https://github.com/cran/aster
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Tip revision: 61fc3cd89e7f6776279333b534652356c7059f89 authored by Charles J. Geyer on 13 June 2021, 03:40:32 UTC
version 1.1-2
Tip revision: 61fc3cd
is.zero.R
### implements test of (35) of the design doc

is.zero <- function(alphabeenu, fixed, random, obj, y, origin, zwz,
    tolerance = sqrt(.Machine$double.eps))
{
    stopifnot(inherits(obj, "aster"))
    if (missing(y)) {
        y <- obj$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(is.vector(alphabeenu))
    stopifnot(is.numeric(alphabeenu))
    stopifnot(is.finite(alphabeenu))
    if (length(alphabeenu) != nfix + sum(nrand) + length(nrand))
        stop("alphabeenu wrong length")

    idx <- seq(along = alphabeenu)
    is.alpha <- idx <= nfix
    is.bee <- nfix < idx & idx <= nfix + sum(nrand)
    is.nu <- nfix + sum(nrand) < idx
    alpha <- alphabeenu[is.alpha]
    bee <- alphabeenu[is.bee]
    nu <- alphabeenu[is.nu]
    dee <- rep(nu, times = nrand)

    if (all(nu > tolerance))
        return(rep(FALSE, length(nu)))
    if (any(nu < (- tolerance)))
        stop("apparently negative components of nu, impossible")
    nu[nu < tolerance] <- 0

    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$gradient)
    is.bee <- nfix < idx
    pb <- mout$gradient[is.bee]

    bigh <- sweep(zwz, 2, dee, "*") + diag(length(dee))
    bigh.inv <- solve(bigh)
    idx <- rep(seq(along = nu), times = nrand)
    pn <- rep(NaN, length(nu))
    for (k in seq(along = nu)) {
        eek <- as.numeric(idx == k)
        fook <- sweep(zwz, 2, eek, "*")
        pn[k] <- sum(t(bigh.inv) * fook) / 2
    }

    result <- rep(FALSE, length(nu))
    for (k in seq(along = nu))
        if (nu[k] == 0)
            result[k] <- pn[k] >= sum(pb[idx == k]^2) / 4
    return(result)
}

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