R Under development (unstable) (2024-05-20 r86569 ucrt) -- "Unsuffered Consequences"
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> 
> library("mvtnorm")
> 
> set.seed(290875)
> 
> if (require("numDeriv", quietly = TRUE)) {
+ 
+ 
+ chk <- function(...) stopifnot(isTRUE(all.equal(...)))
+ 
+ J <- 5
+ 
+ chks <- function(dg, tol = .Machine$double.eps^(1 / 4)) {
+ 
+     prm <- runif(J * (J + c(-1, 1)[dg + 1L]) / 2)
+     L <- ltMatrices(prm, diag = dg)
+     a <- matrix(-2, nrow = J, ncol = 1)
+     b <- matrix( 2, nrow = J, ncol = 1)
+     M <- 10000L
+ 
+     l <- function(x) {
+         x <- ltMatrices(x, diag = dg)
+         lpmvnorm(a, b, chol = x, M = M, seed = 29)
+     }
+ 
+     s <- function(x)
+         slpmvnorm(a, b, chol = x, M = M, seed = 29)
+ 
+     rl <- l(L)
+     rs <- s(L)
+     chk(rl, rs$logLik)
+     if (dg) {
+         chk(rs$chol, ltMatrices(grad(l, unclass(L)), diag = dg), tol = tol)
+     } else {
+         chk(c(Lower_tri(rs$chol)), grad(l, unclass(L)), tol = tol)
+     }
+ 
+     l <- function(x) {
+         x <- ltMatrices(x, diag = dg)
+         lpmvnorm(a, b, invchol = x, M = M, seed = 29)
+     }
+ 
+     s <- function(x)
+         slpmvnorm(a, b, invchol = x, M = M, seed = 29)
+ 
+     rl <- l(L)
+     rs <- s(L)
+     chk(rl, rs$logLik)
+     if (dg) {
+         chk(rs$invchol, ltMatrices(grad(l, unclass(L)), diag = dg), tol = tol)
+     } else {
+         chk(c(Lower_tri(rs$invchol)), grad(l, unclass(L)), tol = tol)
+     }
+ 
+     l <- function(x)
+         lpmvnorm(a, b, mean = x, chol = L, M = M, seed = 29)
+     s <- function(x)
+         slpmvnorm(a, b, mean = x, chol = L, M = M, seed = 29)
+ 
+     x <- numeric(J)
+     rl <- l(x)
+     rs <- s(x)
+     chk(rl, rs$logLik)
+     chk(grad(l, x), c(rs$mean), tol = tol)
+     x <- 1:J
+     rl <- l(x)
+     rs <- s(x)
+     chk(rl, rs$logLik)
+     chk(grad(l, x), c(rs$mean), tol = tol)
+ }
+ 
+ chks(TRUE)
+ chks(FALSE)
+ 
+ ### check scores for conditional distributions
+ 
+ .cmvnorm <- function(invchol, which_given, given) {
+     L <- invchol
+     J <- dim(L)[2L]
+     tmp <- matrix(0, ncol = ncol(given), nrow = J - length(which_given))
+     center <- Mult(L, rbind(given, tmp))
+     center <- center[-which_given,,drop = FALSE]
+     L <- L[,-which_given]
+     return(list(center = center, invchol = L))
+ }
+ 
+ J <- (cJ <- 4) + (dJ <- 4)
+ N <- 3
+ M <- 30
+ ltM <- function(x) ltMatrices(x, diag = FALSE, byrow = TRUE)
+ ltD <- function(x) ltMatrices(x, diag = TRUE, byrow = TRUE)
+ prm <- matrix(runif(J * (J - 1) / 2 * N), ncol = N)
+ L <- ltM(prm)
+ 
+ obs <- matrix(rnorm(J * N), ncol = N)
+ lwr <- -abs(obs)
+ upr <- abs(obs)
+ 
+ w <- matrix(runif((dJ - 1) * M), ncol = M)
+ 
+ ### without scaling, diag(L) == 1
+ ## score wrt L
+ j <- 1:cJ
+ ll <- function(x) {
+     L <- ltM(x)
+     cd <- .cmvnorm(invchol = L, which = j, given = obs[j,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- ltM(matrix(grad(ll, unclass(L)), ncol = N))
+ diagonals(a) <- 0
+ 
+ cd <- .cmvnorm(invchol = L, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+              invchol = cd$invchol, w = w)
+ 
+ chk(as.array(a[,-j]), as.array(s$invchol), check.attributes = FALSE)
+ 
+ ## score wrt obs
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = L, which = j, given = x)
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, obs[1:cJ,,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = L, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ tmp0 <- solve(cd$invchol, s$mean, transpose = TRUE)
+ aL <- as.array(L)[-(1:cJ), 1:cJ,]
+ lst <- tmp0[rep(1:dJ, cJ),,drop = FALSE]
+ dobs <- -margin.table(aL * array(lst, dim = dim(aL)), 2:3)
+ 
+ chk(a, dobs, check.attributes = FALSE)
+ 
+ ## score wrt lower
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = L, which = j, given = obs[1:cJ,,drop = FALSE])
+     lpmvnorm(x, upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, lwr[-(1:cJ),,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = L, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ chk(a, s$lower, check.attributes = FALSE)
+ 
+ ## score wrt upper
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = L, which = j, given = obs[1:cJ,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], x, center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, upr[-(1:cJ),,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = L, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ chk(a, s$upper, check.attributes = FALSE)
+ 
+ ### after scaling
+ LD <- invcholD(L)
+ 
+ ## score wrt LD (!)
