Is there any way where we can calculate parameter covariance matrix instead of correlation matrix in python lmfit package

Question

I have re-parameterize Arrhenius equation of form k = kref*exp(-E/R((1/T)-(1/Tref)) and i wanted to estimate the values of parameters E & kref which i got from lmfit package and correlations between it too. However the whole idea of re-parameterization was to see if we are getting low correlation between k0 and E after re-parameterization of original Arrhenius equation i.e. k = ko*exp(-E/RT) where kref = ko*exp(-E/RTref) so to do that i got following relation

                     Cov(ko,E)/k0  = Var(E)/RTref  -  Cov(Kref,E)/kref

So my question is that is there any way we can find Var(E) and also standard deviation of kref??

Answer 1

I'm not certain I understand what you are asking - your notation is not clear. It is really always better to provide a minimal and complete example of working code.

The fit result from lmfit does include the covariance matrix (covar) between the variable parameters. Is that what you are looking for?