Does anyone know how to program a mathematica algorithm that does the same thing that FindFit does? Is there documentation of it somewhere? I assume it is a least squares algorithm but with very generalized arguments. I ask because I'm trying to use 'weighted' least squares and it basically involves just one more factor.
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Fitworks using singular value decomposition.FindFituses the same method for the linear least-squares case, the Levenberg–Marquardt method for nonlinear least-squares, and generalFindMinimummethods for other norms.
NonlinearModelFit allows fitting of weighted data, as J.M. commented
Edit:
The best fit parameters are a property of the model:
p = Table[Prime[x], {x, 20}];
nlm = NonlinearModelFit[p, a x Log[b + c x], {a, b, c}, x];
nlm["BestFitParameters"]
{a -> 1.42076, b -> 1.65558, c -> 0.534645}
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$\begingroup$ How do I get the list parameters from NonlinearModel? FindFit outputs such a thing $\endgroup$ – minusatwelfth Oct 31 '15 at 16:56
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$\begingroup$ @minus, it's all in the docs, if you'd look for them. $\endgroup$ – J. M. will be back soon♦ Nov 1 '15 at 2:04
NonlinearModelFit[]can handle weighted nonlinear least squares. $\endgroup$ – J. M. will be back soon♦ Oct 31 '15 at 14:04