####Function to run the sampler#####
##SPECIFYING PRIORS######
#########################
##if priors = NULL => uses randomly generated priors
##else: priors is a list of following objects:
##MuBeta:= prior mean for betas & intercepts;
##SigmaBeta:= prior variance for betas & intercept;
##MuAlpha:= prior mean for alpha;
##SigmaAlpha:= prior variance for alpha;
##MuZ; VarZ;
##PriorA; PriorB
##TUNING PARAMETERS#######
##########################
##if tune = NULL => uses auto tuning
##else: tune is a list of following objects:
##tuneAlpha = 0.9
##tuneBeta = array, dim=c(PP,KK)
##tuneInt = vec, len = KK
##tuneZ = list( vec(len = nn[x]])) length of list = KK
##############################################################
#library(MASS)
HLSMrandomEF = function(Y,edgeCov = NULL, receiverCov = NULL,senderCov =NULL,FullX = NULL, initialVals = NULL, priors = NULL, tune = NULL,
tuneIn = TRUE, TT = NULL,dd, niter,intervention)
{
#X and Y are provided as list.
if(class(Y) != 'list'){
if(dim(Y)[2] != 4){stop('Invalid data structure type')} }
if(class(Y) == 'list' & class(Y[[1]]) != 'matrix' & class(Y[[1]]) != 'data.frame'){stop('Invalid data structure type')}
if(class(Y) == 'list'){
KK = length(Y)
if(dim(Y[[1]])[1] == dim(Y[[1]])[2]){
nn =sapply(1:length(Y),function(x) nrow(Y[[x]])) }
if(dim(Y[[1]])[1] != dim(Y[[1]])[2] & dim(Y[[1]])[2] == 4){
nn = sapply(1:length(Y), function(x)length(unique(c(Y[[x]]$Receiver,Y[[x]]$Sender))))
nodenames = lapply(1:length(Y), function(x) unique(c(Y[[x]]$Receiver,Y[[x]]$Sender)))
} }
if(class(Y) != 'list'){
if(dim(Y)[2] == 4){
nid = unique(Y$id)
KK = length(nid)
nn = rep(0,KK)
df.list = list()
nodenames = list()
for(k in 1:KK){
df.sm = Y[which(Y$id == nid[k],),]
nn[k] = length(unique(c(df.sm$Receiver,df.sm$Sender)))
nodenames[[k]] = unique(c(df.sm$Receiver, df.sm$Sender))
df.list[[k]] = array(0, dim = c(nn[k],nn[k]))
dimnames(df.list[[k]])[[1]] = dimnames(df.list[[k]])[[2]] = nodenames[[k]]
for(i in 1:dim(df.sm)[1]){
df.list[[k]][df.sm$Sender[i],df.sm$Receiver[i]] = df.sm$Outcome[i] #assume undirected graph and missing items are zeros
}
}
Y = df.list
}}
##prepare covariates#####
#########################
if(!is.null(FullX) & !is.null(edgeCov) &!is.null(receiverCov) & !is.null(senderCov))(stop('FullX cannot be used when nodal or edge covariates are provided'))
if(is.null(FullX) & is.null(edgeCov) & is.null(receiverCov) & is.null(senderCov)){
X = lapply(1:KK,function(x) array(0, dim = c(nn[x],nn[x],1)))
}
if(is.null(FullX)){
if(!is.null(edgeCov) | !is.null(senderCov)| !is.null(receiverCov)){
