dssens

Calculates the sensitivity function from the gradient vector, the Identity Matrix and the parameters of
interest.

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

dssens function

<dl><dt>grad</dt>
<dd>A function in a single variable that returns the partial derivatives vector of the model.</dd>
<dt>M</dt>
<dd>Information Matrix for the sensitivity function.</dd>
<dt>par_int</dt>
<dd>Numeric vector of the indexes of the parameters of interest for Ds-Optimality.</dd></dl>

Arguments

Sensitivity function for Ds-Optimality — dssens

<dl>

<dt>grad</dt>
<dd>A function in a single variable that returns the partial derivatives vector of the model.</dd>


<dt>M</dt>
<dd>Information Matrix for the sensitivity function.</dd>


<dt>par_int</dt>
<dd>Numeric vector of the indexes of the parameters of interest for Ds-Optimality.</dd>

</dl>

dssens: Sensitivity function for Ds-Optimality

Description

Usage

Value

Arguments