https://github.com/cran/aster
Tip revision: b4fd42b2256e32a18f4b6661e3558f602df8d775 authored by Charles J. Geyer on 30 June 2013, 00:00:00 UTC
version 0.8-27
version 0.8-27
Tip revision: b4fd42b
newpickle.R
### implements either (8) or (41) of the design doc
### if argument zwz is supplied, does (8), otherwise does (41)
newpickle <- function(alphaceesigma, fixed, random, obj, y, origin, zwz,
deriv = 0)
{
stopifnot(inherits(obj, "aster"))
if (missing(y)) {
y <- obj$x
} else {
stopifnot(is.matrix(y))
stopifnot(is.numeric(y))
stopifnot(is.finite(y))
stopifnot(dim(y) == dim(obj$x))
}
if (! missing(origin)) {
stopifnot(is.matrix(origin))
stopifnot(is.numeric(origin))
stopifnot(is.finite(origin))
stopifnot(dim(origin) == dim(obj$origin))
}
stopifnot(is.matrix(fixed))
stopifnot(is.numeric(fixed))
stopifnot(is.finite(fixed))
nfix <- ncol(fixed)
stopifnot(is.matrix(random) | is.list(random))
if (! is.list(random))
random <- list(random)
for (i in seq(along = random)) {
foo <- random[[i]]
if (! is.matrix(foo))
stop("random not matrix or list of matrices")
if (! is.numeric(foo))
stop("random not numeric matrix or list of such")
if (! all(is.finite(foo)))
stop("some random effects model matrix not all finite")
if (nrow(foo) != nrow(fixed))
stop("fixed and random effects model matrices with different nrow")
}
nrand <- sapply(random, ncol)
if (! missing(zwz)) {
stopifnot(is.matrix(zwz))
stopifnot(is.numeric(zwz))
stopifnot(is.finite(zwz))
if (any(dim(zwz) != sum(nrand)))
stop("zwz not square matrix with dimension = number of random effects")
}
stopifnot(length(deriv) == 1)
stopifnot(deriv %in% c(0, 1))
if (missing(zwz) & deriv != 0)
stop("derivatives cannot be done unless zwz is supplied")
stopifnot(is.vector(alphaceesigma))
stopifnot(is.numeric(alphaceesigma))
stopifnot(is.finite(alphaceesigma))
if (length(alphaceesigma) != nfix + sum(nrand) + length(nrand))
stop("alphaceesigma wrong length")
idx <- seq(along = alphaceesigma)
is.alpha <- idx <= nfix
is.cee <- nfix < idx & idx <= nfix + sum(nrand)
is.sigma <- nfix + sum(nrand) < idx
alpha <- alphaceesigma[is.alpha]
cee <- alphaceesigma[is.cee]
sigma <- alphaceesigma[is.sigma]
a <- as.vector(rep(sigma, times = nrand))
bee <- a * cee
modmat <- cbind(fixed, Reduce(cbind, random))
### note: despite documentation of the mlogl function, it actually
### works to have modmat a matrix rather than a 3-way array
mout <- mlogl(c(alpha, bee), obj$pred, obj$fam, y, obj$root, modmat,
deriv = 2, famlist = obj$famlist, origin = origin)
idx.too <- seq(along = mout$gradient)
is.alpha.too <- idx.too <= nfix
is.cee.too <- nfix < idx.too
if (missing(zwz)) {
zwz <- mout$hessian
zwz <- zwz[is.cee.too, ]
zwz <- zwz[ , is.cee.too]
}
bigh <- zwz * outer(a, a) + diag(length(a))
bigh.chol <- chol(bigh)
val <- mout$value + sum(cee^2) / 2 + sum(log(diag(bigh.chol)))
if (deriv == 0)
return(list(value = val))
pa <- mout$gradient[is.alpha.too]
### Z^T (y - mu^*)
zymoo <- mout$gradient[is.cee.too]
pc <- zymoo * a + cee
bigh.inv <- chol2inv(bigh.chol)
idx <- rep(seq(along = sigma), times = nrand)
ps <- rep(NaN, length(sigma))
for (k in seq(along = sigma)) {
eek <- as.numeric(idx == k)
ps[k] <- sum(bigh.inv * zwz * outer(a, eek)) + sum(zymoo * eek * cee)
}
return(list(value = val, gradient = c(pa, pc, ps)))
}