# nlmixr2Hess: Calculate Hessian In nlmixr2est: Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines

 nlmixr2Hess R Documentation

## Calculate Hessian

### Description

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 `what` is a character string and the arguments are symbols or quoted expressions.

### Details

If you have an analytical gradient function, you should use 'stats::optimHess'

### Value

Hessian matrix based on Gill83

### Author(s)

Matthew Fidler

`nlmixr2Gill83`, `optimHess`

### Examples

`````` func0 <- function(x){ sum(sin(x))  }
x <- (0:10)*2*pi/10
nlmixr2Hess(x, func0)

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)
``````

nlmixr2est documentation built on May 29, 2024, 1:39 a.m.