glimix_core/lmm/_lmm.py
def fit(self, verbose=True):
        """
        Maximise the marginal likelihood.

        Parameters
        ----------
        verbose : bool, optional
            ``True`` for progress output; ``False`` otherwise.
            Defaults to ``True``.
        """
        if not self._isfixed("logistic"):
            self._maximize_scalar(desc="LMM", rtol=1e-6, atol=1e-6, verbose=verbose)

        if not self._fix["beta"]:
            self._update_beta()

        if not self._fix["scale"]:
            self._update_scale()
def fit(self, verbose=True):
        """
        Maximise the marginal likelihood.

        Parameters
        ----------
        verbose : bool, optional
            ``True`` for progress output; ``False`` otherwise.
            Defaults to ``True``.
        """
        if not self._isfixed("logistic"):
            self._maximize_scalar(desc="LMM", rtol=1e-6, atol=1e-6, verbose=verbose)

        if not self._fix["beta"]:
            self._update_beta()

        if not self._fix["scale"]:
            self._update_scale()
def fit ( self , verbose = True ) : if not self . _isfixed ( "logistic" ) : self . _maximize_scalar ( desc = "LMM" , rtol = 1e-6 , atol = 1e-6 , verbose = verbose ) if not self . _fix [ "beta" ] : self . _update_beta ( ) if not self . _fix [ "scale" ] : self . _update_scale ( )
Maximise the marginal likelihood.

        Parameters
        ----------
        verbose : bool, optional
            ``True`` for progress output; ``False`` otherwise.
            Defaults to ``True``.
Maximise the marginal likelihood .