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
quickle.R
### implements (7) of the design doc
quickle <- function(alphanu, bee, 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)) {
origin <- obj$origin
} else {
stopifnot(is.matrix(origin))
stopifnot(is.numeric(origin))
stopifnot(is.finite(origin))
stopifnot(dim(origin) == dim(obj$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)
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, 2))
stopifnot(is.vector(alphanu))
stopifnot(is.numeric(alphanu))
stopifnot(is.finite(alphanu))
if (length(alphanu) != nfix + length(nrand))
stop("alphanu wrong length")
stopifnot(is.vector(bee))
stopifnot(is.numeric(bee))
stopifnot(is.finite(bee))
if (length(bee) != sum(nrand))
stop("bee wrong length")
idx <- seq(along = alphanu)
is.alpha <- idx <= nfix
is.nu <- nfix < idx
alpha <- alphanu[is.alpha]
nu <- alphanu[is.nu]
dee <- rep(nu, times = nrand)
if (any(nu < 0))
return(list(value = Inf, alpha = alpha, bee = bee, nu = nu))
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 <- seq(along = mout$gradient)
is.alpha <- idx <= nfix
is.bee <- nfix < idx
if (missing(origin))
mymlogl <- function(bee)
mlogl(c(alpha, bee), obj$pred, obj$fam, y, obj$root,
modmat, deriv = 2, famlist = obj$famlist)
else
mymlogl <- function(bee)
mlogl(c(alpha, bee), obj$pred, obj$fam, y, obj$root,
modmat, deriv = 2, famlist = obj$famlist, origin = origin)
objfun <- function(bee) {
### note: despite documentation of the mlogl function, it actually
### works to have modmat a matrix rather than a 3-way array
mout <- mymlogl(bee)
val <- mout$value + sum(bee^2 / dee) / 2
grad <- mout$gradient[is.bee] + bee / dee
hess <- mout$hessian
hess <- hess[is.bee, , drop = FALSE]
hess <- hess[ , is.bee, drop = FALSE]
### see note on help page for diag !!!
hess <- hess + diag(1 / dee, nrow = length(dee))
return(list(value = val, gradient = grad, hessian = hess))
}
tout <- trust(objfun, bee, rinit = 1, rmax = 10, iterlim = 1000)
stopifnot(tout$converged)
bee <- tout$argument
mout <- mlogl(c(alpha, bee), obj$pred, obj$fam, y, obj$root, modmat,
deriv = 2, famlist = obj$famlist, origin = origin)
a <- sqrt(dee)
bigh <- zwz * outer(a, a) + diag(length(a))
bigh.chol <- chol(bigh)
val <- mout$value + sum(bee^2 / dee) / 2 + sum(log(diag(bigh.chol)))
if (deriv == 0)
return(list(value = val, alpha = alpha, bee = bee, nu = nu))
pa <- mout$gradient[is.alpha]
pb <- mout$gradient[is.bee] + bee / dee
bigh.inv <- chol2inv(bigh.chol)
idx <- rep(seq(along = nu), times = nrand)
pn <- rep(NaN, length(nu))
for (k in seq(along = nu)) {
eek <- as.numeric(idx == k)
pn[k] <- sum(bigh.inv * zwz * outer(a, eek / a)) / 2 -
sum(bee^2 / dee^2 * eek) / 2
}
if (deriv == 1)
return(list(value = val, gradient = c(pa, pn),
alpha = alpha, bee = bee, nu = nu))
foo <- mout$hessian[is.alpha, , drop = FALSE]
paa <- foo[ , is.alpha, drop = FALSE]
pab <- foo[ , is.bee, drop = FALSE]
foo <- mout$hessian[is.bee, , drop = FALSE]
pbb <- foo[ , is.bee, drop = FALSE] + diag(1 / dee, nrow = length(dee))
pan <- matrix(0, length(alpha), length(nu))
pbn <- matrix(NaN, length(bee), length(nu))
pnn <- matrix(NaN, length(nu), length(nu))
bigh.inverse <- chol2inv(bigh.chol)
for (k in seq(along = nu)) {
eek <- as.numeric(idx == k)
pbn[ , k] <- (- bee * eek / dee^2)
fook <- zwz * outer(a, eek / a)
fook <- bigh.inverse %*% fook
for (m in seq(along = nu)) {
eem <- as.numeric(idx == m)
foom <- zwz * outer(a, eem / a)
foom <- bigh.inverse %*% foom
pnn[k, m] <- sum(bee^2 * eek * eem / dee^3) -
sum(fook * t(foom)) / 2
}
}
pbb.inv <- chol2inv(chol(pbb))
poo <- rbind(cbind(paa, pan), cbind(t(pan), pnn))
pob <- rbind(pab, t(pbn))
hess <- poo - pob %*% pbb.inv %*% t(pob)
return(list(value = val, gradient = c(pa, pn), hessian = hess,
alpha = alpha, bee = bee, nu = nu, pbb.inv = pbb.inv,
pba = t(pab), pbn = pbn))
}