I have been wanting to get away from using power calculation tools (JMP, Lenth) in favor of Monte Carlo techniques to determine the a-priori power to observe factor level effects in a DOE. I am really new to this and have a few questions. Lets start with what I have:

  1. A binomial response which I plan to perform a logistic regression to
  2. List of factors that I think are relevant
  3. A model (Main Effects + interaction + quadratic terms)
  4. An experimental design (Taken from JMP or the DoE.wrapper package)
  5. A threshold requirement (0.6)
  6. An effect size (ES = 0.1)

I would like to determine the power to observe an effect in the response for each one of the model terms. I have read the excellent response by user gung which I wanted to use to help tailor my own approach. After reading, I had a few specific questions.

  1. The example uses prior knowledge of the system performance (the rates) that I do not have. How do I continue without this knowledge?
  2. The solution does not factor in effect size in any way (at least as far as I can tell). How do I factor in effect size to this process?
  3. The power for each of the terms is coming from the significance of the model term to be non-zero. Is this the same as the power to observe an effect size in the response?

For question 2, I would assume that you would just need to run the model assuming the null performance (p = 0.6) and then re-run the fit assuming the performance of Null + Effect Size (p = 0.6 + ES). I know this last part might be a bit jumbled up. Any help would be greatly appreciated.


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