MATLAB: What is the best non-linear least square fitting method that will parameter error in addition to parameters

nonlinear sqyare fitparameter error

Hi,
I have an array A,
A=[296/296 0.08485182/0.08485182
296/463 0.070180715/0.08485182
296/681 0.055920654/0.08485182
296/894 0.042669196/0.08485182
296/1098 0.03980615/0.08485182
];
now i have fitted array A to an objective function objfcn = @(b,x) b(1).*x.^b(2) + b(3).*x.^b(4); as below:
B0 = ones(4,1);
[B,rsdnrm] = fminsearch(@(b) norm(A(:,2) – objfcn(b,A(:,1))), B0);
fprintf(1, 'c_1 = %12.6f\nc_2 = %12.6f\nn_1 = %12.6f\nn_2 = %12.6f\n', B)
and i am satisfied with the fit. However, fminsearch method does not give errors on parameters (b(1),b(2),b(3),b(4)). I tried other methods such as ''lsqnonlin'' and "lsqcurvefit ", but they do not reproduce the same parameters that i obtain from fminsearch. I was wondering if anyone knows a robust nonlinear least square fit method that is able to estimate parameter error?
Thank you all

Best Answer

If you have the Statistics and Machine Learning Toolbox, see if the fitnlm function will do what you want. Other options are nlinfit with nlparci.
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