Nothing
################## update Loading ##############################################################
Gibbs_LA_IYE <- function(y, mu, 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 + mst
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) {
pind <- const$cati
# inf <- const$inf
zind <- const$zind
ys <- ysa[pind, ]
acc <- ((ys > 0) ==(zind>1))
ys <- ys * acc + (1 - acc) * y[pind, ]
# accr<-c(mean(accind),mean(acc1),mean(sel),mean(ptd0),mean(ptd1))
accr <- c( mean(acc, 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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