https://github.com/cran/aster
Tip revision: ddf27b47107eeef427b837ef899cfd8219184832 authored by Charles J. Geyer on 16 July 2015, 00:00:00 UTC
version 0.8-31
version 0.8-31
Tip revision: ddf27b4
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))
}