https://github.com/cran/aster
Tip revision: ed74c053fb02861447cf88cdf466b4ba46e657dc authored by Charles J. Geyer on 10 March 2017, 23:18:08 UTC
version 0.9
version 0.9
Tip revision: ed74c05
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))
}