fdHess {nlme}R Documentation

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 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.


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 fun evaluated at the parameter values pars
gradient an approximate gradient
Hessian a matrix whose upper triangle containst an approximate Hessian.


Jose Pinheiro jcp@research.bell-labs.com, Douglas Bates bates@stat.wisc.edu


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

[Package nlme version 3.1-66 Index]