Timeline for MeanPredictionBands not working in ResourceFunction["MultiNonlinearModelFit"]
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Oct 10, 2022 at 17:09 | history | edited | Lukas Lang | CC BY-SA 4.0 |
deleted 14 characters in body
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| Oct 10, 2022 at 17:08 | comment | added | Lukas Lang | @SjoerdSmit Not really - I could have sworn that I first tried something like that, but evidently, I failed. I can't see a reason why it should ever fail. Thanks! | |
| Oct 10, 2022 at 11:56 | comment | added | Sjoerd Smit |
Thanks for figuring this out! Is there a reason for not just computing the gradient with grad = D[fitfun, {Replace[fitParams, {v_, ___} :> v , {1}]}]? It seems to give the exact same results for me that way.
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| Sep 12, 2022 at 18:34 | comment | added | JimB |
+1 for the modification of MultiNonlinearModelFit (and despite my multiple complaints about MultiNonlinearModelFit, it is wonderfully constructed and useful function). However, that single complaint I have ("assuming a common error variance") results in this case with the resulting prediction interval being too small for data1 and too large for data2.
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| Sep 12, 2022 at 14:46 | comment | added | JackySnoep | Thank you Lukas, I need to study the definition a bit more but your solution looks good. I find the MultiNonlinearModelFit function very useful, and with the explicit "grad" definition you used, I hope to be able to use the "MeanPredictionBands" for a wider set of FittedModels. | |
| Sep 12, 2022 at 14:12 | history | answered | Lukas Lang | CC BY-SA 4.0 |