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# mvnorm
### allow more than one distribution
mvnorm <- function(mean, chol, invchol) {
# mvnorm chol invchol
if (missing(chol) && missing(invchol))
chol <- as.chol(ltMatrices(1, diag = TRUE))
stopifnot(xor(missing(chol), missing(invchol)))
if (!missing(chol)) {
if (!is.ltMatrices(chol))
chol <- as.ltMatrices(chol)
scale <- as.chol(chol)
}
if (!missing(invchol)) {
if (!is.ltMatrices(invchol))
invchol <- as.ltMatrices(invchol)
scale <- as.invchol(invchol)
}
ret <- list(scale = scale)
# mvnorm mean
if (!missing(mean)) {
stopifnot(is.numeric(mean))
stopifnot(NROW(mean) == dim(scale)[2L])
if (!is.matrix(mean)) {
mean <- matrix(mean, nrow = NROW(mean))
rownames(mean) <- names(mean)
}
nm <- dimnames(scale)[[2L]]
if (is.null(rownames(mean)))
rownames(mean) <- nm
if (!isTRUE(all.equal(rownames(mean), nm)))
stop("rownames of mean do not match")
nm <- dimnames(scale)[[1L]]
if (!is.null(nm) && dim(scale)[[2L]] == ncol(mean)) {
if (is.null(colnames(mean)))
colnames(mean) <- nm
if (!isTRUE(all.equal(colnames(mean), nm)))
stop("colnames of mean do not match")
}
ret$mean <- mean
}
class(ret) <- "mvnorm"
return(ret)
}
# mvnorm methods
names.mvnorm <- function(x)
dimnames(x$scale)[[2L]]
aperm.mvnorm <- function(a, perm, ...) {
ret <- list(scale = aperm(a$scale, perm = perm, ...))
if (!is.null(a$mean))
ret$mean <- a$mean[perm,,drop = FALSE]
class(ret) <- "mvnorm"
ret
}
# mvnorm simulate
simulate.mvnorm <- function(object, nsim = dim(object$scale)[1L], seed = NULL,
standardize = FALSE, as.data.frame = FALSE, ...) {
J <- dim(object$scale)[2L]
N <- dim(object$scale)[1L]
if (N > 1)
stopifnot(nsim == N)
if (standardize) {
if (is.chol(object$scale)) {
object$scale <- standardize(chol = object$scale)
} else {
object$scale <- standardize(invchol = object$scale)
}
}
Z <- matrix(rnorm(nsim * J), nrow = J)
if (is.chol(object$scale)) {
Y <- Mult(object$scale, Z)
} else {
Y <- solve(object$scale, Z)
}
ret <- Y
if (!is.null(object$mean))
ret <- ret + c(object$mean)
rownames(ret) <- dimnames(object$scale)[[2L]]
if (!as.data.frame)
return(ret)
return(as.data.frame(t(ret)))
}
# mvnorm margDist
margDist <- function(object, which, ...)
UseMethod("margDist")
margDist.mvnorm <- function(object, which, ...) {
if (is.chol(object$scale)) {
ret <- list(scale = as.chol(marg_mvnorm(chol = object$scale,
which = which)$chol))
} else {
ret <- list(scale = as.invchol(marg_mvnorm(invchol = object$scale,
which = which)$invchol))
}
if (!is.null(object$mean))
ret$mean <- object$mean[which,,drop = FALSE]
class(ret) <- "mvnorm"
return(ret)
}
# mvnorm condDist
condDist <- function(object, which_given, given, ...)
UseMethod("condDist")
condDist.mvnorm <- function(object, which_given = 1L, given, ...) {
if (is.chol(object$scale)) {
ret <- cond_mvnorm(chol = object$scale, which_given = which_given,
given = given, ...)
ret$scale <- as.chol(ret$chol)
ret$chol <- NULL
} else {
ret <- cond_mvnorm(invchol = object$scale, which_given = which_given,
given = given, ...)
ret$invchol <- as.chol(ret$invchol)
ret$invchol <- NULL
}
if (!is.null(object$mean)) {
if (is.character(which_given))
which_given <- match(which_given, dimnames(object$scale)[[2L]])
if (ncol(object$mean) > 1L && ncol(ret$mean) > 1)
stop("dimensions do not match")
if (ncol(object$mean) == 1L && ncol(ret$mean) > 1L) {
ret$mean <- object$mean[-which_given,,drop = TRUE] + ret$mean
} else {
ret$mean <- object$mean[-which_given,,drop = FALSE] + c(ret$mean)
}
}
class(ret) <- "mvnorm"
return(ret)
}
# mvnorm logLik
logLik.mvnorm <- function(object, obs, lower, upper, standardize = FALSE,
...) {
# argchecks
args <- c(object, list(...))
