sphereN

This function calculates the Euclidian distance from a point to the origin
after a random displacement of its position.

Stochastic

random

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

sphereN function

<dl><dt>x</dt>
<dd>The coordinates of the point</dd>
<dt>sd</dt>
<dd>The standard deviation of the noise
to be added to the position of <code>x</code>, a normal distribution with mean
zero is used.</dd>
<dt>aggregate</dt>
<dd>If <code>aggregate</code> is <code>TRUE</code> the distance is returned, 
otherwise the size of the projection of the distance among each axis.</dd></dl>

Arguments

Author

Sphere function with random noise — sphereN

<dl>

<dt>x</dt>
<dd>The coordinates of the point</dd>


<dt>sd</dt>
<dd>The standard deviation of the noise
to be added to the position of <code>x</code>, a normal distribution with mean
zero is used.</dd>


<dt>aggregate</dt>
<dd>If <code>aggregate</code> is <code>TRUE</code> the distance is returned, 
otherwise the size of the projection of the distance among each axis.</dd>

</dl>

sphereN: Sphere function with random noise

Description

Usage

Value

Arguments

Author

Examples