Nothing
################## update Loading ##############################################################
GwMH_LA_MYE <- function(y, mu = 0, ome, la, psx, gammal_sq, thd, const, prior, alas) {
# y=Y;ome=OME;la=LA;psx=PSX;mu=0;thd=THD
Q <- const$Q
J <- const$J
N <- const$N
K <- const$K
Jp <- const$Jp
Nmis <- const$Nmis
a_gamma <- prior$a_gaml_sq
b_gamma <- prior$b_gaml_sq
Pmean <- prior$m_LA
Sigla <- prior$s_LA
sub_sl <- const$sub_sl
len_sl <- const$len_sl
sub_ul <- const$sub_ul
len_ul <- const$len_ul
taul_sq <- gammal_sq
a_gams <- prior$a_gams
b_gams <- prior$b_gams
temp <- y - mu - la %*% ome # NY*N
S <- temp %*% t(temp) # NY*NY
for (j in 1:J) {
# j=1 for specified loadings
subs <- sub_sl[j, ]
len <- len_sl[j]
if (len > 0)
{
# yj<-(y[j,]-mst[j,])-matrix(la[j,(!subs)],nrow=1)%*%matrix(ome[(!subs),],ncol=N)
yj <- y[j, ] - matrix(la[j, (!subs)], nrow = 1) %*% matrix(ome[(!subs), ], ncol = N)
yj <- as.vector(yj) # vector
if (len == 1) {
omesub <- matrix(ome[subs, ], nrow = 1)
} else {
omesub <- ome[subs, ]
}
PSiginv <- diag(len) * Sigla
# diag(PSiginv)<-rep(Sigly,len) Pmean<-PLA[j,subs]
vtmp <- chol2inv(chol(tcrossprod(omesub)/psx[j, j] + PSiginv))
mtmp <- (omesub %*% yj/psx[j, j] + PSiginv %*% rep(Pmean,len))
la[j, subs] <- mvrnorm(1, vtmp %*% mtmp, Sigma = vtmp)
} # end len>0
# subs<-(Q[j,]==-1) len<-sum(subs) for specified loadings
subs <- sub_ul[j, ]
len <- len_ul[j]
if (len > 0) {
# yj<-(y[j,]-mst[j,])-matrix(la[j,(!subs)],nrow=1)%*%matrix(ome[(!subs),],ncol=N)
yj <- y[j, ] - matrix(la[j, (!subs)], nrow = 1) %*% matrix(ome[(!subs), ], ncol = N)
yj <- as.vector(yj) # vector
Cadj <- pmax((la[j, subs])^2, 10^(-6))
mu_p <- pmin(sqrt(gammal_sq[j, subs]/Cadj), 10^12)
taul_sq[j, subs] <- 1/rinvgauss1(len, mean = mu_p, dispersion = 1/gammal_sq[j, subs])
if(alas) gammal_sq[j, subs] <- rgamma(len, shape = a_gamma + 1, rate = b_gamma + taul_sq[j, subs]/2)
if (len == 1) {
omesub <- matrix(ome[subs, ], nrow = 1)
invD_tau <- 1/taul_sq[j, subs]
} else {
omesub <- ome[subs, ]
invD_tau <- diag(1/taul_sq[j, subs])
}
vtmp <- chol2inv(chol(tcrossprod(omesub)/psx[j, j] + invD_tau))
mtmp <- (omesub %*% yj/psx[j, j])
la[j, subs] <- mvrnorm(1, vtmp %*% mtmp, Sigma = vtmp)
tmp <- t(la[j, subs]) %*% invD_tau %*% la[j, subs]
psx[j, j] <- 1/rgamma(1, shape = a_gams + (N + len)/2 - 1, rate = b_gams + (S[j, j] +
tmp)/2)
} else {
psx[j, j] <- 1/rgamma(1, shape = a_gams + (N - 1)/2, rate = b_gams + (S[j, j])/2)
} # end len>0
} # end of J
if(!alas)
gammal_sq[Q==-1]<- rgamma(1, shape=a_gamma+sum(Q==-1), rate=b_gamma + sum(taul_sq)/2)