+ # use center
+ ll <- function(x) {
+     LD <- ltD(x)
+     cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w, logLik = TRUE)
+ }
+ 
+ a1 <- ltD(matrix(grad(ll, unclass(LD)), ncol = N))
+ # use mean
+ ll <- function(x) {
+     LD <- ltD(x)
+     cd <- cond_mvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], mean = cd$mean, 
+             invchol = cd$invchol, w = w, logLik = FALSE)
+ }
+ 
+ a2 <- ltMatrices(matrix(grad(function(...) sum(ll(...)), unclass(LD)), ncol = N), 
+                 diag = TRUE, byrow = TRUE)
+ 
+ chk(a1, a2)
+ 
+ cd <- cond_mvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s1 <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], mean = cd$mean, 
+              invchol = cd$invchol, w = w)
+ 
+ cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s2 <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+              invchol = cd$invchol, w = w)
+ 
+ ### needs to be FALSE
+ # all.equal(s1, s2)
+ 
+ chk(a1[,-j], s2$invchol, check.attributes = FALSE)
+ 
+ # score wrt obs
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = LD, which = j, given = x)
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, obs[1:cJ,,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, 
+         w = w)
+ 
+ tmp0 <- solve(cd$invchol, s$mean, transpose = TRUE)
+ aL <- as.array(LD)[-(1:cJ), 1:cJ,]
+ lst <- tmp0[rep(1:dJ, cJ),,drop = FALSE]
+ dobs <- -margin.table(aL * array(lst, dim = dim(aL)), 2:3)
+ 
+ chk(a, dobs, check.attributes = FALSE)
+ 
+ ## score wrt lower
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = LD, which = j, given = obs[1:cJ,,drop = FALSE])
+     lpmvnorm(x, upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, lwr[-(1:cJ),,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ chk(a, s$lower, check.attributes = FALSE)
+ 
+ ## score wrt upper
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = LD, which = j, given = obs[1:cJ,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], x, center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, upr[-(1:cJ),, drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ chk(a, s$upper, check.attributes = FALSE)
+ 
+ ### one-dimensional conditional distribution
+ 
+ J <- (cJ <- 4) + (dJ <- 1)
+ prm <- matrix(runif(J * (J - 1) / 2 * N), ncol = N)
+ L <- ltM(prm)
+ 
+ obs <- matrix(rnorm(J * N), ncol = N)
+ lwr <- -abs(obs)
+ upr <- abs(obs)
+ 
+ w <- matrix(runif((dJ - 1) * M), ncol = M)
+ 
+ ### without scaling, diag(L) == 1
+ ## score wrt L not needed (a constant)
+ j <- 1:cJ
+ 
+ ## score wrt obs
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = L, which = j, given = x)
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, obs[1:cJ,,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = L, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ tmp0 <- solve(cd$invchol, s$mean, transpose = TRUE)
+ aL <- as.array(L)[-(1:cJ), 1:cJ,]
+ lst <- tmp0[rep(1:dJ, cJ),,drop = FALSE]
+ dobs <- -aL * array(lst, dim = dim(aL))
+ 
+ chk(a, dobs, check.attributes = FALSE)
+ 
+ ## score wrt lower
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = L, which = j, given = obs[1:cJ,,drop = FALSE])
+     lpmvnorm(x, upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, lwr[-(1:cJ),,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = L, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ chk(a, s$lower, check.attributes = FALSE)
+ 
+ ## score wrt upper
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = L, which = j, given = obs[1:cJ,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], x, center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, upr[-(1:cJ),,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = L, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ chk(a, s$upper, check.attributes = FALSE)
+ 
+ ### after scaling
+ LD <- invcholD(L)
+ 
+ ## score wrt LD (!)
