R Under development (unstable) (2023-12-13 r85679 ucrt) -- "Unsuffered Consequences"
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> 
>  library(aster)
>  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 
   0.34    0.03    0.34