objFn

fitness

Calcuted error measure between observed and simulated data

General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim.
It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients.
Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided.
See <https://roliveros-ramos.github.io/calibrar/> for more details.

Ricardo OliverosRamos

calibrar

Automated Parameter Estimation for Complex Models

Ricardo Oliveros-Ramos

objFn function

<dl><dt>obs</dt>
<dd>observed data as expected by FUN.</dd>
<dt>sim</dt>
<dd>simulated data matching 'obs'</dd>
<dt>FUN</dt>
<dd>the error function. Current accepted values area: 'norm2', 
'lnorm2', 'lnorm3', 'multinomial', 'pois', 'penalty0', 'penalty1', 'penalty2' and 'normp'.</dd>
<dt>...</dt>
<dd>Additional arguments to FUN</dd></dl>

Arguments

Calcuted error measure between observed and simulated data — objFn

<dl>

<dt>obs</dt>
<dd>observed data as expected by FUN.</dd>


<dt>sim</dt>
<dd>simulated data matching 'obs'</dd>


<dt>FUN</dt>
<dd>the error function. Current accepted values area: 'norm2', 
'lnorm2', 'lnorm3', 'multinomial', 'pois', 'penalty0', 'penalty1', 'penalty2' and 'normp'.</dd>


<dt>...</dt>
<dd>Additional arguments to FUN</dd>

</dl>

objFn: Calcuted error measure between observed and simulated data

Description

Usage

Value

Arguments