if(!is.null(edgeCov)){
if(class(edgeCov) != 'data.frame'){
stop('edgeCov must be of class data.frame')}
X1 = getEdgeCov(edgeCov, nn,nodenames)
}else(X1 =NULL)
if(!is.null(senderCov)){
if(class(senderCov) != 'data.frame'){
stop('senderCov must be of class data.frame')}
X2 = getSenderCov(senderCov, nn,nodenames)
}else(X2 = NULL)
if(!is.null(receiverCov)){
if(class(receiverCov) != 'data.frame'){
stop('receiverCov must be of class data.frame')}
X3 = getReceiverCov(receiverCov, nn,nodenames)
}else(X3 = NULL)
X = lapply(1:KK, function(x){if(!is.null(X1)&!is.null(X2)&!is.null(X3)){
ncov = dim(X1[[x]])[3]+dim(X2[[x]])[3]+dim(X3[[x]])[3];
df = array(0, dim = c(nn[x],nn[x],ncov));
df[,,1:dim(X1[[x]])[3]] = X1[[x]];
df[,,(dim(X1[[x]])[3]+1):(dim(X1[[x]])[3]+dim(X2[[x]])[3])] = X2[[x]];
df[,,(dim(X1[[x]])[3]+dim(X2[[x]])[3]+1):(dim(X1[[x]])[3]+dim(X2[[x]])[3]+dim(X3[[x]])[3])] = X3[[x]] };
if(!is.null(X1)&!is.null(X2) & is.null(X3)){
ncov = dim(X1[[x]])[3]+dim(X2[[x]])[3];
df = array(0, dim = c(nn[x],nn[x],ncov));
df[,,1:dim(X1[[x]])[3]] = X1[[x]];
df[,,(dim(X1[[x]])[3]+1):(dim(X1[[x]])[3]+dim(X2[[x]])[3])] = X2[[x]]};
if(!is.null(X1)&!is.null(X3)&is.null(X2)){
ncov = dim(X1[[x]])[3]+dim(X3[[x]])[3];
df = array(0, dim = c(nn[x],nn[x],ncov));
df[,,1:dim(X1[[x]])[3]] = X1[[x]];
df[,,(dim(X1[[x]])[3]+1):(dim(X1[[x]])[3]+dim(X3[[x]])[3])] = X3[[x]]};
if(!is.null(X2)&!is.null(X3)&is.null(X1)){
ncov = dim(X2[[x]])[3]+dim(X3[[x]])[3];
df = array(0, dim = c(nn[x],nn[x],ncov));
df[,,1:dim(X2[[x]])[3]] = X2[[x]];
df[,,(dim(X2[[x]])[3]+1):(dim(X2[[x]])[3]+dim(X3[[x]])[3])] = X3[[x]]};
if(!is.null(X1)& is.null(X2)& is.null(X3)){
df = X1[[x]] };
if(is.null(X1)& !is.null(X2)& is.null(X3)){
df = X2[[x]] };
if(is.null(X1)& is.null(X2)& !is.null(X3)){
df = X3[[x]] };
return(df) } )
}
}
if(!is.null(FullX)) X = FullX
PP = dim(X[[1]])[3]
XX = unlist(X)
YY = unlist(Y)
YY[which(is.na(YY))] = 0
XX[which(is.na(XX))] = 0
#Priors
if(is.null(priors)){
MuBeta= rep(0,(PP+1))
VarBeta = rep(1,(PP+1))
MuAlpha=0
VarAlpha = 1
MuZ = c(0,0)
VarZ = c(20,20)
PriorA = 100
PriorB = 150
}else{
if(class(priors) != 'list')(stop("priors must be of class list, if not NULL"))
MuBeta = priors$MuBeta
VarBeta = priors$VarBeta
MuAlpha = priors$MuAlpha
VarAlpha = priors$VarAlpha
MuZ = priors$MuZ
VarZ = priors$VarZ
PriorA = priors$PriorA
PriorB = priors$PriorB
}
##starting values
if(is.null(initialVals)){
Z0 = array(0, dim = c(sum(nn),dd))
cc = 1
for(i in 1:KK){
cc1 = (cc-1)+nn[i]
ZZ = t(replicate(nn[i],rnorm(dd,0,sqrt(10))))
ZZ[1,]=c(1,0)
