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Jake_b
Jake_b
Level I

nonlinear vs fit curve giving different results?

Created: Nov 9, 2020 06:11 AM  |  Last Modified: Jun 10, 2023 1:40 PM (3258 views)

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!

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10 REPLIES 10
Jake_b
Jake_b
Level I

Re: nonlinear vs fit curve giving different results?

Nov 10, 2020 06:27 AM (768 views)  |  Posted in reply to message from Jake_b 11-09-2020
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.
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