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feat: p-value calculation added to fit functions and fit result object

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Fabian Joswig 2022-01-10 15:00:47 +01:00
parent 88572c06a8
commit 5b831ec250
1 changed files with 5 additions and 0 deletions
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pyerrors/fits.py
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@ -7,6 +7,7 @@ import scipy.stats
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from matplotlib import gridspec from matplotlib import gridspec
from scipy.odr import ODR, Model, RealData from scipy.odr import ODR, Model, RealData
from scipy.stats import chi2
import iminuit import iminuit
from autograd import jacobian from autograd import jacobian
from autograd import elementwise_grad as egrad from autograd import elementwise_grad as egrad
@ -45,6 +46,8 @@ class Fit_result(Sequence):
my_str += 'residual variance = ' + f'{self.residual_variance:2.6f}' + '\n' my_str += 'residual variance = ' + f'{self.residual_variance:2.6f}' + '\n'
if hasattr(self, 'chisquare_by_expected_chisquare'): if hasattr(self, 'chisquare_by_expected_chisquare'):
my_str += '\u03C7\u00b2/\u03C7\u00b2exp = ' + f'{self.chisquare_by_expected_chisquare:2.6f}' + '\n' my_str += '\u03C7\u00b2/\u03C7\u00b2exp = ' + f'{self.chisquare_by_expected_chisquare:2.6f}' + '\n'
if hasattr(self, 'p_value'):
my_str += 'p-value = ' + f'{self.p_value:2.4f}' + '\n'
my_str += 'Fit parameters:\n' my_str += 'Fit parameters:\n'
for i_par, par in enumerate(self.fit_parameters): for i_par, par in enumerate(self.fit_parameters):
my_str += str(i_par) + '\t' + ' ' * int(par >= 0) + str(par).rjust(int(par < 0.0)) + '\n' my_str += str(i_par) + '\t' + ' ' * int(par >= 0) + str(par).rjust(int(par < 0.0)) + '\n'
@ -306,6 +309,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
output.dof = x.shape[-1] - n_parms output.dof = x.shape[-1] - n_parms
output.p_value = 1 - chi2.cdf(output.odr_chisquare, output.dof)
return output return output
@ -619,6 +623,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
output.chisquare = chisqfunc(fit_result.x) output.chisquare = chisqfunc(fit_result.x)
output.dof = x.shape[-1] - n_parms output.dof = x.shape[-1] - n_parms
output.p_value = 1 - chi2.cdf(output.chisquare, output.dof)
if kwargs.get('resplot') is True: if kwargs.get('resplot') is True:
residual_plot(x, y, func, result) residual_plot(x, y, func, result)