nonlinear vs fit curve giving different results?Hello - I believe that, if I go through either the "nonlinear" or the "fit curve" options, I should end up with the same result (JMP 15.0). I'm trying to use the built in 4 parameter logistic model from the model library, and fitting a number of different variables by strain (where "strain" is a number of different genetic backgrounds).
For some of the strains, the 'fit curve' and the 'nonlinear' approaches give, as I would expect, identical results - BUT for some of the strains, the 'nonlinear' approach does not fit the data correctly. Even though I make sure that the initial starting parameters are very close to a best fit (by eye), for the strains where it doesn't work, it either won't fit at all, or converges on an answer far away from where it should be. It gives an error message saying: "Warning: xx missing models", but there are definitely no missing data. Does anyone have a fix for this please (ideally without having to use scripting), or can tell me where I am going wrong?
many thanks!
AnalyticsOtherRe: nonlinear vs fit curve giving different results?Why would you use the Nonlinear platform if the Fit Curve platform fits the desired model?
Fit Curve includes heuristics about each of the models that it supports. It is easier to use and more powerful for a set of models but otherwise inflexible. The Nonlinear platform is more powerful and flexible but does not have any heuristics, so it is up to you to handle all of the details. That is the reason that Fit Curve was created. It eases the burden on you.
Nonlinear regression is a numerical optimization or search technique. It is not a closed form computation like linear regression. You should not expect the results to exactly agree unless every detail at the start is identical and the same algorithm and its implementation are used.
Re: nonlinear vs fit curve giving different results?thanks for responses!
"Why would you use the Nonlinear platform if the Fit Curve platform fits the desired model?"
Because the nonlinear platform is quicker to use when I have several variables to fit and each variable is fitted by several groups. The nonlinear platform also allows me to calculate confidence intervals for the model parameters, which the fit curve doesn't - this last is really useful as I am using the CIs as part of the visualisation of the output.
"You should not expect the results to exactly agree unless every detail at the start is identical and the same algorithm and its implementation are used."
OK, fine. BUT the results do agree, exactly, between the two platforms, EXCEPT (I've now found, on closer inspection), for just one of the groups, where the nonlinear platform fails to fit it correctly, across all of the variables. I can't see why this is - I will try creating a new jmp file with just that group in it, to see if I could manage to fit it that way, but I would like to understand, if possible, just why that group is misbehaving. (It's not an outlier in terms of the data, so it's not that I am just further away from finding a minimum compared to the other groups.)
thanks again
Re: nonlinear vs fit curve giving different results?The two different platforms almost have different functions. Fit curve is super handy when I want to fit typical models quickly, and Nonlinear is more useful when I have a custom function, like something I find in a research paper that isn't main-stream and I want to solve for a set of parameters.
The iterative numerical optimization used to solve for the parameters in either platform is pretty different that solving a least squares regression. A lot of other software use a pile of hidden assumptions rather than giving you all the levers and button like the nonlinear platform.
Re: nonlinear vs fit curve giving different results?OK, a final comment. If I re-save the group (i.e. not re-importing data) that did not fit correctly using nonlinear as a new jmp file, it now all works fine. I'm not sure why this is the case - is it possible for some of the variables to be corrupted for some of the samples only? - but it does mean I now have a way forward.
