Principal components and screening DoE

[#1]

Hello - I am currently using PCA on 60+ descriptors of chemical elements to reduce the dimensionality and convert "element" from a categorical variable to a continuous variable. However, I cannot find a simple way to use a set of factors with lots of required level combinations. My two thoughts are the "set covariate factors" or the disallowed combinations script. However covariate runs A) has a weird suggestion of the number of runs equaling the number of levels of my PC's, even without me checking the "Include all selected covariate rows in the design" and B) seems antithetical to the concept of PCA, which generates uncorrelated components. The disallowed combinations script I assume would work (haven't explored it yet), but I have never played with JSL and so I wanted to see if anyone had any suggestions before I go down that road.

Thanks so much!

P.S. I also included the (unfinished) table and a separate one for the PC's that I'm using to generate the DoE if that helps.

[#2]

Oh I understand. I guess I assumed Phil was saying "...and then throw it into custom design." That makes sense. Thanks!

Not sure if I should put this into a new question, but I am also using two different heat treatments (microwave and thermal), which normally would be fine as a categorical variable, but there is also a switch of a second factor associated with the different levels of the "heat treatment type" factor: Microwave makes more sense to control the "power input" as an additional factor, while thermal makes more sense to use the "temperature" set point as a factor. My guess is that the only thing to do would be two DoE's for the different heat treatment methods, but just thought I'd ask if y'all had any thoughts.

If this should be a second question, I'll be happy to make a new one. 

[#3]

Yes, I think that would be good to put in a separate question. But briefly, I think your options are to either run separate experiments for muwave and thermal. Or you could have a categorical factor for heat treatment with combinations of type and level, e.g. thermal_100C, thermal_120C, muwave_200W, muwave_300W. I think that you could even analyse the data in this way having run the experiments separately.

[#4]

@Phil_Kay Awesome. I posted the question as its own, as you suggested, so I guess we can continue there, as I do have a question or two. Thanks! https://community.jmp.com/t5/Discussions/DoE-where-choice-of-factor-changes-with-the-levels-of-anoth...