Hi All,
I am trying to generate a custom design with one continuous factor, X1, and two discrete numeric factors, X2 and X3. 
The two discrete numeric factors are interdependent such that if X2=0 then X3 doesn't apply. To be specific, X2 is concentration of a molecule and X3 is molecular weight. If I have a molecule concentration of 0 for my sample, then there is no molecular weight because there is no molecule in the sample.
I could just manually change the levels of X3 to 0 where appropriate in the design matrix, however, this artificially increases replicates of certain runs. For example, three different runs (with levels of [X1, X2, X3]), [37, 0, 1500], [37, 0, 500], and [37, 0, 50] will all become [37, 0, 0] if I just manually adjust the table. I would rather run new unique conditions than repeat this one.
I tried writing a script to do this but it could not converge (I think because X3=0 is not defined in the initial design. I don't want X3=0 for any samples except for when X2=0).
If( Name( "X2" ) ==0, Name( "X3" ) == 0, Name ( "X3" ) != 0 )
I was hoping this would assign X3 a value of 0 if X2 is 0 and if not, then X3 would not be equal to 0.
Any suggestions on how to properly assign factor levels of 0 without creating unnecessary replicates? Is it possible that I don't need to assign X3 = 0 but I can tell the software that X3 doesnt apply when X2=0?
Thanks!!!
What if you eliminate the X3 = 0 level from your study?
