https://github.com/cran/aster
Tip revision: 7016e6b97f24943bdab11323884baf030f38260b authored by Charles J. Geyer on 06 July 2018, 07:20:08 UTC
version 1.0-2
version 1.0-2
Tip revision: 7016e6b
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))
}