Measured parameters in a fit

I have been fitting data using a function defined with 10 measured parameters and 2 free ones that I want to read out.

So far I have assigned one of the measured parameters, say mp[0] to the bin at -1 which is not otherwise used, giving its measured value and measured error to that bin and making the fit function give the value of mp[0] it is using to that bin, and I left all the other measured parameters fixed so far.

The bin thing works and I could maybe just keep doing that but it would be messy to clean up plot outputs, I don’t know if it will work properly if I use a log likelihood fit, and it does not allow me to incorporate the co-variance of the 10 parameters in to the chi-squared. Is there a more reliable way to modify the chi-squared used for fitting depending on deviations from measured values of parameters? I was not able to find one so far.

The lines relevant to the fit are

TF1* f1 = new TF1(“CaliT”,mCalTime,-1,20,4);
TFitResultPtr fitres = multH->Fit(“CaliT”,“S”,"",-1,17)

as a separate function:
Double_t mCalTime(Double_t* x, Double_t* par)

This is the function where I currently pick values of a parameter fit them in the bin at -1.

Sorry for the late answer.
Can you post a small reproducer ?