Uncertainties in Vector Fitting fit parameters

[neutrinonerd3333]

Uncertainties in Vector Fitting fit parameters

[Vinc0110]

Hi Tony!
Welcome and congratulations for opening the thousandth issue on this project 🥇

Please clarify what you mean by uncertainty. I was initially thinking this is about random deviations between subsequent measurements (due to noise), resulting in deviations of the fitted models. But I'm assuming it is about the accuracy of the individual fits, i.e. the error between the fit and the data. If that's the case, are you aware of the function get_rms_error()?

Apparently, the various types of error calculations are often correlated or even equal:
https://stackoverflow.com/questions/40187517/getting-covariance-matrix-of-fitted-parameters-from-scipy-optimize-least-squares

In the final step of the vector fitting process in scikit-rf, the model residues are fitted using numpy.linalg.lstsq(), which also returns the sums of squared residuals. These are currently ignored in the code, but we could make them accessible for the user.

scikit-rf/skrf/vectorFitting.py

Lines 534 to 537 in 74ddb7f

	# solve least squares and obtain results as stack of real part vector and imaginary part vector
	x, residuals, rank, singular_vals = np.linalg.lstsq(np.vstack((A.real, A.imag)),
	np.hstack((freq_responses.real, freq_responses.imag)).transpose(),
	rcond=None)

Is this already what you need? It would also be possible to add new error functions other than rms error.

Regards,
Vincent