gen.design.pars

This function can generate a set of optimal design parameters
 based on given distributions of the rank of optimization target
 (or budget).

Calculate the optimal sample size allocation that
uses the minimum resources to achieve targeted statistical
power in experiments.
Perform power analyses with and without accommodating
costs and budget. The designs cover single-level
and multilevel experiments detecting main, mediation,
and moderation effects (and some combinations).
The references for the proposed methods include:
(1) Shen, Z., & Kelcey, B. (2020).
Optimal sample allocation under unequal costs in
cluster-randomized trials.
Journal of Educational and Behavioral Statistics, 45(4): 446-474.
<doi:10.3102/1076998620912418>.
(2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for
three-level multisite cluster-randomized trials.
Journal of Research on Educational Effectiveness, 15 (1), 130-150.
<doi:10.1080/19345747.2021.1953200>.
(3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in
multisite randomized trials. The Journal of Experimental Education,
90(3), 693-711. <doi:10.1080/00220973.2020.1830361>.
(4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025).
Using ant colony optimization to identify optimal sample
allocations in cluster-randomized trials.
The Journal of Experimental Education, 93(1), 167-185.
<doi:10.1080/00220973.2024.2306392>.
(5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and
optimal design for randomized controlled trials investigating
mediation effects. Psychological Methods.
<doi:10.1037/met0000698>.
(6) Champely, S. (2020). pwr: Basic functions for power analysis
(Version 1.3-0) [Software]. Available from
<https://CRAN.R-project.org/package=pwr>.

Zuchao Shen

Optimal Design and Statistical Power for Experimental Studies
Investigating Main, Mediation, and Moderation Effects

Benjamin Kelcey

gen.design.pars function

<dl><dt>dist.mean</dt>
<dd>List of means - coordinates</dd>
<dt>dist.rank</dt>
<dd>Rank of the archived values of the objective function(s)</dd>
<dt>n.of.ants</dt>
<dd>The number of artificial ants in the search.</dd>
<dt>nl</dt>
<dd>Neighborhood of the search area</dd>
<dt>q</dt>
<dd>The locality of the search (0, 1)</dd>
<dt>n.of.archive</dt>
<dd>The number of the solution archive.</dd>
<dt>xi</dt>
<dd>The convergence pressure (0, Inf)</dd></dl>

Arguments

Generate optimal design parameters using ant colony optimization — gen.design.pars

<dl>

<dt>dist.mean</dt>
<dd>List of means - coordinates</dd>


<dt>dist.rank</dt>
<dd>Rank of the archived values of the objective function(s)</dd>


<dt>n.of.ants</dt>
<dd>The number of artificial ants in the search.</dd>


<dt>nl</dt>
<dd>Neighborhood of the search area</dd>


<dt>q</dt>
<dd>The locality of the search (0, 1)</dd>


<dt>n.of.archive</dt>
<dd>The number of the solution archive.</dd>


<dt>xi</dt>
<dd>The convergence pressure (0, Inf)</dd>

</dl>

gen.design.pars: Generate optimal design parameters using ant colony optimization

Description

Usage

Value

Arguments

References