nargs <- missing(obs) + missing(lower) + missing(upper)
stopifnot(nargs < 3L)
nmobs <- NULL
if (!missing(obs)) {
if (!is.null(obs)) {
stopifnot(is.matrix(obs))
nmobs <- rownames(obs)
}
}
nmlower <- nmupper <- nmlu <- NULL
if (!missing(lower)) {
if (!is.null(lower)) {
stopifnot(is.matrix(lower))
nmlu <- nmlower <- rownames(lower)
}
}
if (!missing(upper)) {
if (!is.null(lower)) {
stopifnot(is.matrix(upper))
nmupper <- rownames(upper)
if (!missing(lower)) {
stopifnot(isTRUE(all.equal(nmlower, nmupper)))
} else {
nmlu <- nmupper
}
}
}
nm <- c(nmobs, nmlu)
no <- names(object)
stopifnot(nm %in% no)
perm <- NULL
if (!isTRUE(all.equal(nm, no)))
perm <- c(nm, no[!no %in% nm])
if (!missing(obs)) args$obs <- obs
if (!missing(lower)) args$lower <- lower
if (!missing(upper)) args$upper <- upper
if (is.chol(object$scale)) {
# logLik chol
names(args)[names(args) == "scale"] <- "chol"
if (standardize)
args$chol <- standardize(chol = args$chol)
if (!is.null(perm)) {
args$chol <- aperm(as.chol(args$chol), perm = perm)
if (length(nm) < length(no))
args$chol <- marg_mvnorm(chol = args$chol, which = nm)$chol
args$mean <- args$mean[nm,,drop = FALSE]
}
return(do.call("ldpmvnorm", args))
}
# logLik invchol
names(args)[names(args) == "scale"] <- "invchol"
if (standardize)
args$invchol <- standardize(invchol = args$invchol)
if (!is.null(perm)) {
args$invchol <- aperm(as.invchol(args$invchol), perm = perm)
if (length(nm) < length(no))
args$invchol <- marg_mvnorm(invchol = args$invchol,
which = nm)$invchol
args$mean <- args$mean[nm,,drop = FALSE]
}
return(do.call("ldpmvnorm", args))
}
# mvnorm lLgrad
lLgrad <- function(object, ...)
UseMethod("lLgrad")
lLgrad.mvnorm <- function(object, obs, lower, upper, standardize = FALSE,
...) {
# argchecks
args <- c(object, list(...))
nargs <- missing(obs) + missing(lower) + missing(upper)
stopifnot(nargs < 3L)
nmobs <- NULL
if (!missing(obs)) {
if (!is.null(obs)) {
stopifnot(is.matrix(obs))
nmobs <- rownames(obs)
}
}
nmlower <- nmupper <- nmlu <- NULL
if (!missing(lower)) {
if (!is.null(lower)) {
stopifnot(is.matrix(lower))
nmlu <- nmlower <- rownames(lower)
}
}
if (!missing(upper)) {
if (!is.null(lower)) {
stopifnot(is.matrix(upper))
nmupper <- rownames(upper)
if (!missing(lower)) {
stopifnot(isTRUE(all.equal(nmlower, nmupper)))
} else {
nmlu <- nmupper
}
}
}
nm <- c(nmobs, nmlu)
no <- names(object)
stopifnot(nm %in% no)
perm <- NULL
if (!isTRUE(all.equal(nm, no)))
perm <- c(nm, no[!no %in% nm])
if (!missing(obs)) args$obs <- obs
if (!missing(lower)) args$lower <- lower
if (!missing(upper)) args$upper <- upper
if (is.chol(object$scale)) {
# lLgrad chol
names(args)[names(args) == "scale"] <- "chol"
sc <- args$chol
if (standardize)
args$chol <- sc <- standardize(chol = args$chol)
if (!is.null(perm)) {
if (!attr(args$chol, "diag")) {
diagonals(args$chol) <- 1
sc <- args$chol
}
args$chol <- pc <- aperm(as.chol(args$chol), perm = perm)
if (length(nm) < length(no))
args$chol <- marg_mvnorm(chol = args$chol, which = nm)$chol
args$mean <- args$mean[nm,,drop = FALSE]
}
