def summary(self):
        """
        Summary statistics describing the fit.

        Returns
        -------
        df : pd.DataFrame
            Contains columns coef, exp(coef), se(coef), z, p, lower, upper

        See Also
        --------
        ``print_summary``
        """
        ci = 1 - self.alpha
        lower_upper_bounds = self._compute_confidence_bounds_of_parameters()
        df = pd.DataFrame(index=self._fitted_parameter_names)
        df["coef"] = self._fitted_parameters_
        df["se(coef)"] = self._compute_standard_errors().loc["se"]
        df["lower %g" % ci] = lower_upper_bounds["lower-bound"]
        df["upper %g" % ci] = lower_upper_bounds["upper-bound"]
        df["p"] = self._compute_p_values()
        with np.errstate(invalid="ignore", divide="ignore"):
            df["-log2(p)"] = -np.log2(df["p"])
        return df
def summary ( self ) : ci = 1 - self . alpha lower_upper_bounds = self . _compute_confidence_bounds_of_parameters ( ) df = pd . DataFrame ( index = self . _fitted_parameter_names ) df [ "coef" ] = self . _fitted_parameters_ df [ "se(coef)" ] = self . _compute_standard_errors ( ) . loc [ "se" ] df [ "lower %g" % ci ] = lower_upper_bounds [ "lower-bound" ] df [ "upper %g" % ci ] = lower_upper_bounds [ "upper-bound" ] df [ "p" ] = self . _compute_p_values ( ) with np . errstate ( invalid = "ignore" , divide = "ignore" ) : df [ "-log2(p)" ] = - np . log2 ( df [ "p" ] ) return df
Summary statistics describing the fit.

        Returns
        -------
        df : pd.DataFrame
            Contains columns coef, exp(coef), se(coef), z, p, lower, upper

        See Also
        --------
        ``print_summary``