glimix_core/lmm/_lmm.py
def beta(self):
        """
        Fixed-effect sizes.

        Returns
        -------
        effect-sizes : numpy.ndarray
            Optimal fixed-effect sizes.

        Notes
        -----
        Setting the derivative of log(p(𝐲)) over effect sizes equal
        to zero leads to solutions 𝜷 from equation ::

            (QᵀX)ᵀD⁻¹(QᵀX)𝜷 = (QᵀX)ᵀD⁻¹(Qᵀ𝐲).
        """
        from numpy_sugar.linalg import rsolve

        return rsolve(self._X["VT"], rsolve(self._X["tX"], self.mean()))
def beta(self):
        """
        Fixed-effect sizes.

        Returns
        -------
        effect-sizes : numpy.ndarray
            Optimal fixed-effect sizes.

        Notes
        -----
        Setting the derivative of log(p(𝐲)) over effect sizes equal
        to zero leads to solutions 𝜷 from equation ::

            (QᵀX)ᵀD⁻¹(QᵀX)𝜷 = (QᵀX)ᵀD⁻¹(Qᵀ𝐲).
        """
        from numpy_sugar.linalg import rsolve

        return rsolve(self._X["VT"], rsolve(self._X["tX"], self.mean()))
def beta ( self ) : from numpy_sugar . linalg import rsolve return rsolve ( self . _X [ "VT" ] , rsolve ( self . _X [ "tX" ] , self . mean ( ) ) )
Fixed-effect sizes.

        Returns
        -------
        effect-sizes : numpy.ndarray
            Optimal fixed-effect sizes.

        Notes
        -----
        Setting the derivative of log(p(𝐲)) over effect sizes equal
        to zero leads to solutions 𝜷 from equation ::

            (QᵀX)ᵀD⁻¹(QᵀX)𝜷 = (QᵀX)ᵀD⁻¹(Qᵀ𝐲).
Fixed - effect sizes .