I live in a fictional world where my data has no random variation as all of my data is derived from deterministic computer simulations. My goal is typically to find a model which fits my data that is maximally significant but minimally complex so that I can make predictions about the unknown realms.
Most of the variables in my design are continuous but I often cast them as categorical in the Design because each variable typically represents a sub block in my simulation which can take min, typical and max settings. I use categorical variables in the Design but then treat them as continuous in the model fit so I can capture the expected quadratic behavior.
With JMP 10 I found that I could start using Discrete Numeric Factor types which has the behavior which I was mimicking as detailed above. The only problem is when I use discrete numeric I find a much higher occurrence of duplicates of the center points and other replicates. This occurs even when the 'Number of Center Points:' and 'Number of Replicate Runs' fields are set to zero.
Now, since I live in a world with no random variation the duplicate points are of no worth to me.
I've attached an example script which has replicate center points. To detect replicate points just sort the table and browse to the center to see them.
I am using D-optimal designs (which seem to have less center points than I-optimal designs).
Does anyone know of a way to ensure that there are no duplicate data points in a Design?
