FIM_sig_emax

It provides the cpp function for FIM for the model <code>~b1+(b2-b1)*(x^b4)/(x^b4+b3^b4)</code>

Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2022) <doi:10.32614/RJ-2022-043>, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.

Ehsan Masoudi

ICAOD

Optimal Designs for Nonlinear Models via ICA

Heinz Holling

Weng Kee Wong

Seongho Kim

FIM_sig_emax function

<dl><dt>x</dt>
<dd>Vector of design points.</dd>
<dt>w</dt>
<dd>Vector of design weight. Its length must be equal to the length of <code>x</code> and <code>sum(w) = 1</code>.</dd>
<dt>param</dt>
<dd>Vector of values for the model parameters <code>c(b1, b2, b3, b4)</code>.
The mean of response variable is .</dd></dl>

Arguments

Fisher Information Matrix for the Sigmoid Emax Model — FIM_sig_emax

<dl>

<dt>x</dt>
<dd>Vector of design points.</dd>


<dt>w</dt>
<dd>Vector of design weight. Its length must be equal to the length of <code>x</code> and <code>sum(w) = 1</code>.</dd>


<dt>param</dt>
<dd>Vector of values for the model parameters <code>c(b1, b2, b3, b4)</code>.
The mean of response variable is .</dd>

</dl>

FIM_sig_emax: Fisher Information Matrix for the Sigmoid Emax Model

Description

Usage

Value

Arguments