update_weightsDS

Implementation of the weight update formula for Ds-Optimality used to optimize the weights of a design,
which is to be applied iteratively until no sizable changes happen.

Calculates D-, Ds-, A-, I- and L-optimal designs for non-linear models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Compares designs via their efficiency, and augments any design with a controlled efficiency. An efficient rounding function has been provided to transform approximate designs to exact designs.

Carlos de la Calle-Arroyo

optedr

Calculating Optimal and D-Augmented Designs

Jesús López-Fidalgo

Licesio J. Rodríguez-Aragón

update_weightsDS function

<dl><dt>design</dt>
<dd>Design to optimize the weights from. It's a dataframe with two columns:<ul>
<li><code>Point</code> contains the support points of the design.</li>
<li><code>Weight</code> contains the corresponding weights of the <code>Point</code>s.</li>
</ul></dd>
<dt>sens</dt>
<dd>Sensibility function for the design and model.</dd>
<dt>s</dt>
<dd>number of parameters of interest of the model</dd>
<dt>delta</dt>
<dd>A parameter of the algorithm that can be tuned. Must be \(0&lt; delta &lt; 1\).</dd></dl>

Arguments

Update weight Ds-Optimality — update_weightsDS

<dl>

<dt>design</dt>
<dd>Design to optimize the weights from. It's a dataframe with two columns:<ul>
<li><code>Point</code> contains the support points of the design.</li>
<li><code>Weight</code> contains the corresponding weights of the <code>Point</code>s.</li>
</ul></dd>


<dt>sens</dt>
<dd>Sensibility function for the design and model.</dd>


<dt>s</dt>
<dd>number of parameters of interest of the model</dd>


<dt>delta</dt>
<dd>A parameter of the algorithm that can be tuned. Must be \(0&lt; delta &lt; 1\).</dd>

</dl>

update_weightsDS: Update weight Ds-Optimality

Description

Usage

Value

Arguments