Timeline for NonlinearModelFit increase accuracy for 2 dimensional model
Current License: CC BY-SA 3.0
6 events
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| Apr 13, 2018 at 16:53 | comment | added | JimB |
You'll need to see how that works with the full dataset. With just the 26 data points the model is overparameterized, a1[1,0] and a1[1,1] are estimated to be essentially zero, and all of the correlations among the parameter estimates are either -1 of +1. If a bad/undesired fit is then found, that means the model doesn't fit the data and you'll need to figure out the guilty party: data or model. But NonlinearModelFit is innocent in this case.
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| Apr 13, 2018 at 16:20 | comment | added | Psyphy | Thanks for the answer. However, I forgot to mention that the restrictions are very important, $b1[1,1]$ has to be more or less equal to $k$. Let´s suppose that $k=1.45$. | |
| Apr 13, 2018 at 5:31 | comment | added | JimB |
@Psyphy Obviously you'll need to check out this approach with the full dataset. But when you do you might want to run the following command: nlm["CorrelationMatrix"]//MatrixForm. If there are lots of values close to -1 and +1, then it is likely that the model is way over-parameterized. (It certainly is overparameterized with just the 26 data points supplied to estimate 16 parameters: 15 coefficients and 1 error variance.)
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| Apr 13, 2018 at 5:20 | comment | added | JimB | @J.M. It sure seems that way. I guess the test will be if things work with the complete dataset. Non-convergence (or convergence to the wrong solution) because of scaling issues is pretty common but not always recognized. | |
| Apr 13, 2018 at 5:07 | comment | added | J. M.'s missing motivation♦ | Huh, so it was a matter of bad scaling... :D Maybe the OP should have picked better units for his measurements. | |
| Apr 13, 2018 at 5:02 | history | answered | JimB | CC BY-SA 3.0 |