https://github.com/cran/aster
Tip revision: caf80bbb933639f6a5334d03486d97e18fb725ec authored by Charles J. Geyer on 29 March 2013, 00:00:00 UTC
version 0.8-21
version 0.8-21
Tip revision: caf80bb
pickle3.R
checkargs3part1 <- function(fixed, random, obj, y, origin)
{
stopifnot(inherits(obj, "aster"))
if (! missing(y)) {
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))
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 (nrow(foo) != nrow(fixed))
stop("fixed and random effects model matrices with different nrow")
if (! all(is.finite(foo)))
stop("some random effects model matrix not all finite")
}
}
checkargs3part2 <- function(alpha, cee, sigma, nfix, nrand)
{
if (! is.numeric(alpha))
stop("vector of fixed effects not numeric")
if (! all(is.finite(alpha)))
stop("vector of fixed effects not all finite")
if (length(alpha) != nfix)
stop("vector of fixed effects wrong length")
if (! is.numeric(cee))
stop("vector of rescaled random effects not numeric")
if (! all(is.finite(cee)))
stop("vector of rescaled random effects not all finite")
if (length(cee) != sum(nrand))
stop("vector of rescaled random effects wrong length")
if (! is.numeric(sigma))
stop("vector of square roots of variance components not numeric")
if (! all(is.finite(sigma)))
stop("vector of square roots of variance components not all finite")
if (length(sigma) != length(nrand))
stop("vector of square roots of variance components wrong length")
}
pickleHelper <- function(alpha, cee, sigma, fixed, random, obj, y,
origin, zwz, deriv = 0)
{
nfix <- ncol(fixed)
if (! is.list(random)) random <- list(random)
nrand <- sapply(random, ncol)
if (missing(y)) y <- obj$x
a <- as.vector(rep(sigma, times = nrand))
bee <- a * cee
modmat <- fixed
for (i in seq(along = random))
modmat <- cbind(modmat, random[[i]], deparse.level = 0)
mout <- mlogl(c(alpha, bee), obj$pred, obj$fam, y, obj$root,
modmat, deriv = 2, famlist = obj$famlist, origin = origin)
bigh <- zwz * outer(a, a) + diag(length(a))
bigh.chol <- chol(bigh)
val <- mout$value + sum(cee^2) / 2 + sum(log(diag(bigh.chol)))
if (deriv == 0) return(list(value = val))
is.alpha <- seq(along = mout$gradient) <= nfix
is.c <- seq(along = mout$gradient) > nfix
pa <- mout$gradient[is.alpha]
### Z^T (y - mu^*)
zymoo <- mout$gradient[is.c]
pc <- zymoo * a + cee
bigh.inv <- chol2inv(bigh.chol)
idx <- rep(seq(along = sigma), times = nrand)
pt <- rep(NaN, length(sigma))
for (k in seq(along = sigma)) {
eek <- as.numeric(idx == k)
pt[k] <- sum(bigh.inv * zwz * outer(a, eek)) + sum(zymoo * eek * cee)
}
grad <- list(pa = pa, pc = pc, pt = pt)
if (deriv == 1) return(list(value = val, gradient = grad))
### M^T W^* M and M^T W^* Z
foo <- mout$hessian[is.alpha, , drop = FALSE]
paa <- foo[ , is.alpha, drop = FALSE]
mwz <- foo[ , is.c, drop = FALSE]
### M^T W^* Z A
pac <- sweep(mwz, 2, a, "*")
### Z^T W^* Z
foo <- mout$hessian[is.c, , drop = FALSE]
zwz.star <- foo[ , is.c, drop = FALSE]
pcc <- zwz.star * outer(a, a) + diag(length(a))
pat <- matrix(NaN, length(alpha), length(sigma))
pct <- matrix(NaN, length(cee), length(sigma))
ptt <- matrix(NaN, length(sigma), length(sigma))
for (k in seq(along = sigma)) {
eek <- as.numeric(idx == k)
pat[ , k] <- as.numeric(mwz %*% cbind(eek * cee))
pct[ , k] <- as.numeric(zwz.star %*% cbind(eek * cee)) * a - zymoo * eek
for (m in seq(along = sigma))
if (k <= m) {
eem <- as.numeric(idx == m)
qux <- sum(zwz.star * outer(eek * cee, eem * cee))
quux <- sum(bigh.inv * zwz * outer(eek, eem))
fook <- zwz * outer(eek, a)
foom <- zwz * outer(eem, a)
fook <- fook + t(fook)
quuux <- bigh.inv %*% fook %*% bigh.inv
quuux <- sum(quuux * foom)
ptt[k, m] <- qux + quux - quuux
ptt[m, k] <- qux + quux - quuux
}
}
hess <- list(paa = paa, pac = pac, pat = pat, pcc = pcc,
pct = pct, ptt = ptt)
return(list(value = val, gradient = grad, hessian = hess))
}
pickle3 <- function(alphaceesigma, fixed, random, obj, y, origin,
zwz, deriv = 0)
{
checkargs3part1(fixed, random, obj, y, origin)
nfix <- ncol(fixed)
if (! is.list(random)) random <- list(random)
nrand <- sapply(random, ncol)
stopifnot(is.vector(alphaceesigma))
if(length(alphaceesigma) != nfix + sum(nrand) + length(nrand))
stop("length(alphaceesigma) != number of fixed effects + number of random effects + number of variance components")
idx <- seq(along = alphaceesigma)
alpha <- alphaceesigma[idx <= nfix]
cee <- alphaceesigma[idx > nfix & idx <= nfix + sum(nrand)]
sigma <- alphaceesigma[idx > nfix + sum(nrand)]
checkargs3part2(alpha, cee, sigma, nfix, nrand)
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))
pout <- pickleHelper(alpha, cee, sigma, fixed, random, obj, y,
origin, zwz, deriv)
if (deriv == 0)
return(pout)
grad <- c(pout$gradient$pa, pout$gradient$pc, pout$gradient$pt)
if (deriv == 1)
return(list(value = pout$value, gradient = grad))
hess <- rbind(cbind(pout$hessian$paa, pout$hessian$pac, pout$hessian$pat),
cbind(t(pout$hessian$pac), pout$hessian$pcc, pout$hessian$pct),
cbind(t(pout$hessian$pat), t(pout$hessian$pct), pout$hessian$ptt))
return(list(value = pout$value, gradient = grad, hessian = hess))
}