ret <- do.call("sldpmvnorm", args)
# lLgrad mean
### sldmvnorm returns mean score as -obs
if (is.null(ret$mean)) ret$mean <- - ret$obs
# lLgrad marginalisation
om <- length(no) - length(nm)
if (om > 0) {
am <- matrix(0, nrow = om, ncol = ncol(ret$mean))
rownames(am) <- no[!no %in% nm]
ret$mean <- rbind(ret$mean, am)
Jo <- dim(object$scale)[[2L]]
pJ <- dim(args$invchol)[[2L]]
am <- matrix(0, nrow = Jo * (Jo + 1) / 2 - pJ * (pJ + 1) / 2,
ncol = dim(ret$invchol)[1L])
byrow_orig <- attr(ret$chol, "byrow")
ret$chol <- ltMatrices(ret$chol, byrow = TRUE)
### rbind only works for byrow = TRUE
ret$chol <- ltMatrices(rbind(unclass(ret$chol), am),
byrow = TRUE,
diag = TRUE,
names = perm)
ret$chol <- ltMatrices(ret$chol, byrow = byrow_orig)
}
# lLgrad deperma
if (!is.null(perm))
ret$chol <- deperma(chol = sc, permuted_chol = pc,
perm = match(perm, no),
score_schol = ret$chol)
# lLgrad destandarized
if (standardize)
ret$chol <- destandardize(chol = object$scale,
score_schol = ret$chol)
# lLgrad diagonals
if (!attr(sc, "diag"))
ret$chol <- ltMatrices(Lower_tri(ret$chol, diag = FALSE),
diag = FALSE,
byrow = attr(ret$chol, "byrow"),
names = dimnames(ret$chol)[[2L]])
# lLgrad return
ret$scale <- ret$chol
ret$chol <- NULL
ret$mean <- ret$mean[no,,drop = FALSE]
return(ret)
}
# lLgrad invchol
names(args)[names(args) == "scale"] <- "invchol"
si <- args$invchol
if (standardize)
args$invchol <- si <- standardize(invchol = args$invchol)
if (!is.null(perm)) {
if (!attr(args$invchol, "diag")) {
diagonals(args$invchol) <- 1
si <- args$invchol
}
args$invchol <- pi <- aperm(as.invchol(args$invchol), perm = perm)
if (length(nm) < length(no))
args$invchol <- marg_mvnorm(invchol = args$invchol,
which = nm)$invchol
args$mean <- args$mean[nm,,drop = FALSE]
}
ret <- do.call("sldpmvnorm", args)
### sldmvnorm returns mean score as -obs
if (is.null(ret$mean)) ret$mean <- - ret$obs
om <- length(no) - length(nm)
if (om > 0) {
am <- matrix(0, nrow = om, ncol = ncol(ret$mean))
rownames(am) <- no[!no %in% nm]
ret$mean <- rbind(ret$mean, am)
Jo <- dim(object$scale)[[2L]]
pJ <- dim(args$invchol)[[2L]]
am <- matrix(0, nrow = Jo * (Jo + 1) / 2 - pJ * (pJ + 1) / 2,
ncol = dim(ret$invchol)[1L])
byrow_orig <- attr(ret$invchol, "byrow")
ret$invchol <- ltMatrices(ret$invchol, byrow = TRUE)
### rbind only works for byrow = TRUE
ret$invchol <- ltMatrices(rbind(unclass(ret$invchol), am),
byrow = TRUE,
diag = TRUE,
names = perm)
ret$invchol <- ltMatrices(ret$invchol, byrow = byrow_orig)
}
if (!is.null(perm))
ret$invchol <- deperma(invchol = si, permuted_invchol = pi,
perm = match(perm, no),
score_schol = -vectrick(pi, ret$invchol))
if (standardize)
ret$invchol <- destandardize(invchol = object$scale,
score_schol = -vectrick(si, ret$invchol))
if (!attr(si, "diag"))
ret$invchol <- ltMatrices(Lower_tri(ret$invchol, diag = FALSE),
diag = FALSE,
byrow = attr(ret$invchol, "byrow"),
names = dimnames(ret$invchol)[[2L]])
ret$scale <- ret$invchol
ret$invchol <- NULL
ret$mean <- ret$mean[no,,drop = FALSE]
return(ret)
}
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