weight_function

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

weight_function function

<dl><dt>model</dt>
<dd>formula describing the model to use. Must use x as the variable.</dd>
<dt>char_vars</dt>
<dd>character vector with the parameters of the models, as written in the <code>formula</code></dd>
<dt>values</dt>
<dd>numeric vector with the parameters nominal values, in the same order as given in <code>parameters</code>.</dd>
<dt>distribution</dt>
<dd>character variable specifying the probability distribution of the response. Can be one of the following:<ul>
<li>'Normal', for normal homoscedastic (default)</li>
<li>'Gamma', which can be used for exponential or normal heteroscedastic with constant relative error</li>
<li>'Poisson'</li>
<li>'Logistic'</li>
</ul></dd></dl>

Arguments

Weight function per distribution — weight_function

<dl>

<dt>model</dt>
<dd>formula describing the model to use. Must use x as the variable.</dd>


<dt>char_vars</dt>
<dd>character vector with the parameters of the models, as written in the <code>formula</code></dd>


<dt>values</dt>
<dd>numeric vector with the parameters nominal values, in the same order as given in <code>parameters</code>.</dd>


<dt>distribution</dt>
<dd>character variable specifying the probability distribution of the response. Can be one of the following:<ul>
<li>'Normal', for normal homoscedastic (default)</li>
<li>'Gamma', which can be used for exponential or normal heteroscedastic with constant relative error</li>
<li>'Poisson'</li>
<li>'Logistic'</li>
</ul></dd>

</dl>

weight_function: Weight function per distribution

Description

Usage

Value

Arguments