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lmfit / lmfit-py

Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize, and with many additional classes and methods for curve fitting.
https://lmfit.github.io/lmfit-py/
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During Triple Gaussian fitting, huge devise from initial guess value and high chi-square value #683

Closed sundar3492 closed 3 years ago

sundar3492 commented 3 years ago

I am tried Triple Gaussian fitting, there huge diverse from initial guess value and high chi-square value

How to resolve this problem.

## Warning: uncertainties could not be estimated:

and it has huge diverse from initial guess values and also very high chis-square value. I attached the output value below.

`[[Model]] ((Model(gaussian1) + Model(gaussian2)) + Model(gaussian3)) [[Fit Statistics]]

fitting method = leastsq

    # function evals   = 751
    # data points      = 89
    # variables        = 9
    chi-square         = 3715.94994
    reduced chi-square = 46.4493743
    Akaike info crit   = 350.126040
    Bayesian info crit = 372.523767
##  Warning: uncertainties could not be estimated:
[[Variables]]
    amp1: -7174.13129 (init = 23)
    cen1: -853.048883 (init = -1.5)
    wid1: -84.6651961 (init = 3.5)
    amp2: -189.857626 (init = 17)
    cen2:  3.47343596 (init = 1)
    wid2: -1.02072899 (init = 1.5)
    amp3:  111.911023 (init = 80)
    cen3: -0.65585443 (init = 3.5)
    wid3:  2.37279022 (init = 3)

`

newville commented 3 years ago

@sundar3492 have a hard time understanding how you could raise this issue and not only completely ignore but also erase the instructions for how to ask for help. At this point, I think your only hope is thoroughly reading all of the guides and documentation about asking for help and to ask a complete, well-formed question on the lmfit mailing list or some other forum and hope that someone will be kind enough to help you.