update_design

Updates a design adding a new point to it. If the added point is closer than <code>delta</code> to an existing
point of the design, the two points are merged together as their arithmetic average. Then updates the weights
to be equal to all points of the design.

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_design function

<dl><dt>design</dt>
<dd>Design to update. 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>xmax</dt>
<dd>The point to add as a numeric value.</dd>
<dt>delta</dt>
<dd>Threshold which defines how close the new point has to be to any of the existing ones in order to
merge with them.</dd>
<dt>new_weight</dt>
<dd>Number with the weight for the new point.</dd></dl>

Arguments

Update Design with new point — update_design

<dl>

<dt>design</dt>
<dd>Design to update. 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>xmax</dt>
<dd>The point to add as a numeric value.</dd>


<dt>delta</dt>
<dd>Threshold which defines how close the new point has to be to any of the existing ones in order to
merge with them.</dd>


<dt>new_weight</dt>
<dd>Number with the weight for the new point.</dd>

</dl>

update_design: Update Design with new point

Description

Usage

Value

Arguments