MCMCsampleRW = function(niter,Y,Z,Intercept,TT,dd,nn,MuInt,VarInt,VarZ,
accZ,accInt,tuneZ,tuneInt,A,B,gList)
{
#using MDS of dis-similarity matrix of observed network at time tt
# Z = lapply(1:TT,function(tt){
# g = graph.adjacency(Y[[tt]]);
# ss = shortest.paths(g);
# ss[ss > 4] = 4;
# Z0 = cmdscale(ss,k = dd);
# dimnames(Z0)[[1]] = dimnames(Y[[tt]])[[1]];
# return(Z0)})
##Centering matrix
# C = (diag(nn[1]) - (1/nn[1]) * array(1, dim = c(nn[1],nn[1])))
##projection matrix
# Z00 = C %*% Z[[1]]
ZFinal = list()
InterceptFinal = rep(NA,niter)
Likelihood = rep(NA,niter)
ZVarFinal = list()
# llikOld = array(NA,dim=c(nn[1],TT))
llikOld = list()
length(llikOld) = TT
for(iter in 1:niter){
#update Z
llikOld = lapply(1:TT,function(x){
sapply(1:nn[x],function(y) likelihoodi(y,dd,nn[x],Y[[x]],Z[[x]],Intercept))
})
Zupdt = ZupdateRW(Y=Y,Z=Z,TT=TT,Intercept=Intercept,dd=dd,
var=VarZ,llikOld=llikOld,acc=accZ,tune=tuneZ)
Z = Zupdt$Z
accZ = Zupdt$acc
llikAll = sum(sapply(1:TT,function(x){
FullLogLik(Y[[x]],Z[[x]],Intercept,nn[x],dd)}))
Intupdt = InterceptupdateRW(Intercept=Intercept,llikAll=llikAll,
MuBeta=MuInt,VarBeta=VarInt,tune=tuneInt,
acc=accInt,Y=Y,Z=Z,TT=TT,
nn=nn,dd=dd)
Intercept = Intupdt$Intercept
accInt = Intupdt$acc
llikAll = Intupdt$llikAll
VarZ = SigmaUpdateRW(A=A,B=B,Z=Z,nn=nn,dd=dd,TT=TT,gList=gList)
#STORE UPDATES
InterceptFinal[iter] = Intercept
ZFinal[[iter]] = Z
Likelihood[iter] = llikAll
ZVarFinal[[iter]] = VarZ
print(iter)
}
draws = list(Z=ZFinal,Intercept=InterceptFinal,Likelihood =Likelihood ,VarZ =ZVarFinal )
accZ = lapply(1:TT,function(x)accZ[[x]]/niter)
accInt = accInt/niter
acc = list(accZ=accZ,accInt=accInt)
return(list(draws=draws,acc=acc))
}
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