Hi Anouk,
As we almost always suggest, it would be helpful to have a script that
shows more details of what you are doing.
We do not have an easy, general-purpose approach to including
uncertainties for a non-fitted parameter.
But, I think it may be possible to do this, at least in some cases.
That is, one might follow the approach discussed for including
uncertainties in the independent data, x, at
https://groups.google.com/g/lmfit-py/c/ijQ4fbYOMvE/m/ZfgAmXHnCQAJ
where one sets the uncertainty (or a portion of the uncertainty) in
the result to be
fixedpar_uncertainty * df / dfixedpar
Where 'fixedpar' is the fixed parameter. Unlike for an independent
variable x, a numerical derivative for a Parameter would not be
possible (the parameter has one value). But if you can figure out an
analytic derivative df/dfixedpar, then one might be able to use that
as the uncertainty used to weigh the fit.
--Matt
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