https://github.com/cran/aster
Tip revision: fa7795259e71bf245e06b2cf7c012e2f3322cd2f authored by Charles J. Geyer on 14 March 2008, 00:00:00 UTC
version 0.7-4
version 0.7-4
Tip revision: fa77952
mlogl-unco.R
library(aster)
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
phi <- matrix(modmat, ncol = ncoef) %*% beta
phi <- matrix(phi, ncol = nnode)
aster:::setfam(fam.default())
theta <- .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))$theta
root <- sample(1:3, nind * nnode, replace = TRUE)
root <- matrix(root, nind, nnode)
x <- raster(theta, pred, fam, root)
zip <- rep(0, nind * nnode)
out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
type = "unco", origin = zip)
print(out)
aster:::setfam(fam.default())
a <- .C("aster_theta2phi",
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
theta = as.double(zip),
phi = matrix(as.double(0), nind, nnode),
PACKAGE = "aster")$phi
M <- matrix(modmat, ncol = ncoef)
alpha <- as.numeric(lm(as.numeric(a) ~ 0 + M)$coefficients)
out.too <- mlogl(beta - alpha, pred, fam, x, root, modmat, deriv = 2,
type = "unco")
all.equal(out, out.too)
beta.old <- beta
beta <- beta - alpha
my.value <- 0
for (j in 1:nnode) {
ifam <- fam[j]
k <- pred[j]
if (k > 0)
xpred <- x[ , k]
else
xpred <- root[ , j]
for (i in 1:nind)
my.value <- my.value -
sum(x[i, j] * theta[i, j] -
xpred[i] * famfun(famlist[[ifam]], 0, theta[i, j]))
}
all.equal(out$value, my.value)
my.grad <- NaN * out$gradient
epsilon <- 1e-9
for (i in 1:ncoef) {
beta.eps <- beta
beta.eps[i] <- beta[i] + epsilon
out.eps <- mlogl(beta.eps, pred, fam, x, root, modmat, deriv = 0,
type = "unco")
my.grad[i] <- (out.eps$value - out$value) / epsilon
}
all.equal(out$gradient, my.grad, tolerance = sqrt(epsilon))
##########
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)
}
nout <- nlm(objfun, beta, fscale = nind)
print(nout)
nout <- nlm(objfun, nout$estimate, fscale = nind)
print(nout)
beta.mle.new <- nout$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)
##########
my.hess <- matrix(NaN, ncoef, ncoef)
for (i in 1:ncoef) {
beta.eps <- beta
beta.eps[i] <- beta[i] + epsilon
out.eps <- mlogl(beta.eps, pred, fam, x, root, modmat, deriv = 1,
type = "unco")
my.hess[ , i] <- (out.eps$gradient - out$gradient) / epsilon
}
all.equal(out$hessian, my.hess, tolerance = sqrt(epsilon))
##########
objfun <- function(beta) {
out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
type = "unco")
result <- out$value
attr(result, "gradient") <- out$gradient
attr(result, "hessian") <- out$hessian
return(result)
}
nout <- try(nlm(objfun, beta, fscale = nind))
print(nout)
nout <- nlm(objfun, nout$estimate, fscale = nind, iterlim = 1000)
print(nout)
objfun.old <- function(beta) {
out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
type = "unco", origin = zip)
result <- out$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)
print(nout.old)
all.equal(nout$minimum, nout.old$minimum)
all.equal(nout$estimate, nout.old$estimate - alpha)