Is it possible to create new variables when logistic 4P fitting?

[#1]

Hello.

I have a question about the how to create more variables when fitting 4PL curve.

For example, we normally test 10 serially diluted concentration points and each point has triplicate results for reference and test product.

Then, we do equivalence test using nonlinear fitting with sigmoid curves-logistic curves-logistic 4p with log transformed concentration data.

And we can see the chart that has four categories (growth rate, Inflection point, lower asymptote, and upper asymptote).

My question is

Is it possible to create new variables other than those 4 categories above? 

In some cases, a higher variability with the lower asymptote ratio can be observed due to the large impact of small variations in the ratios of reference and test sample. To overcome this potential drawback, Yang et al. suggest not considering the lower asymptote alone, but together with the upper asymptote as the ratio of upper to lower asymptote like New Variable=(upper asymptote - Lower asymptote)ref / (upper asymptote - lower asymptote)test.

This is not about creating new column in data table but I think we have to make use of the result data (upper and lower asymptote) and create new variable.

Does anyone have the same issue with me?

Thank you for your time. 

[#2]

@Hyunsik we currently do not have the feature to create a custom test for functional combinations of model parameters in Fit Curve.  I encourage to go to our Wish List and describe your need, so we can look into adding the capabilities you would like to see in JMP.

[#3]

Thank you Sam. I'll put this issue on Wish List.