def predict(self, params, exog=None, exog_precision=None, which='mean'):
    'Predict values for mean or precision\n\n        Parameters\n        ----------\n        params : array_like\n            The model parameters.\n        exog : array_like\n            Array of predictor variables for mean.\n        exog_precision : array_like\n            Array of predictor variables for precision parameter.\n        which : str\n\n            - "mean" : mean, conditional expectation E(endog | exog)\n            - "precision" : predicted precision\n            - "linear" : linear predictor for the mean function\n            - "linear-precision" : linear predictor for the precision parameter\n\n        Returns\n        -------\n        ndarray, predicted values\n        '
    if (which == 'linpred'):
        which = 'linear'
    if (which in ['linpred_precision', 'linear_precision']):
        which = 'linear-precision'
    k_mean = self.exog.shape[1]
    if (which in ['mean', 'linear']):
        if (exog is None):
            exog = self.exog
        params_mean = params[:k_mean]
        linpred = np.dot(exog, params_mean)
        if (which == 'mean'):
            mu = self.link.inverse(linpred)
            res = mu
        else:
            res = linpred
    elif (which in ['precision', 'linear-precision']):
        if (exog_precision is None):
            exog_precision = self.exog_precision
        params_prec = params[k_mean:]
        linpred_prec = np.dot(exog_precision, params_prec)
        if (which == 'precision'):
            phi = self.link_precision.inverse(linpred_prec)
            res = phi
        else:
            res = linpred_prec
    elif (which == 'var'):
        res = self._predict_var(params, exog=exog, exog_precision=exog_precision)
    else:
        raise ValueError(('which = %s is not available' % which))
    return res
Predict values for mean or precision

Parameters
----------
params : array_like
    The model parameters.
exog : array_like
    Array of predictor variables for mean.
exog_precision : array_like
    Array of predictor variables for precision parameter.
which : str

    - "mean" : mean, conditional expectation E(endog | exog)
    - "precision" : predicted precision
    - "linear" : linear predictor for the mean function
    - "linear-precision" : linear predictor for the precision parameter

Returns
-------
ndarray, predicted values
def predict(self, params, exog=None, exog_precision=None, which='mean'):
    'Predict values for mean or precision\n\n        Parameters\n        ----------\n        params : array_like\n            The model parameters.\n        exog : array_like\n            Array of predictor variables for mean.\n        exog_precision : array_like\n            Array of predictor variables for precision parameter.\n        which : str\n\n            - "mean" : mean, conditional expectation E(endog | exog)\n            - "precision" : predicted precision\n            - "linear" : linear predictor for the mean function\n            - "linear-precision" : linear predictor for the precision parameter\n\n        Returns\n        -------\n        ndarray, predicted values\n        '
    if (which == 'linpred'):
        which = 'linear'
    if (which in ['linpred_precision', 'linear_precision']):
        which = 'linear-precision'
    k_mean = self.exog.shape[1]
    if (which in ['mean', 'linear']):
        if (exog is None):
            exog = self.exog
        params_mean = params[:k_mean]
        linpred = np.dot(exog, params_mean)
        if (which == 'mean'):
            mu = self.link.inverse(linpred)
            res = mu
        else:
            res = linpred
    elif (which in ['precision', 'linear-precision']):
        if (exog_precision is None):
            exog_precision = self.exog_precision
        params_prec = params[k_mean:]
        linpred_prec = np.dot(exog_precision, params_prec)
        if (which == 'precision'):
            phi = self.link_precision.inverse(linpred_prec)
            res = phi
        else:
            res = linpred_prec
    elif (which == 'var'):
        res = self._predict_var(params, exog=exog, exog_precision=exog_precision)
    else:
        raise ValueError(('which = %s is not available' % which))
    return res