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# hdi method for class stats::density, a list with elements
# x = the n coordinates of the points where the density is estimated.
# y = the estimated density values.
hdi.density <- function(object, credMass=0.95, allowSplit=FALSE, ...) {
checkCredMass(credMass)
sorted = sort( object$y , decreasing=TRUE )
heightIdx = min( which( cumsum( sorted) >= sum(object$y) * credMass ) )
height = sorted[heightIdx]
indices = which( object$y >= height )
# HDImass = sum( object$y[indices] ) / sum(object$y)
gaps <- which(diff(indices) > 1)
if(length(gaps) > 0 && !allowSplit) {
# In this case, return shortest 95% CrI
warning("The HDI is discontinuous but allowSplit = FALSE;
the result is a valid CrI but not HDI.")
cumul <- cumsum(object$y) / sum(object$y)
upp.poss <- low.poss <- which(cumul < 1 - credMass)
for (i in low.poss)
upp.poss[i] <- min(which(cumul > cumul[i] + credMass))
# all(cumul[upp.poss] - cumul[low.poss] > credMass) # check
width <- upp.poss - low.poss
best <- which(width == min(width)) # usually > 1 value due to ties
result <- c(lower = mean(object$x[low.poss[best]]),
upper = mean(object$x[upp.poss[best]]))
} else {
begs <- indices[c(1, gaps + 1)]
ends <- indices[c(gaps, length(indices))]
result <- cbind(begin = object$x[begs], end = object$x[ends])
if(!allowSplit) {
result <- as.vector(result)
names(result) <- c("lower", "upper")
}
}
attr(result, "credMass") <- credMass
attr(result, "height") <- height
return(result)
}
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