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mainMGHD<-function(data=NULL, gpar0, G, n, label , eps, method ,nr=NULL) {
pcol=ncol(data)
if(!is.null(label)){
lc=apply(data[label==1,],2,mean)
# if(min(label)==0&max(label)==G){
for(i in 2:G){
lc=rbind(lc,apply(data[label==i,],2,mean))
}
#}
# else{
# print("G needs to be equal to max(label)")
# # for(i in 2:max(label)){
# # lc=rbind(lc,apply(data[label==i,],2,mean))
# # }
# # for(i in (max(label)+1):G){
# # lc=rbind(lc,apply(data[label==i,],2,mean))
# # }
# }
z = combinewk(weights=matrix(1/G,nrow=nrow(data),ncol=G), label=label)
if (is.null(gpar0)) gpar = rgparGH(data=data, g=G, w=z,l=lc)
else gpar = gpar0
}
else{
if (is.null(gpar0)) gpar = try(igpar(data=data, g=G, method=method,nr=nr))
else gpar = gpar0}
loglik = numeric(n)
for (i in 1:3) {
gpar = try(EMgrstepGH(data=data, gpar=gpar, v=1, label = label)) ###parameter estimation
loglik[i] = llikGH(data, gpar)}
while ( ( getall(loglik[1:i]) > eps) & (i < (n) ) ) {
i = i+1
gpar = try(EMgrstepGH(data=data, gpar=gpar, v=1, label = label)) ###parameter estimation
loglik[i] = llikGH(data, gpar) ##likelyhood
}
if(i<n){loglik=loglik[-(i+1:n)]}
BIC=2*loglik[i]-log(nrow(data))*((G-1)+G*(2*pcol+2+pcol*(pcol-1)/2))
z=weightsGH(data=data, gpar= gpar)
ICL=BIC+2*sum(log(apply(z,1,max)))
AIC=2*loglik[i]-2*((G-1)+G*(2*pcol+2+pcol*(pcol-1)/2))
AIC3=2*loglik[i]-3*((G-1)+G*(2*pcol+2+pcol*(pcol-1)/2))
val = list(loglik= loglik, gpar=gpar, z=z, map=MAPGH(data=data, gpar= gpar, label=label),BIC=BIC,ICL=ICL,AIC=AIC,AIC3=AIC3 )
return(val)
}
MGHD <- function(data=NULL, gpar0=NULL, G=2, max.iter=100, label =NULL , eps=1e-2, method="kmeans" ,scale=TRUE ,nr=10, modelSel="AIC") {
##Expexctation Maximization estimation of GHD
##data
## G n clusters
##n number of iterations
data=as.matrix(data)
if( scale==TRUE){
data=scale(as.matrix(data))}
pcol=ncol(data)
#if (nrow(data)<((G-1)+G*(2*pcol+2+pcol*(pcol-1)/2)))stop('G is too big, number of parameters > n')
if (is.null(data)) stop('data is null')
if (nrow(data) == 1) stop('nrow(data) is equal to 1')
if (any(is.na(data))) stop('No NAs allowed.')
if (is.null(G)) stop('G is NULL')
#if ( G < 1) stop('G is not a positive integer')
if ( max.iter < 1) stop('max.iter is not a positive integer')
if(modelSel=="BIC"){
bico=-Inf
t=length(G)
BIC=matrix(NA,t,1)
cont=0
for(b in 1:t){
mo=try(mainMGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps, label=label,method= method,nr=nr),silent = TRUE)
cont=cont+1
if(is.list(mo)){
bicn=mo$BIC
BIC[cont]=bicn}
else{bicn=-Inf
BIC[cont]=NA}
if(bicn>bico){
bico=bicn
sg=G[b]
model=mo
}
}
# val=list(BIC=BIC,model=model)
val=MixGHD(Index=BIC,AIC=model$AIC,AIC3=model$AIC3,BIC=model$BIC,ICL=model$ICL, map=model$map, gpar=model$gpar, loglik=model$loglik, z=model$z,method="MGHD",data=as.data.frame(data),scale=scale)
cat("The best model (BIC) for the range of components used is G = ", sg,".\nThe BIC for this model is ", bico,".",sep="")
return(val)}
else if(modelSel=="ICL"){
bico=-Inf
t=length(G)
ICL=matrix(NA,t,1)
cont=0
for(b in 1:t){
mo=try(mainMGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps, label=label,method= method,nr=nr),silent = TRUE)
cont=cont+1
if(is.list(mo)){
bicn=mo$ICL
ICL[cont]=bicn}
else{bicn=-Inf
ICL[cont]=NA}
if(bicn>bico){
bico=bicn
sg=G[b]
model=mo
}
}
# val=list(ICL=ICL,model=model)
val=MixGHD(Index=ICL,AIC=model$AIC,AIC3=model$AIC3,BIC=model$BIC,ICL=model$ICL, map=model$map, gpar=model$gpar, loglik=model$loglik, z=model$z,method="MGHD",data=as.data.frame(data),scale=scale)
cat("The best model (ICL) for the range of components used is G = ", sg,".\nThe ICL for this model is ", bico,".",sep="")
return(val)}
else if(modelSel=="AIC3"){
bico=-Inf
t=length(G)
AIC3=matrix(NA,t,1)
cont=0
for(b in 1:t){
mo=try(mainMGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps, label=label,method= method,nr=nr),silent = TRUE)
cont=cont+1
if(is.list(mo)){
bicn=mo$AIC3
AIC3[cont]=bicn}
else{bicn=-Inf
AIC3[cont]=NA}
if(bicn>bico){
bico=bicn
sg=G[b]
model=mo
}
}
# val=list(AIC3=AIC3,model=model)
val=MixGHD(Index=AIC3,AIC=model$AIC,AIC3=model$AIC3,BIC=model$BIC,ICL=model$ICL, map=model$map, gpar=model$gpar, loglik=model$loglik, z=model$z,method="MGHD",data=as.data.frame(data),scale=scale)
cat("The best model (AIC3) for the range of components used is G = ", sg,".\nThe AIC3 for this model is ", bico,".",sep="")
return(val)}
else {
bico=-Inf
t=length(G)
AIC=matrix(NA,t,1)
cont=0
for(b in 1:t){
mo=try(mainMGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps, label=label,method= method,nr=nr),silent = TRUE)
cont=cont+1
if(is.list(mo)){
bicn=mo$AIC
AIC[cont]=bicn}
else{bicn=-Inf
AIC[cont]=NA}
if(bicn>bico){
bico=bicn
sg=G[b]
model=mo
}
}
val=MixGHD(Index=AIC,AIC=model$AIC,AIC3=model$AIC3,BIC=model$BIC,ICL=model$ICL, map=model$map, gpar=model$gpar, loglik=model$loglik, z=model$z,method="MGHD",data=as.data.frame(data),scale=scale)
#val=list(index=AIC,model=model)
cat("The best model (AIC) for the range of components used is G = ", sg,".\nThe AIC for this model is ", bico,".",sep="")
return(val)}
}
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