+ # use center
+ ll <- function(x) {
+     LD <- ltD(x)
+     cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w, logLik = TRUE)
+ }
+ 
+ a1 <- ltD(matrix(grad(ll, unclass(LD)), ncol = N))
+ # use mean
+ ll <- function(x) {
+     LD <- ltD(x)
+     cd <- cond_mvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], mean = cd$mean, 
+             invchol = cd$invchol, w = w, logLik = FALSE)
+ }
+ 
+ a2 <- ltMatrices(matrix(grad(function(...) sum(ll(...)), unclass(LD)), ncol = N), 
+                 diag = TRUE, byrow = TRUE)
+ 
+ chk(a1, a2)
+ 
+ cd <- cond_mvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s1 <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], mean = cd$mean, 
+              invchol = cd$invchol, w = w)
+ 
+ cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s2 <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+              invchol = cd$invchol, w = w)
+ 
+ ### needs to be FALSE
+ # all.equal(s1, s2)
+ 
+ chk(a1[,-j], s2$invchol, check.attributes = FALSE)
+ 
+ # score wrt obs
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = LD, which = j, given = x)
+     lpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, obs[1:cJ,,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, 
+         w = w)
+ 
+ tmp0 <- solve(cd$invchol, s$mean, transpose = TRUE)
+ aL <- as.array(LD)[-(1:cJ), 1:cJ,]
+ lst <- tmp0[rep(1:dJ, cJ),,drop = FALSE]
+ dobs <- -(aL * array(lst, dim = dim(aL)))
+ 
+ chk(a, dobs, check.attributes = FALSE)
+ 
+ ## score wrt lower
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = LD, which = j, given = obs[1:cJ,,drop = FALSE])
+     lpmvnorm(x, upr[-j,,drop = FALSE], center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, lwr[-(1:cJ),,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ chk(a, s$lower, check.attributes = FALSE)
+ 
+ ## score wrt upper
+ ll <- function(x) {
+     cd <- .cmvnorm(invchol = LD, which = j, given = obs[1:cJ,,drop = FALSE])
+     lpmvnorm(lwr[-j,,drop = FALSE], x, center = cd$center, 
+             invchol = cd$invchol, w = w)
+ }
+ a <- matrix(grad(ll, upr[-(1:cJ),,drop = FALSE]), ncol = N)
+ 
+ cd <- .cmvnorm(invchol = LD, which = j, given = obs[j,,drop = FALSE])
+ s <- slpmvnorm(lwr[-j,,drop = FALSE], upr[-j,,drop = FALSE], center = cd$center, invchol = cd$invchol, w = w)
+ 
+ chk(a, s$upper, check.attributes = FALSE)
+ 
+ ### check scores for extremely small likelihoods
+ ### use independence model as ground truth
+ J <- 10
+ 
+ yl <- matrix(-as.double(rep(1, J) / 2000), ncol = 1)
+ yr <- abs(yl)
+ 
+ w <- matrix(.5, nrow = J - 1, ncol = 1)
+ 
+ ## L = diag(J)
+ L <- ltMatrices(rep(1, J * (J + 1) / 2), diag = TRUE)
+ L[] <- 0
+ diagonals(L) <- 1
+ 
+ lpmvnorm(lower = yl, upper = yr, invchol = L, w = w)
+ 
+ s <- slpmvnorm(lower = yl, upper = yr, invchol = L, w = w)[c("logLik", "invchol")]
+ 
+ chk(c((dnorm(yr) * yr - dnorm(yl) * yl ) / (pnorm(yr) - pnorm(yl))),
+     c(diagonals(s$invchol)))
+ }
> 
> proc.time()
   user  system elapsed 
   9.10    0.09    9.20