construct a loss function for nonlinear fit

[#1]

I see a similar question with no answer, so not much hope but let's try.

Let's say I want to do a non-linear fit (for example logistic function) and then use either log-ratio test or AIC to demonstrate that it fits better than linear. I understand that I need to construct an appropriate Loss function and then the Solution report will show the log-likelihood. What do I put into the loss function?

Is there another way to obtain LL of a non-linear fit?

Thanks!

[#2]

I recommend you see Help > JMP Documentary Library > Predictive and Specialized Modeling > Chapter: Nonlinear Regression > Section: Example of Maximum Likelihood: Logistic Regression. The procedure and process are fully explained and an example is given that results in -2L for the loss value of the fitted model.

[#3]

yes, I have seen that example. However, it provides instructions of how to test the hypothesis about logistic regression (i.e., with categorical response variable). I am trying to compare two models with continuous response variables - one linear, one non-linear.

But thanks!

[#4]

my problem was solved by moving from JMP10 to JMP15 (for now just as a trial). There there is a Curve Fit dialog which does exactly what I need.