def resid_anscombe(self, endog, mu):
        '''
        The Anscombe residuals

        Parameters
        ----------
        endog : array-like
            Endogenous response variable
        mu : array-like
            Fitted mean response variable

        Returns
        -------
        resid_anscombe : array
            The Anscombe residuals as defined below.

        Notes
        -----
        sqrt(n)*(cox_snell(endog)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.))

        where cox_snell is defined as
        cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.)
        where betainc is the incomplete beta function

        The name 'cox_snell' is idiosyncratic and is simply used for
        convenience following the approach suggested in Cox and Snell (1968).
        Further note that
        cox_snell(x) = x**(2/3.)/(2/3.)*hyp2f1(2/3.,1/3.,5/3.,x)
        where hyp2f1 is the hypergeometric 2f1 function.  The Anscombe
        residuals are sometimes defined in the literature using the
        hyp2f1 formulation.  Both betainc and hyp2f1 can be found in scipy.

        References
        ----------
        Anscombe, FJ. (1953) "Contribution to the discussion of H. Hotelling's
            paper." Journal of the Royal Statistical Society B. 15, 229-30.

        Cox, DR and Snell, EJ. (1968) "A General Definition of Residuals."
            Journal of the Royal Statistical Society B. 30, 248-75.

        '''
        cox_snell = lambda x: (special.betainc(3./2, 2/3., x)
                               * special.beta(2/3., 2/3.))
        return np.sqrt(self.n) * ((cox_snell(endog) - cox_snell(mu)) /
                                  (mu**(1/6.) * (1 - mu)**(1/6.)))
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/genmod/families/family.py/Binomial.resid_anscombe/swapped operands of `/`