Hello,
Can covariates also be used in connection with a Definitive Screening Design, or is that only possible, for example, in a Custom Design?
Regards
Klaus
Ok, with this context, you're in the first situation described in point a) of my answer. You won't be able to balance these values in the design because you don't know in advance these values, so this factor is uncontrolled, like ambiant température recorded during experiments, not a covariate.
You can create any design you want and record these values after, and adding this uncontrollable factor effect in the model.
Hi @NominalGemsbok3,Definitive Screening Designs can only handle numeric continuous factors or 2-levels categorical factors.Read more about Definitive Screening Designs in JMP Help.
Custom designs can handle many types of factors.Hope this answer will help you,
It seems, however, that there is a workaround in which you can add the covariate as a continuous variable directly in Construct Model Effects.
Covariates are a specific type of Factor : "The values of a covariate factor are measurements on experimental units that are known in advance of an experiment."
Unless the covariates levels and factor levels combinations do match with the ones needed for a Definitive Screening Design (in which case, you can only specify them as continuous factors), else you'll have to use the Custom Designs platform.
You can use a Bayesian D-Optimal design with covariates and get similar performances as DSD : https://www.linkedin.com/posts/victorguiller_doe-experimentaldesign-datascience-activity-7254386032484184066-VAfN
Hope this clarify the answer,
Thank you very much — this is a very interesting approach. But I hope you’ll forgive me for needing some further clarification; the experiment is very important, time-consuming, and also expensive.
a) What do you mean by: “Unless the combinations of covariate levels and factor levels match those required for a Definitive Screening Design (DSD)…”?In our example, it would look like this: we have the following factors and factor levels in our DSD:
1 0 1 1 1 1 No1 0 -1 -1 -1 -1 Yes2 1 0 1 1 -1 No2 -1 0 -1 -1 1 Yes1 1 -1 0 1 1 Yes1 -1 1 0 -1 -1 No2 1 -1 -1 0 1 No2 -1 1 1 0 -1 Yes1 1 1 -1 -1 0 No1 -1 -1 1 1 0 Yes2 1 -1 1 -1 -1 No2 -1 1 -1 1 1 Yes1 1 1 -1 1 -1 Yes1 -1 -1 1 -1 1 No2 1 1 1 -1 1 Yes2 -1 -1 -1 1 -1 No1 0 0 0 0 0 Yes1 0 0 0 0 0 No2 0 0 0 0 0 Yes2 0 0 0 0 0 No
The first column represents a block, and the last one a categorical variable.For each row — that is, for each experimental run — we have a corresponding measured value, say a frequency, which clearly and uniquely belongs to that specific run and which we would like to use as a covariate.Would that be acceptable?
b) I had the impression that in a Bayesian design, there are more and different correlations present. Are the results obtained from both methods still comparable or equivalent?
Sorry, but where did you get your definition of covariate? The values don't have to be known before the experiment. The covariate is a measurable independent noise variable (a factor one is not willing to control) that varies over the course of the experiment. It covaries with the Y. Since it is a random variable, to analyze it, you must add it to the model. This requires you create a mixed model (both fixed and random effects in the model).
