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Solve problems, and share tips and tricks with other JMP users.
Hi @Mohnasre,
Welcome in the Community !
You can read the documentation linked to Augment Designs platform.
The process to augment a design is quite straightforward:
- Based on the modeling of the responses in your original design, identify key important factors that appear in effect terms of the models.
- Open the Augment Designs platform, specify the key factors you want to keep in the design augmentation and the responses, and choose an augmentation method. I would recommend at this stage to check the option Group new runs into separate block, as it will introduce a block effect for the second round of experiments that you can use to check if your average response didn't change between the two stages of the design (original vs. augmented) as well as to track any variance difference between the two stages:
If your goal is to augment the initial DSD to fit a Response Surface Model (RSM) on the identified important factors, you can choose Augment, as it will allow you to specify the assumed model you want to be able to fit thanks to the augmented design:
Note that in my screenshot I kept all factors from the original 22-runs DSD, which makes the number of runs to add quite high (48 in total, including the 22 original runs, so 26 new runs to add). In your situation, you may augment the design with less factors, depending on the importance and significance of these factors based on the analysis of your original DSD.
Concerning your second question, a categorical factor will appear in the model as a "If Then" rule, like in this example:
In this example, "Hydrolyze" and "Pre-Soak" are two 2-levels categorical factors. Depending on the level set in the experiment, it will have a positive or negative effect on the response. For example, taking the example of the main effect linked to factor "Hydrolyze" (first Match function appearing in the equation), if the experiment is set at level L1, then the response will increase on average by 0,3794[...]. If the experiment is set at level L2, the response will decrease on average by -0,3794[...].
Note that categorical factors with 2 levels will have the same absolute value for the coefficients of these level, but opposite signs. If you have several levels for a categorical factor, these levels coefficients are linked by the equation: L1 + L2 + L3 + ... = 0.
Hope this answer will help you,
Yes there may be several options possible, but the runs you couldn't afford to build your model will increase the uncertainty and lack of precision of your RSM model.
An option consists in creating a Bayesian I-Optimal model, where the estimability of some terms are set to "If Possible" instead of "Necessary". To do this, left-click on the estimability of the effects you want to change, and set it to "If Possible":
This will have the effect to reduce the required number of runs, but you may end up not able to include these "If Possible" terms in the model, as the creation of new runs will enforce the estimation of "Necessary" effects before the "If Possible" terms. So you have to carefully choose and evaluate the trade-off between the reduction of runs and the assumed model you want to fit.
Another option could have been to do a space filling augmentation, but the presence of your categorical factor will prevent you from this option.
Finally, I would encourage you to wait to have all your results and spend some time on the analysis. Some factors and terms may not be significant and practically important in the evaluation of your responses, so it may simplify the assumed model you want to use in the augmentation phase, by removing non important terms and respecting the principles of Effect Heredity, Effect Sparsity and Effect Hierarchy.
Hope this answer will give you some ideas for later,
