def loglike(self, endog, mu, freq_weights=1., scale=1.):
        r"""
        The log-likelihood function in terms of the fitted mean response.

        Parameters
        ----------
        endog : array-like
            Endogenous response variable
        mu : array-like
            Fitted mean response variable
        freq_weights : array-like
            1d array of frequency weights. The default is 1.
        scale : float, optional
            The default is 1.

        Returns
        -------
        llf : float
            The value of the loglikelihood function evaluated at
            (endog,mu,freq_weights,scale) as defined below.

        Notes
        --------
        .. math::

           llf = -1 / scale * \sum_i *(Y_i / \mu_i+ \log(\mu_i)+
                 (scale -1) * \log(Y) + \log(scale) + scale *
                 \ln \Gamma(1 / scale))
        """
        return - 1./scale * np.sum((endog/mu + np.log(mu) + (1 - scale) *
                                    np.log(endog) + np.log(scale) + scale *
                                   special.gammaln(1./scale)) * freq_weights)

        # in Stata scale is set to equal 1 for reporting llf
        # in R it's the dispersion, though there is a loss of precision vs.
        # our results due to an assumed difference in implementation
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/genmod/families/family.py/Gamma.loglike/swapped operands of `-`