Nothing
ce.simnormal.MeanVar.BIC <-
function(N,data,h,L0,L,M,Melite,eps,a,b){
if (N==0){
# seql <- c(1, L)
# LL.full <- loglikMeanVarNormal(seql, data, h)
# BIC.value <- -2*LL.full + 2*(N + 1)* log(L)
# #AIC.val <- -2*LL.full + 4* (N + 1)
# return(list(locis = c(1, (L + 1)), BIC.Val = BIC.val, LogLike = LL.full))
# rm(LL.full, BIC.val)
#mBic.full <- mBIC(seql, data, 0, L, h)
# return(list(loci = c(1, (L + 1)), BIC.Val = BIC.value, LogLike = LL.full))
# rm(BIC.value, LL.full, seql)
LL.full <- loglik.MeanVarNormal(1, (L + 1), data, h)[[1]]
BIC.full <- BIC.MeanVarNormal(LL.full, 0, L)
return(list(loci = c(1, (L + 1)), BIC.Val = BIC.full, LogLike = LL.full))
} else {
########################Parameter initialization######################################################
new_para<-rbind(rep(L0+(L-L0)/2,N),rep(sqrt(L-L0)^2/12,N))
######################################################################################################
# llVal <- c()
# bic <- c()
k <- 0
repeat
{
k<-k+1
ch<-array(0,dim=c(M,N+2))
ch[,1]<-c(1)
ch[,N+2]<-c(L+1)
ch[,(2:(N+1))]<-apply(new_para,2,normrand,L0,L,M)
ch<-t(apply(ch,1,sort))
# LL.full <- apply(ch, 1, loglikMeanVarNormal, data, h)
# BIC.val <- -2*LL.full + 2*(N + 1)* log(L)
LL.full <- apply(ch, 1, llhood.MeanVarNormal, data, h)
BIC.val <- apply(as.data.frame(LL.full), 1, BIC.MeanVarNormal, N, L)
ch <- cbind(ch, LL.full, BIC.val)
ch <- ch[order(ch[, (N + 4)], decreasing = FALSE), ]
melitesmpl<-ch[1:Melite,]
# llVal[k] <- melitesmpl[1, (N + 3)]
# bic[k] <- melitesmpl[1, (N + 4)]
new_par_n<-array(0,dim=c(2,N))
new_par_n[1,]<-apply(as.matrix(melitesmpl[,(2:(N+1))]),2,mean)
new_par_n[2,]<-apply(as.matrix(melitesmpl[,(2:(N+1))]),2,sd)
new_para[1,] <- a*new_par_n[1,] + (1-a)*new_para[1,]
new_para[2,] <- b*new_par_n[2,] + (1-b)*new_para[2,]
mad<-apply(as.matrix(melitesmpl[,(2:(N+1))]),2,mad)
if(max(mad)<=eps){break}
}
# return(list(loci=ch[1,(1:(N+2))], BIC.Val = bic[k], LogLike = llVal[k]))
return(list(loci=ch[1,(1:(N+2))], BIC.Val = melitesmpl[1, (N + 4)][[1]], LogLike = melitesmpl[1, (N + 3)][[1]]))
}
}
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