Nothing
ce.sim4beta.MeanBIC <-
function(N, data, h, L0, L, M, Melite, eps, a){
v <- var(data[, 1])
if (N == 0){
# seql <- c(1, L)
# LL.full <- loglikMeanNormal(seql, data, h)
# BIC.value <- -2*LL.full + (N + 2)* 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.MeanNormal(1, (L + 1), data, h, v)[[1]]
BIC.full <- BIC.MeanNormal(LL.full, 0, L)
return(list(loci = c(1, (L + 1)), BIC.Val = BIC.full, LogLike = LL.full))
} else {
# ########################Parameter initialization######################################################
# new.para <- array(1, dim = c(2, 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, betarand, L0, L, M)
# ch <- t(apply(ch, 1, sort))
#
# LL.full <- apply(ch, 1, loglikMeanNormal, data, h)
# BIC.val <- -2*LL.full + (N + 2)* log(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)]
#
# newpar.n <- array(0, dim = c(2, N))
# newpar.n[1, ] <- apply(as.matrix(melitesmpl[, (2 :(N + 1))]), 2, mean)
# newpar.n[2, ] <- apply(as.matrix(melitesmpl[, (2 :(N + 1))]), 2, var)
#
# newpar.new <- array(0, dim = c(2, N))
# newpar.new[1, ] <- apply(newpar.n, 2, fun.alpha, L0, L)
# newpar.new[2, ] <- apply(newpar.n, 2, fun.beta, L0, L)
# new.para <- a * newpar.new + (1 - a) * new.para
#
# 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]))
########################Parameter initialization######################################################
new.para <- array(1, dim = c(2, 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, betarand, L0, L, M)
ch <- t(apply(ch, 1, sort))
# LL.full <- apply(ch, 1, loglikMeanNormal, data, h)
# BIC.val <- -2*LL.full + (N + 2)* log(L)
LL.full <- apply(ch, 1, llhood.MeanNormal, data, v, h)
BIC.val <- apply(as.data.frame(LL.full), 1, BIC.MeanNormal, 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)]
newpar.n <- array(0, dim = c(2, N))
newpar.n[1, ] <- apply(as.matrix(melitesmpl[, (2 :(N + 1))]), 2, mean)
newpar.n[2, ] <- apply(as.matrix(melitesmpl[, (2 :(N + 1))]), 2, var)
newpar.new <- array(0, dim = c(2, N))
newpar.new[1, ] <- apply(newpar.n, 2, fun.alpha, L0, L)
newpar.new[2, ] <- apply(newpar.n, 2, fun.beta, L0, L)
new.para <- a * newpar.new + (1 - a) * new.para
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]))
}
}
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