##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function(smat, alpha, beta, D, y, By, k )
{
# function to evaluate log marginal posterior of s and generate tausqtot
smat <- as.matrix(smat)
n <- length(y)
F <- ncol(D) + 1
logpostvect <- numeric()
tausqtot <- numeric()
sumalpha <- sum(alpha)
# change to use apply 09/29/09 MKC
genst <- function( s )
{
s0 <- 1-sum(s)
neweigennumer <- s[1] * D[,1]
if( F > 2)
for(j in 2:(F-1))
neweigennumer <- neweigennumer + s[j] * D[,j]
neweigendenom <- neweigennumer + s0
neweigen <- s0 * neweigennumer / neweigendenom
# corrected to (alpha - 1) 09/18/09
#logpostdensnumer <- sum( log(c(s0,s)) * alpha ) +
logpostdensnumer <- sum( log(c(s0,s)) * (alpha-1) ) +
sum( log(neweigen[ neweigen > 0 ]) ) / 2
whole <- sum( neweigen * By^2)
newbeta <- whole/2 + sum( c(s0,s) * beta )
newalpha <- (sumalpha + (n-k)/2 )
logpostdensdenom <- log( newbeta)* newalpha
#logpostvect[i] <- logpostdensnumer - logpostdensdenom
#tausqtot[i] <- rgamma(1, newalpha, newbeta)
c( logpostdensnumer - logpostdensdenom,
rgamma(1, newalpha, newbeta))
}
if( multicoreflag )
output <- t( parApply(cl, smat, 1, genst ) )
else
output <- t( apply( smat, 1, genst ) )
output
}
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