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##' Create a univariate Gauss-Hermite quadrature rule
##'
##' This version of Gauss-Hermite quadrature provides the node
##' positions and weights for a scalar integral of a function
##' multiplied by the standard normal density.
GHrule <- function (ord, asMatrix=TRUE) {
stopifnot(length(ord) == 1,
(ord <- as.integer(ord)) >= 0L,
ord < 101L)
if (ord == 0L) {
if (asMatrix) return(matrix(0, nrow=0L, ncol=3L))
stop ("combination of ord==0 and asMatrix==TRUE not implemented")
}
## fgq_rules comes from sysdata.rda:
## result of
## library("fastGHQuad")
## rescale <- function(x, scale.weights=TRUE, scale.roots=TRUE) {
## within(x, {
## if (scale.weights) w <- w/sum(w)
## if (scale.roots) x <- x*sqrt(2)
## })
## }
## fgqRules <- lapply(1:100, function(n) setNames(rescale(gaussHermiteData(n)), c("z", "w")))
## ## However, this shows small differences !!
## all.equal(lme4:::fgq_rules, fgqRules, tol=0)
fr <- as.data.frame(fgq_rules[[ord]])
rownames(fr) <- NULL
fr$ldnorm <- dnorm(fr$z, log=TRUE)
if (asMatrix) as.matrix(fr) else fr
}
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