evaluateErrorModelDerivatives

evaluateErrorModelDerivatives; evaluate the derivatives of the model error.

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

evaluateErrorModelDerivatives function

<dl><dt>modelError</dt>
<dd>An object <code>ModelError</code> that defines the model error.</dd>
<dt>evaluationModel</dt>
<dd>A dataframe giving the outputs for the model evaluation.</dd></dl>

Arguments

evaluateErrorModelDerivatives; evaluate the derivatives of the model error. — evaluateErrorModelDerivatives

<dl>

<dt>modelError</dt>
<dd>An object <code>ModelError</code> that defines the model error.</dd>


<dt>evaluationModel</dt>
<dd>A dataframe giving the outputs for the model evaluation.</dd>

</dl>

evaluateErrorModelDerivatives: evaluateErrorModelDerivatives; evaluate the derivatives of the model error.

Description

Value

Arguments