nlme (version 3.1-1)

fdHess: Finite difference Hessian

Description

Evaluate an approximate Hessian and gradient of a scalar function using finite differences.

Usage

fdHess(pars, fun, ..., .relStep=(.Machine$double.eps)^(1/3), minAbsPar=0)

Arguments

pars

the numeric values of the parameters at which to evaluate the function fun and its derivatives.

fun

a function depending on the parameters pars that returns a numeric scalar.

...

Optional additional arguments to fun

.relStep

The relative step size to use in the finite differences. It defaults to the cube root of .Machine$double.eps

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.

Value

• A list with components
• mean
  the value of function fun evaluated at the parameter values pars
• gradient
  an approximate gradient
• Hessian
  a matrix whose upper triangle containst an approximate Hessian.

Details

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.

Examples

fdHess(c(12.3, 2.34), function(x) x[1]*(1-exp(-0.4*x[2])))

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