c(j,(acold-aopt),aopt)
#print(del.n)
descold[i,]=temp
}
del.1<-del.1[order(del.1[,3]),]
delbest=t(del.1[1,])
descold[i,]=arrays[delbest[1],]
acold=delbest[3]
cdel=delbest[2]
ivalacold=rbind(ivalacold, c(i,acold))
if(i>itr.cvrgval) if(all(ivalacold[c(i-(itr.cvrgval-2),i),2]==ivalacold[i-(itr.cvrgval-1),2])) break
}
#print(c(000,irep,acold,cdel,000))
}
#aopt0=acold
cdel<-1000
while( abs(cdel)>=0.000000001){
aopt=acold
#desg<-graph(t(descold))
#plot(desg)
del.2<-matrix(1000,col.N+1,3)
del.2[col.N+1,]<-c(col.N+1,0,acold)
for(i in 1:col.N){
temp=descold[i,]
descold[i,]=rev(descold[i,])
cmato=cmatrcd.mae(trt.N,col.N, 0,t(descold))
egv<-sort(eigen(cmato)$values)
if(egv[2]<0.000001) {aopt2=1000; del.2[i,]<-c(i,(acold-aopt2),aopt2); next}
cmat=cmatrcd.mae(trt.N,col.N,theta,t(descold))
aopt2=sum(diag(ginv(cmat)))
del.2[i,]<-c(i,(acold-aopt2),aopt2)
descold[i,]=temp
}
del.2<-del.2[order(del.2[,3]),]
delbest=t(del.2[1,])
if(delbest[1]<=col.N) {descold[delbest[1],]=rev(descold[delbest[1],]); cdel=delbest[2]; acold=delbest[3]} else {cdel=0}
#print(delbest[1]<=col.N)
#desg<-graph(t(descold))
#plot(desg,main=paste(aopt-acold,sep=" / "))
#print(del.2)
#print(cdel)
#cat("\n", aopt-acold,"\n")
#cdel<-aopt-acold
#print(cdel)
#print(c(111,irep,acold,aopt0-acold,cdel,111))
}
#desg<-graph(t(descold))
#plot(desg,main=paste(aopt-acold,sep=" / "))
#print(del.2)
#print(cdel)
#cat("\n", aopt-acold,"\n")
#"============================================================="
next.it<- if (irep==1) {desbest.1=t(descold)} else {desbest.1=rbind(desbest.1,t(descold))}
aoptbest.1[irep,]=c(irep,acold)
}
best=aoptbest.1[order(aoptbest.1[,2]),]
nb=best[1,1]
Ascore<-best[1,2]
Aoptde<- desbest.1[c((nb-1)*2+1,nb*2),]
tkmessageBox(title="Search completed",message=paste("Search completed",sep=""))
cnames=paste0("Ary",1:col.N)
dimnames(Aoptde)=list(c("Dye 1:", "Dye 2:"),cnames)
Aopt_sum2<-list("v"=trt.N,"b"=col.N,theta=theta,nrep=nrep,itr.cvrgval=itr.cvrgval, "OptdesF"=Aoptde,"Optcrtsv" =Ascore)
return(Aopt_sum2)
}#End of SubSubsection 2.1.2 (Aoptrcd.maeA function) construction of A-optimal row-column design using array exchange algorithm
#End of Subsection 2.1 (Function for construction of A-optimal row-column design)
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