| T O P I C R E V I E W |
| udhaya |
Posted - 07/22/2023 : 09:08:59 AM
I am trying to fit a dataset to an exponential decay function (nonlinear fit). As I start the fitting with the rough estimates of parameters, for the first few cycles of iteration I get a fitting curve which looks visually acceptable. The error values are low (< 5% of the three fitting parameters) and COD (R2) > 0.985. As soon as I increase the iterations by hitting "fit until converge", the resulting final parameters have error values > 100% of the fitting parameters with COD (R2) being > 0.99. I understand, the default criteria of fitting is to achieve COD(R2) as close to one as possible.
Is there a way to achieve low error in fitting parameters at the cost of COD(R2)? It is possible to tell origin to iterate to maintain the error within a limit, while achieving the COD(R2) closest to unity! Will appreciate any help.
Thanks |
| 1 L A T E S T R E P L I E S (Newest First) |
| YimingChen |
Posted - 07/24/2023 : 08:41:12 AM
If the error of the fitted parameter is large, it means the fitting function is over-parameterized. You may consider reducing the number of parameters in the model. Please refer to this page.
https://www.originlab.com/doc/Origin-Help/The_Reason_Why_Fail_to_Converge#Over-parameterized_functions
James |
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