FIM_3par_exp_censor2

It provides the cpp function for the FIM introduced in Page 247 of Schmidt and Schwabe (2015) for random censored data (type two censored data).

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_3par_exp_censor2 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 \((\beta_0, \beta_1, \beta_2)\).</dd>
<dt>tcensor</dt>
<dd>The experiment is terminated at the fixed time point <code>tcensor</code>.</dd></dl>

Arguments

Fisher Information Matrix for a 3-Parameter Cox Proportional-Hazards Model for Random Censored Data — FIM_3par_exp_censor2

<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 \((\beta_0, \beta_1, \beta_2)\).</dd>


<dt>tcensor</dt>
<dd>The experiment is terminated at the fixed time point <code>tcensor</code>.</dd>

</dl>

FIM_3par_exp_censor2: Fisher Information Matrix for a 3-Parameter Cox Proportional-Hazards Model for Random Censored Data

Description

Usage

Value

Arguments

References