ZZ[2,2]=0
if(ZZ[2,1] < ZZ[1,1]){
ZZ[2,1] = -1*(ZZ[2,1]-ZZ[1,1])+1}
ZZ[3,2] = abs(ZZ[3,2])
Z0[cc:cc1,] = ZZ
cc = cc+nn[i]
}
Z0 = unlist(Z0)
beta0 = replicate(KK,rnorm(PP,0,1))
intercept0 = rnorm(KK, 0,1)
if(intervention == 1){ alpha0=rnorm(1, 0, 1) }
print("Starting Values Set")
}else{
if(class(initialVals)!= 'list')(stop("initialVals must be of class list, if not NULL"))
Z0 = initialVals$ZZ
beta0 = initialVals$beta
intercept0 = initialVals$intercept
if(intervention == 1){ alpha0 = initialVals$alpha}
}
if(intervention == 0){
alpha0 = 0
TT = rep(0, KK)
}
###tuning parameters#####
if(is.null(tune)){
a.number = 5
tuneAlpha = 0.9
tuneBeta = array(1,dim=c(PP,KK))
tuneInt = rep(0.2,KK)
tuneZ = lapply(1:KK,function(x) rep(1.2,nn[x]))
} else{
if(class(tune) != 'list')(stop("tune must be of class list, if not NULL"))
a.number = 1
tuneAlpha = tune$tuneAlpha
tuneBeta = tune$tuneBeta
tuneInt = tune$tuneInt
tuneZ = tune$tuneZ
}
###Tuning the Sampler####
do.again = 1
tuneX = 1
if(tuneIn == TRUE){
while(do.again ==1){
print('Tuning the Sampler')
for(counter in 1:a.number){
rslt = MCMCfunction(nn=nn,PP=PP,KK=KK,dd=dd,XX = XX,YY = YY,ZZ = Z0,TT = TT,beta = beta0 ,intercept = intercept0,
alpha = alpha0,MuAlpha = MuAlpha,SigmaAlpha = VarAlpha,MuBeta = MuBeta,SigmaBeta = VarBeta,MuZ = MuZ,
VarZ = VarZ,tuneBetaAll = tuneBeta, tuneInt = tuneInt, tuneAlpha = tuneAlpha,tuneZAll = unlist(tuneZ),
niter = 200,PriorA = PriorA, PriorB = PriorB, intervention = intervention)
tuneAlpha = adjust.my.tune(tuneAlpha, rslt$acc$alpha,1)
tuneZ = lapply(1:KK,function(x)adjust.my.tune(tuneZ[[x]], rslt$acc$Z[[x]], 2))
tuneBeta = array(sapply(1:KK,function(x)adjust.my.tune(tuneBeta[,x], rslt$acc$beta[,x],1)),dim = c(PP,KK))
tuneInt = sapply(1:KK,function(x)adjust.my.tune(tuneInt[x],rslt$acc$intercept[x], 1))
print(paste('TuneDone = ',tuneX))
tuneX = tuneX+1
}
extreme = lapply(1:KK,function(x)which.suck(rslt$acc$Z[[x]],2))
do.again = max(sapply(extreme, length)) > 5
}
print("Tuning is finished")
}
rslt = MCMCfunction(nn=nn,PP=PP,KK=KK,dd= dd,XX = XX,YY = YY,ZZ = Z0,TT = TT,beta = beta0 ,intercept = intercept0,
alpha = alpha0,MuAlpha = MuAlpha,SigmaAlpha = VarAlpha,MuBeta = MuBeta,SigmaBeta = VarBeta,MuZ = MuZ,
VarZ = VarZ,tuneBetaAll = tuneBeta, tuneInt = tuneInt, tuneAlpha = tuneAlpha,tuneZAll = unlist(tuneZ),
niter = niter,PriorA = PriorA, PriorB = PriorB,intervention = intervention)
rslt$call = match.call()
rslt$tune = list(tuneAlpha = tuneAlpha, tuneZ = tuneZ, tuneBeta = tuneBeta, tuneInt = tuneInt)
class(rslt) = 'HLSM'
rslt
}