def _llobs(self, endog, exog, exog_precision, params):
    '\n        Loglikelihood for observations with data arguments.\n\n        Parameters\n        ----------\n        endog : ndarray\n            1d array of endogenous variable.\n        exog : ndarray\n            2d array of explanatory variables.\n        exog_precision : ndarray\n            2d array of explanatory variables for precision.\n        params : ndarray\n            The parameters of the model, coefficients for linear predictors\n            of the mean and of the precision function.\n\n        Returns\n        -------\n        loglike : ndarray\n            The log likelihood for each observation of the model evaluated\n            at `params`.\n        '
    (y, X, Z) = (endog, exog, exog_precision)
    nz = Z.shape[1]
    params_mean = params[:(- nz)]
    params_prec = params[(- nz):]
    linpred = np.dot(X, params_mean)
    linpred_prec = np.dot(Z, params_prec)
    mu = self.link.inverse(linpred)
    phi = self.link_precision.inverse(linpred_prec)
    eps_lb = 1e-200
    alpha = np.clip((mu * phi), eps_lb, np.inf)
    beta = np.clip(((1 - mu) * phi), eps_lb, np.inf)
    ll = ((((lgamma(phi) - lgamma(alpha)) - lgamma(beta)) + (((mu * phi) - 1) * np.log(y))) + ((((1 - mu) * phi) - 1) * np.log((1 - y))))
    return ll
Loglikelihood for observations with data arguments.

Parameters
----------
endog : ndarray
    1d array of endogenous variable.
exog : ndarray
    2d array of explanatory variables.
exog_precision : ndarray
    2d array of explanatory variables for precision.
params : ndarray
    The parameters of the model, coefficients for linear predictors
    of the mean and of the precision function.

Returns
-------
loglike : ndarray
    The log likelihood for each observation of the model evaluated
    at `params`.
def _llobs(self, endog, exog, exog_precision, params):
    '\n        Loglikelihood for observations with data arguments.\n\n        Parameters\n        ----------\n        endog : ndarray\n            1d array of endogenous variable.\n        exog : ndarray\n            2d array of explanatory variables.\n        exog_precision : ndarray\n            2d array of explanatory variables for precision.\n        params : ndarray\n            The parameters of the model, coefficients for linear predictors\n            of the mean and of the precision function.\n\n        Returns\n        -------\n        loglike : ndarray\n            The log likelihood for each observation of the model evaluated\n            at `params`.\n        '
    (y, X, Z) = (endog, exog, exog_precision)
    nz = Z.shape[1]
    params_mean = params[:(- nz)]
    params_prec = params[(- nz):]
    linpred = np.dot(X, params_mean)
    linpred_prec = np.dot(Z, params_prec)
    mu = self.link.inverse(linpred)
    phi = self.link_precision.inverse(linpred_prec)
    eps_lb = 1e-200
    alpha = np.clip((mu * phi), eps_lb, np.inf)
    beta = np.clip(((1 - mu) * phi), eps_lb, np.inf)
    ll = ((((lgamma(phi) - lgamma(alpha)) - lgamma(beta)) + (((mu * phi) - 1) * np.log(y))) + ((((1 - mu) * phi) - 1) * np.log((1 - y))))
    return ll