Finite difference Hessian
Evaluate an approximate Hessian and gradient of a scalar function using finite differences.
fdHess(pars, fun, ...,
.relStep = .Machine$double.eps^(1/3), minAbsPar = 0)pars |
the numeric values of the parameters at which to evaluate the
function |
fun |
a function depending on the parameters |
... |
Optional additional arguments to |
.relStep |
The relative step size to use in the finite
differences. It defaults to the cube root of |
minAbsPar |
The minimum magnitude of a parameter value that is considered non-zero. It defaults to zero meaning that any non-zero value will be considered different from zero. |
This function uses a second-order response surface design known as a “Koschal design” to determine the parameter values at which the function is evaluated.
A list with components
mean |
the value of function |
gradient |
an approximate gradient (of length |
Hessian |
a matrix whose upper triangle contains an approximate Hessian. |
José Pinheiro and Douglas Bates bates@stat.wisc.edu
(fdH <- fdHess(c(12.3, 2.34), function(x) x[1]*(1-exp(-0.4*x[2]))))
stopifnot(length(fdH$ mean) == 1,
length(fdH$ gradient) == 2,
identical(dim(fdH$ Hessian), c(2L, 2L)))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.