https://github.com/cran/aster
Tip revision: aa47935123bfca8a22cbc8345d658d0c1713a289 authored by Charles J. Geyer on 14 December 2023, 15:20:02 UTC
version 1.1-3
version 1.1-3
Tip revision: aa47935
mlogl-cond.Rout.save
R version 3.6.0 (2019-04-26) -- "Planting of a Tree"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
> library(aster)
Loading required package: trust
> library(numDeriv)
>
> # needed because of the change in R function "sample" in R-devel
> suppressWarnings(RNGversion("3.5.2"))
>
> set.seed(42)
>
> nind <- 25
> nnode <- 5
> ncoef <- nnode + 1
>
> famlist <- fam.default()
> fam <- c(1, 1, 2, 3, 3)
> pred <- c(0, 1, 1, 2, 3)
>
> modmat <- array(0, c(nind, nnode, ncoef))
> modmat[ , , 1] <- 1
> for (i in 2:nnode)
+ modmat[ , i, i] <- 1
> modmat[ , , ncoef] <- rnorm(nind * nnode)
>
> beta <- rnorm(ncoef) / 10
>
> theta <- matrix(modmat, ncol = ncoef) %*% beta
> theta <- matrix(theta, ncol = nnode)
>
> root <- sample(1:3, nind * nnode, replace = TRUE)
> root <- matrix(root, nind, nnode)
>
> x <- raster(theta, pred, fam, root)
>
> out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "conditional")
>
> my.value <- 0
> for (j in 1:nnode) {
+ ifam <- fam[j]
+ k <- pred[j]
+ if (k > 0)
+ xpred <- x[ , k]
+ else
+ xpred <- root[ , j]
+ for (i in 1:nind)
+ my.value <- my.value -
+ sum(x[i, j] * theta[i, j] -
+ xpred[i] * famfun(famlist[[ifam]], 0, theta[i, j]))
+ }
> all.equal(out$value, my.value)
[1] TRUE
>
> foo <- function(beta) {
+ stopifnot(is.numeric(beta))
+ stopifnot(is.finite(beta))
+ mlogl(beta, pred, fam, x, root, modmat, type = "conditional")$value
+ }
>
> g <- grad(foo, beta)
> all.equal(g, out$gradient)
[1] TRUE
>
> h <- hessian(foo, beta)
> all.equal(h, out$hessian)
[1] TRUE
>
> ##########
>
> objfun <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 1,
+ type = "conditional")
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ return(result)
+ }
> out1 <- nlm(objfun, beta, fscale = nind)
>
> ##########
>
> objfun <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "conditional")
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ attr(result, "hessian") <- out$hessian
+ return(result)
+ }
> out <- nlm(objfun, beta, fscale = nind)
> all.equal(out1$minimum, out$minimum)
[1] TRUE
> all.equal(out1$estimate, out$estimate, tolerance = 1e-4)
[1] TRUE
>
> ########## expected Fisher information ##########
>
> aster:::setfam(fam.default())
>
> fout <- .C(aster:::C_aster_fish_cond,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ ncoef = as.integer(ncoef),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ beta = as.double(out$estimate),
+ root = as.double(root),
+ x = as.double(x),
+ modmat = as.double(modmat),
+ fish = matrix(as.double(0), ncoef, ncoef))
>
> mout <- mlogl(out$estimate, pred, fam, x, root, modmat,
+ deriv = 2, type = "conditional")
>
> aster:::setfam(fam.default())
>
> theta <- .C(aster:::C_aster_mat_vec_mult,
+ nrow = as.integer(nind * nnode),
+ ncol = as.integer(ncoef),
+ a = as.double(modmat),
+ b = as.double(out$estimate),
+ c = matrix(as.double(0), nind, nnode))$c
> ctau <- .C(aster:::C_aster_theta2ctau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(theta),
+ ctau = matrix(as.double(0), nind, nnode))$ctau
> tau <- .C(aster:::C_aster_ctau2tau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ root = as.double(root),
+ ctau = as.double(ctau),
+ tau = matrix(as.double(0), nind, nnode))$tau
> psiDoublePrime <- .C(aster:::C_aster_theta2whatsis,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ deriv = as.integer(2),
+ theta = as.double(theta),
+ result = matrix(as.double(0), nind, nnode))$result
>
> my.hessian <- theta * NaN
> my.fish <- theta * NaN
>
> for (i in 1:nind) {
+ for (j in 1:nnode) {
+ k <- pred[j]
+ if (k > 0) {
+ my.hessian[i, j] <- x[i, k] * psiDoublePrime[i, j]
+ my.fish[i, j] <- tau[i, k] * psiDoublePrime[i, j]
+ } else {
+ my.hessian[i, j] <- root[i, j] * psiDoublePrime[i, j]
+ my.fish[i, j] <- root[i, j] * psiDoublePrime[i, j]
+ }
+ }
+ }
>
> modmat <- matrix(as.double(modmat), ncol = ncoef)
> my.hessian <- as.numeric(my.hessian)
> my.fish <- as.numeric(my.fish)
> my.hessian <- t(modmat) %*% diag(my.hessian) %*% modmat
> my.fish <- t(modmat) %*% diag(my.fish) %*% modmat
>
> all.equal(my.hessian, mout$hessian)
[1] TRUE
> all.equal(my.fish, fout$fish)
[1] TRUE
>
>
> proc.time()
user system elapsed
0.277 0.012 0.281