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-unco.Rout.save
R version 3.6.0 (2019-04-26) -- "Planting of a Tree"
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Platform: x86_64-pc-linux-gnu (64-bit)
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>
> library(aster)
Loading required package: trust
> library(numDeriv)
>
> set.seed(42)
>
> # needed because of the change in R function "sample" in R-devel
> suppressWarnings(RNGversion("3.5.2"))
>
> 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
>
> phi <- matrix(modmat, ncol = ncoef) %*% beta
> phi <- matrix(phi, ncol = nnode)
>
> aster:::setfam(fam.default())
>
> theta <- .C(aster:::C_aster_phi2theta,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ phi = as.double(phi),
+ theta = matrix(as.double(0), nind, nnode))$theta
>
> root <- sample(1:3, nind * nnode, replace = TRUE)
> root <- matrix(root, nind, nnode)
>
> x <- raster(theta, pred, fam, root)
>
> zip <- rep(0, nind * nnode)
>
> out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "unco", origin = zip)
>
> aster:::setfam(fam.default())
>
> a <- .C(aster:::C_aster_theta2phi,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(zip),
+ phi = matrix(as.double(0), nind, nnode))$phi
>
> M <- matrix(modmat, ncol = ncoef)
>
> alpha <- as.numeric(lm(as.numeric(a) ~ 0 + M)$coefficients)
>
> out.too <- mlogl(beta - alpha, pred, fam, x, root, modmat, deriv = 2,
+ type = "unco")
> all.equal(out, out.too)
[1] TRUE
>
> beta.old <- beta
> beta <- beta - alpha
>
> my.value <- 0
> for (j in 1:nnode) {
+ ifam <- fam[j]
+ k <- pred[j]
+ if (k > 0)
+ xpred <- x[ , k]
+ else
+ xpred <- root[ , j]
+ for (i in 1:nind)
+ my.value <- my.value -
+ sum(x[i, j] * theta[i, j] -
+ xpred[i] * famfun(famlist[[ifam]], 0, theta[i, j]))
+ }
> all.equal(out$value, my.value)
[1] TRUE
>
> foo <- function(beta) {
+ stopifnot(is.numeric(beta))
+ stopifnot(is.finite(beta))
+ mlogl(beta, pred, fam, x, root, modmat, type = "unco")$value
+ }
>
> g <- grad(foo, beta)
> all.equal(g, out$gradient)
[1] TRUE
>
> h <- hessian(foo, beta)
> all.equal(h, out$hessian)
[1] TRUE
>
> ##########
>
> objfun <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 1,
+ type = "unco")
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ return(result)
+ }
> nout1 <- nlm(objfun, beta, fscale = nind)
Warning message:
In nlm(objfun, beta, fscale = nind) :
NA/Inf replaced by maximum positive value
> nout <- nlm(objfun, nout1$estimate, fscale = nind)
> all.equal(nout1$minimum, nout$minimum)
[1] TRUE
> all.equal(nout1$estimate, nout$estimate)
[1] TRUE
>
> beta.mle.new <- nout$estimate
> beta.mle.old <- beta.mle.new + alpha
> mout.new <- mlogl(beta.mle.new, pred, fam, x, root, modmat, deriv = 1,
+ type = "unco")
> mout.old <- mlogl(beta.mle.old, pred, fam, x, root, modmat, deriv = 1,
+ type = "unco", origin = zip)
> all.equal(mout.new, mout.old, tol = 1e-7)
[1] TRUE
>
> ##########
>
> objfun <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "unco")
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ attr(result, "hessian") <- out$hessian
+ return(result)
+ }
> nout1 <- nlm(objfun, beta, fscale = nind)
> nout <- nlm(objfun, nout1$estimate, fscale = nind, iterlim = 1000)
> all.equal(nout1$minimum, nout$minimum, tol = 1e-4)
[1] TRUE
> all.equal(nout1$estimate, nout$estimate, tol = 2e-2)
[1] TRUE
>
> objfun.old <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "unco", origin = zip)
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ attr(result, "hessian") <- out$hessian
+ return(result)
+ }
> nout.old <- nlm(objfun.old, beta.mle.old, fscale = nind, iterlim = 1000)
> all.equal(nout$minimum, nout.old$minimum)
[1] TRUE
> all.equal(nout$estimate, nout.old$estimate - alpha, tol = 1e-4)
[1] TRUE
>
>
> proc.time()
user system elapsed
0.276 0.032 0.299