glimix_core/lmm/_lmm_scan.py
def null_scale(self):
        """
        Optimal s according to the marginal likelihood.

        The optimal s is given by ::

            s = n⁻¹𝐲ᵀB(𝐲 - X𝜷),

        where 𝜷 is optimal.

        Returns
        -------
        scale : float
            Optimal scale.
        """
        n = self._nsamples
        beta = self.null_beta
        sqrdot = self._yTBy - dot(sum(self._yTBX), beta)
        return sqrdot / n
def null_scale(self):
        """
        Optimal s according to the marginal likelihood.

        The optimal s is given by ::

            s = n⁻¹𝐲ᵀB(𝐲 - X𝜷),

        where 𝜷 is optimal.

        Returns
        -------
        scale : float
            Optimal scale.
        """
        n = self._nsamples
        beta = self.null_beta
        sqrdot = self._yTBy - dot(sum(self._yTBX), beta)
        return sqrdot / n
def null_scale ( self ) : n = self . _nsamples beta = self . null_beta sqrdot = self . _yTBy - dot ( sum ( self . _yTBX ) , beta ) return sqrdot / n
Optimal s according to the marginal likelihood.

        The optimal s is given by ::

            s = n⁻¹𝐲ᵀB(𝐲 - X𝜷),

        where 𝜷 is optimal.

        Returns
        -------
        scale : float
            Optimal scale.
Optimal s according to the marginal likelihood .