Unlike `stats::optimHess` which assumes the gradient is accurate,
nlmixr2Hess does not make as strong an assumption that the gradient
is accurate but takes more function evaluations to calculate the
Hessian. In addition, this procedures optimizes the forward
difference interval by nlmixr2Gill83
Usage
nlmixr2Hess(par, fn, ..., envir = parent.frame())Arguments
- par
Initial values for the parameters to be optimized over.
- fn
A function to be minimized (or maximized), with first argument the vector of parameters over which minimization is to take place. It should return a scalar result.
- ...
Extra arguments sent to
nlmixr2Gill83- envir
an environment within which to evaluate the call. This will be most useful if
whatis a character string and the arguments are symbols or quoted expressions.
Examples
func0 <- function(x){ sum(sin(x)) }
x <- (0:10)*2*pi/10
nlmixr2Hess(x, func0)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 143438578824 0 0 0 0 0
#> [2,] 0 54098751870 0 0 0 0
#> [3,] 0 0 28167126506 0 0 0
#> [4,] 0 0 0 17234064451 0 0
#> [5,] 0 0 0 0 11620957271 0
#> [6,] 0 0 0 0 0 8362405349
#> [7,] 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 0 0 0 0 0.00
#> [2,] 0 0 0 0 0.00
#> [3,] 0 0 0 0 0.00
#> [4,] 0 0 0 0 0.00
#> [5,] 0 0 0 0 0.00
#> [6,] 0 0 0 0 0.00
#> [7,] 337177531807 0 0 0 0.00
#> [8,] 0 546113920621 0 0 0.00
#> [9,] 0 0 545713835656 0 0.00
#> [10,] 0 0 0 337491323869 0.00
#> [11,] 0 0 0 0 26296.35
fr <- function(x) { ## Rosenbrock Banana function
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
grr <- function(x) { ## Gradient of 'fr'
x1 <- x[1]
x2 <- x[2]
c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1),
200 * (x2 - x1 * x1))
}
h1 <- optimHess(c(1.2,1.2), fr, grr)
h2 <- optimHess(c(1.2,1.2), fr)
## in this case h3 is closer to h1 where the gradient is known
h3 <- nlmixr2Hess(c(1.2,1.2), fr)