jafwd

approximate Jacobian via forward differences

Provides tools for working with nonlinear least squares problems.
For the estimation of models reliable and robust tools than nls(), where the
the Gauss-Newton method frequently stops with 'singular gradient' messages.
This is accomplished by using, where possible, analytic derivatives to compute
the matrix of derivatives and a stabilization of the solution of the estimation
equations. Tools for approximate or externally supplied derivative matrices
are included. Bounds and masks on parameters are handled properly.

John C. Nash

nlsr

Functions for Nonlinear Least Squares Solutions - Updated 2022

Duncan Murdoch

Fernando Miguez

Arkajyoti Bhattacharjee

jafwd function

<dl><dt>pars</dt>
<dd>a named numeric vector of parameters to the model</dd>
<dt>resfn</dt>
<dd>a function to compute a vector of residuals</dd>
<dt>bdmsk</dt>
<dd>Vector defining bounds and masks. Default is <code>NULL</code></dd>
<dt>resbest</dt>
<dd>If supplied, a vector of the residuals at the parameters
<code>pars</code> to save re-evaluation.</dd>
<dt>ndstep</dt>
<dd>A tolerance used to alter parameters to compute numerical
approximations to derivatives. Default <code>1e-7</code>.</dd>
<dt>...</dt>
<dd>Extra information needed to compute the residuals</dd></dl>

Arguments

J C Nash 2014-7-16 nashjc _at_ uottawa.ca

Author

jafwd — jafwd

<dl>

<dt>pars</dt>
<dd>a named numeric vector of parameters to the model</dd>


<dt>resfn</dt>
<dd>a function to compute a vector of residuals</dd>


<dt>bdmsk</dt>
<dd>Vector defining bounds and masks. Default is <code>NULL</code></dd>


<dt>resbest</dt>
<dd>If supplied, a vector of the residuals at the parameters
<code>pars</code> to save re-evaluation.</dd>


<dt>ndstep</dt>
<dd>A tolerance used to alter parameters to compute numerical
approximations to derivatives. Default <code>1e-7</code>.</dd>


<dt>...</dt>
<dd>Extra information needed to compute the residuals</dd>

</dl>

jafwd: jafwd

Description

Usage

Arguments

Author