nls_optimize

Obtain the solution to minimize the sum of squared errors of the defined function <code>fun</code> by levenberg-marquardt method. Adapted from PRACMA package.

An implementation of the parameter cascade method in Ramsay, J. O., Hooker,G., Campbell, D., and Cao, J. (2007) for estimating ordinary differential equation models with missing or complete observations. It combines smoothing method and profile estimation to estimate any non-linear dynamic system. The package also offers variance estimates for parameters of interest based on either bootstrap or Delta method.

Haixu Wang

pCODE

Estimation of an Ordinary Differential Equation Model by
Parameter Cascade Method

Jiguo Cao

nls_optimize function

<dl><dt>fun</dt>
<dd>The function returns the vector of weighted residuals.</dd>
<dt>x0</dt>
<dd>The initial value for optimization.</dd>
<dt>...</dt>
<dd>Parameters to be passed for <code>fun</code></dd>
<dt>options</dt>
<dd>Additional optimization controls.</dd>
<dt>verbal</dt>
<dd>Default = 1 for printing iteration and other for suppressing</dd></dl>

Arguments

Optimizer for non-linear least square problems — nls_optimize

<dl>

<dt>fun</dt>
<dd>The function returns the vector of weighted residuals.</dd>


<dt>x0</dt>
<dd>The initial value for optimization.</dd>


<dt>...</dt>
<dd>Parameters to be passed for <code>fun</code></dd>


<dt>options</dt>
<dd>Additional optimization controls.</dd>


<dt>verbal</dt>
<dd>Default = 1 for printing iteration and other for suppressing</dd>

</dl>

nls_optimize: Optimizer for non-linear least square problems

Description

Usage

Value

Arguments