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Optimal Designs

Thommy7571
Level I

Hello, can JMP also create and evaluate optimal designs? D, E, I designs, etc.?

Which optimal design is suitable as a screening design?

How many runs makes sense?

Can this be expanded and then used for a more complex evaluation?

What optimal design is recommended for the more complex evaluation?

What number of runs makes sense then?

Thanks.

Tommy7571

This post originally written in German and has been translated for your convenience. When you reply, it will also be translated back to German.

3 REPLIES 3
Victor_G
Super User

Re: Optimale Designs

Hi @Thommy7571,

 

Optimal designs are available in JMP under the Custom design platform.

  • By default, JMP will create a design with a recommended optimality criterion based on the model assumed : Optimality Criteria (jmp.com) But you can change the optimality criterion manually by clicking in the red triangle next to Custom design, go to "Optimality Criterion", and from there select the one you want (between D-, I-, A- and Alias-optimality criteria).
  • For screening designs, D- and A- optimal designs are recommended. Bradley Jones recommends using A-Optimal designs for screening designs, as they offer similar performances to D-Optimal designs with higher flexibility if you want to put more emphasis on certain terms/effects estimation (for example main effects vs. interactions). See his presentation here : 21st Century Screening Designs (2020-US-45MP-538) - JMP User Community
  • There is no perfect situation concerning the number of runs, it's a compromise between your available experimental budget/ressources, your objectives and the confidence/precision you want in the results. One good option is to create several designs, and compare them using the platform Compare Designs (jmp.com). You can also generate several designs and compare them quickly (to assess the benefits of increasing the number of runs for example) using the Design Explorer (jmp.com).
  • One design may not answer all your questions/needs.
    Think sequentially and iterate : you can start with a screening design (Optimal design, Definitive Screening design if applicable, others, ...), then augment it to uncover/detect higher order effects through an optimisation design (response surface type), and if you have non-linear/complex relationships between factors, you can also augment your design through a Space-Filling approach, to benefit from various Machine Learning methods in the analysis of results.

 

If you would like to have a follow-up with more precise questions on a specific topic, feel free to answer

Hope this first answer will help you, 

 

Victor GUILLER

"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)
Thommy7571
Level I

Re: Optimale Designs

Hello,

Can a definitive screening design (DSD) be converted into an A-optimal design? If so, can this be useful?

I tried adding factorial points to a DSD, but when converting to an optimal design, the runs with zero were removed.

Greetings

Tommy7571

This post originally written in German and has been translated for your convenience. When you reply, it will also be translated back to German.

Victor_G
Super User


Re: Optimale Designs

Hi @Thommy7571,

 

The construction of Definitive Screening Designs and Optimal designs are very different :

So I'm not sure what you imply with "converting" a design type into another, but you can augment a DSD (or any other design) into an A-Optimal design.
Once again, the augmentation choices are different and rely on different techniques :

  • Algorithmic computations for "Space-Filling" and "Augment" (optimal design augmentation) types
  • Pre-determined and specific points for "Replicate", "Add Centerpoints", "Fold Over" and "Add Axial",

    so you can indeed expect different augmented points proposals betwwen these augmentation techniques.

 

Hope this answer will help you,

Victor GUILLER

"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)