evaluateModelVariance

evaluateModelVariance: evaluate the variance of the model

Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to
Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>,
Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>,
Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>,
Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>,
Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and
Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.

Romain Leroux

PFIM

Population Fisher Information Matrix

France Mentré

Jérémy Seurat

evaluateModelVariance function

<dl><dt>model</dt>
<dd>A object <code>Model</code> giving the model.</dd>
<dt>arm</dt>
<dd>A object <code>Arm</code> giving the arm</dd></dl>

Arguments

evaluateModelVariance: evaluate the variance of the model — evaluateModelVariance

<dl>

<dt>model</dt>
<dd>A object <code>Model</code> giving the model.</dd>


<dt>arm</dt>
<dd>A object <code>Arm</code> giving the arm</dd>

</dl>

evaluateModelVariance: evaluateModelVariance: evaluate the variance of the model

Description

Value

Arguments