# Selecting method and using Abs[] for FindFit

I'm trying to fit the following list data

R = {0, 10^-8, 10^-7, 10^-6};
ub = {500, 25, 1, 0.038}/500
b = N[1 - ub]
data = {R[[#]], b[[#]]} & /@ Range[Length[R]];


with the following function

(ka r)^h/(1 + (ka r)^h)


using FindFit:

FindFit[data, Abs[(ka r)]^h/(1 + Abs[(ka r)]^h), {h, ka}, r]


I get ridiculous results if I use Abs[] or no Abs[] and if I keep my zero data point or not, or if I change "Method"

I was originally using NonlinearModelFit, except the results it was giving me were oddly written. I've tried all the Methods -> options and none are yielding good models, whether or not I use Abs[] or not, or have an initial guess. If I use an initial guess the results I get returned are using my initial guess, which isn't right.

Does anyone have suggestions for picking Method + using Abs[] to appropriately find ka and h in this model?

• Don't blame NonlinearModelFit or Methods->. Blame whoever taught you Statistics. You are attempting to fit 3 parameters (h, ka, and the error variance) with only 4 data points. – JimB May 5 at 5:25

I'm not exactly sure why FindFit has a problem with this one, so maybe others can help with that aspect. I get the error

The gradient is not a vector of real numbers at {h, ka} = {1.5, 1.x10^6}

Since your function evaluates to a real number for positive ka, r, and h, I don't think Abs is necessary. I tried using NMinimize instead. I've seen a few people on this site use it instead of the fitting functions to great effect, so I've started to use it more often myself.

model[ka_, r_, h_] := (ka r)^h / (1 + (ka r)^h)

{rmse, params} = NMinimize[
Sqrt[Mean[(model[ka, data[[All, 1]], h] - data[[All, 2]])^2]],
10^6 < ka < 10^10,
0.5 < h},
{ka, h}
]
(* {1.59966*10^-7, {ka -> 7.96023*10^8, h -> 1.41938}} *)

Show[
Plot[
model[ka, r, h] /. params,
{r, 0, 10^-6},
ImageSize -> 500,
PlotRange -> Full,
PlotStyle -> ColorData[75][5]
],
ListPlot[
data,
PlotStyle -> Directive[ColorData[85][1], AbsolutePointSize[8]]
]
]


In the second plot, I added ScalingFunctions -> {"Log", "Log"} to both Plot and ListPlot. Sometimes log-log plots show anomalies that might be hidden by linear plots.

It looks to me like this gets us a fairly good fit, and the root-mean-square-error is on the order of $$10^{-7}$$, which seems pretty decent.