What is the difference between FindFit and NonlinearModelFit

I've found one difference between NonLinearModelFit and FindFit, and that is that NonLinearModelFit does not allow you to use the NormFunction to adjust how normalization as weighting is done. By default NonLinearModelFit seeks to reduce the sum of the squares of the residuals, so it will be equivalent to FindFit with NormFunction -> (Norm[Abs[#], 2] &)], as described here. While NonLinearModelFit is a bit "prettier", it does have some limitations in usage.


Note that FindFit was first introduced in version 5 and updated in version 7. Since then, FindFit has not been modified. NonlinearModelFit was introduced in 7 and appears to have remained unchanged since then. The two use the same format for arguments; however, FindFit returns the best fit parameters whereas NonlinearModelFit returns a model. The model has a wealth of statistical information included in it, and is referenced in current versions of the FindFit Documentation:

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The two functions seem to behave similarly both in their default behavior:

data = Table[{i, Exp[RandomReal[{i - 1, i}]]}, {i, 10}];
nlm = NonlinearModelFit[data, Exp[a + b x], {a, b}, x][
   "BestFitParameters"];
ff = FindFit[data, Exp[a + b x], {a, b}, x];
nlm == ff
(* True *)

..and in their possible issues

model = a1 Exp[-(b1 (x - x1))^2] + a2 Exp[-(b2 (x - x2))^2];
data = Block[{a1 = 1, b1 = 5, x1 = -.5, a2 = 2, b2 = 10, x2 = .25, 
    x = Sort[RandomReal[{-1, 1}, 100]]},
   Transpose[{x, model + RandomReal[{-.1, .1}, 100]}]];
fit = FindFit[data, model, {a1, b1, x1, a2, b2, x2}, x];
ffplot = Show[ ListPlot[data, PlotRange -> All], 
  Plot[Evaluate[model /. fit], {x, -1, 1}, 
   PlotStyle -> Directive[Thick, Red]]]
nlm = NonlinearModelFit[data, model, {a1, b1, x1, a2, b2, x2}, x]
nlmplot = 
 Show[ListPlot[data, PlotRange -> All], 
  Plot[nlm[x], {x, -1, 1}, PlotStyle -> Directive[Thick, Red]]]
ffplot === nlmplot
(* True *)

The bottom line, NonlinearModelFit is the next generation of FindFit which provides more functionality and information about the fitted model, and there is likely no reason at this point in time to use FindFit.

Tags:

Fitting

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