Hi all,
When creating Functional DoE's - Is there a specific additional "guidance" on selecting the number of runs compared to regular DoE's?
Any thoughts or experiences with this?
Best
Florian
Hi @Florian_Vogt ,
It is going to be very hard to do power analysis because you would be using a response that is the functional principal component score. How would you know what that score means before you have done the experiment? You probably would not even know the number of principal components. Maybe if they have a good amount of useful prior data this might be possible.
Perhaps you could think about a simple (non-functional, scalar) response as a proxy in order to do power analysis. That could work but it will only tell you about the power to detect that response, and not the power to detect changes to the functional response.
Even with simple responses, power analysis is hard to do because you often don't have good estimates of the noise prior to the experiment. I know a lot of customers that are big users of DOE that never use power analysis.
As-many-runs-as-you-can-afford is probably the best advice.
Also, looking at the trade-offs with number of runs vs effect correlation and aliasing is useful for any DOE. The number of effects that you can estimate will be a function of the number of runs, of course. And that is true for functional and non-functional DOE.
If your customer wants to find out more about design evaluation I would recommend the Goos & Jones book for very thorough discussion of this topic. (No mention of functional DOE in that book though as it was a few years before FDE).
I hope that helps,
Phil
