CODEWITHSUNDEEP
pythonconfidence-intervaluncertaintylmfit
sirius123
sirius123

Reputation: 123

LMFIT confidence interval uncertainty estimates error on python

the output error is : MinimizerException: Cannot determine Confidence Intervals without sensible uncertainty estimates

Why I got this error? How can I calculate uncertainty estimates and solve this problem??

for dosya1 in glob.glob("mean*"):
     data1=np.genfromtxt(dosya1, skip_header=0, skip_footer=0, names=["wavelength","mean"])
     x=data1["wavelength"]

     mod=VoigtModel()
     pars = mod.guess(y, x=x)
     pars['gamma'].set(value=0.7, vary=True, expr="")     
out=mod.fit(y,pars, x=x)

     pars=lmfit.Parameters()
     pars.add_many(('amp', out.params["amplitude"].value), ('sig', out.params["sigma"].value), ("gam",out.params["gamma"].value),("cent",out.params["center"].value))

 def residual(p):
     amp=p["amp"].value
     sig=p["sig"].value
     gam=p["gam"].value
     cent=p["cent"].value  
     return  ((wofz((x-cent + wofz(gam).imag)/(sig*(sqrt(2)))).real) / (sig*(sqrt(2))))- y

 mini = lmfit.Minimizer(residual, pars)
 result=mini.minimize()

 ci = lmfit.conf_interval(mini, result)
 lmfit.printfuncs.report_ci(ci)

Upvotes: 3

Views: 5271

Answers (1)

M Newville
M Newville

Reputation: 7862

You will get this error message if lmfit.minimize() (actually, leastsq(), which it calls) is unable to estimate uncertainties by inverting the curvature matrix. It uses these values (which are often very good estimates, BTW) as the scale for explicitly exploring parameter space. There are several possible reasons why leastsq() might fail to estimate uncertainties. Common reasons are that one or more of the variables is not found to alter the fit, or the residual contains NaNs.

It is hard to predict when this might happen. You should allow for the possibility and/or check that the initial fit succeeded and was able to make the initial estimate of the uncertainties (check result.errorbars) before calling conf_interval().

Upvotes: 4

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