if (Nmis > 0 || Jp > 0) {
# yst<-la[pind,]%*%ome+mst[pind,]
ysta <- la %*% ome
spsxa <- sqrt(diag(psx))
ysa <- matrix(rnorm(N * J), J, N) + ysta/spsxa
ysa <- ysa/apply(ysa, 1, sd)
if (Jp > 0) {
td1 <- thd
mnoc <- const$mnoc
cand_std <- const$cand_std
pind <- const$cati
inf <- const$inf
zind <- const$zind
# sel <-matrix(0,Jp,mnoc-1)
for (m in 2:mnoc) {
# m<-3 td1[,m]<-rtnorm(J,mean=td0[,m],sd=cand.std,lower=td1[,m-1],upper=td0[,m+1])
tmp <- rnorm(Jp, mean = thd[, m], sd = cand_std)
# sel[,m-1]<-(tmp>td1[,m-1]&tmp<=td0[,m+1]) td1[,m]<-tmp*sel[,m-1]+td0[,m]*(1-sel[,m-1])
sel <- (tmp > td1[, m - 1] & tmp <= thd[, m + 1])
td1[, m] <- tmp * sel + thd[, m] * (1 - sel)
# tmp0<-pnorm((td0[,m+1]-td0[,m])/cand_std)-pnorm((td1[,m-1]-td0[,m])/cand_std) tmp0[tmp0<=0]<-1
# tmp1<-pnorm((td1[,m+1]-td1[,m])/cand_std)-pnorm((td0[,m-1]-td1[,m])/cand_std) tmp1[tmp1<=0]<-1
# ptd0[,m-1]<-log(tmp0) ptd1[,m-1]<-log(tmp1)
}
tmp00 <- matrix(thd[1:Jp + (zind - 1) * Jp], Jp, N)
tmp01 <- matrix(thd[1:Jp + (zind) * Jp], Jp, N)
tmp10 <- matrix(td1[1:Jp + (zind - 1) * Jp], Jp, N)
tmp11 <- matrix(td1[1:Jp + (zind) * Jp], Jp, N)
# # yst<-la[pind,]%*%ome+mst[pind,] yst<-la[pind,]%*%ome spsx<-sqrt(diag(psx)[pind])
yst <- ysta[pind, ]
spsx <- spsxa[pind]
tmp1 <- log(pnorm((tmp11 - yst)/spsx) - pnorm((tmp10 - yst)/spsx))
tmp1[tmp1 < (-inf)] <- -inf
tmp0 <- log(pnorm((tmp01 - yst)/spsx) - pnorm((tmp00 - yst)/spsx))
tmp0[tmp0 < (-inf)] <- -inf
# acc<-exp(rowSums(tmp1-tmp0)+rowSums(ptd0-ptd1))
acc <- exp(rowSums(tmp1 - tmp0, na.rm = T))
accind <- (acc > runif(Jp))
thd[accind, ] <- td1[accind, ]
# accrate<-mean(accind)
tmp00 <- matrix(thd[1:Jp + (zind - 1) * Jp], Jp, N)
tmp01 <- matrix(thd[1:Jp + (zind) * Jp], Jp, N)
# ys<-matrix(rnorm(N*Jp),Jp,N)+yst/spsx # sdy<-apply(ys,1,sd) # ys<-ys/sdy #using with
# #ys[j,]<-rnorm(N,tmp,sqrt(convar[j]))# ys<-ys/apply(ys,1,sd) # ys <- t(scale(t(ys), center = F,
# scale = apply(ys, 1, sd, na.rm = T)))
ys <- ysa[pind, ]
acc1 <- ((ys > tmp00) & (ys <= tmp01))
ys <- ys * acc1 + (1 - acc1) * y[pind, ]
# accr<-c(mean(accind),mean(acc1),mean(sel),mean(ptd0),mean(ptd1))
accr <- c(mean(accind), mean(acc1, na.rm = T))
out <- list(la = la, gammal_sq = gammal_sq, ys = ys, thd = thd, accr = accr, psx = psx,
ysm = ysa)
} else {
out <- list(la = la, gammal_sq = gammal_sq, psx = psx, ysm = ysa)
}
} else {
out <- list(la = la, gammal_sq = gammal_sq, psx = psx)
} #end Nmis || Jp
return(out)
}
################## end of update Loading ########################################################
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