def _modif_newton(self, eta, est_vect, weights):
"""
Modified Newton's method for maximizing the log 'star' equation. This
function calls _fit_newton to find the optimal values of eta.
Parameters
----------
eta : ndarray, (1,m)
Lagrange multiplier in the profile likelihood maximization
est_vect : ndarray, (n,k)
Estimating equations vector
weights : 1darray
Observation weights
Returns
-------
params : 1xm array
Lagrange multiplier that maximizes the log-likelihood
"""
nobs = len(est_vect)
f = lambda x0: -np.sum(self._log_star(x0, est_vect, weights, nobs))
grad = lambda x0: -self._grad(x0, est_vect, weights, nobs)
hess = lambda x0: -self._hess(x0, est_vect, weights, nobs)
kwds = {'tol': 1e-8}
eta = eta.squeeze()
res = _fit_newton(f, grad, eta, (), kwds, hess=hess, maxiter=50, \
disp=0)
return res[0]
def _modif_newton(self, eta, est_vect, weights):
"""
Modified Newton's method for maximizing the log 'star' equation. This
function calls _fit_newton to find the optimal values of eta.
Parameters
----------
eta : ndarray, (1,m)
Lagrange multiplier in the profile likelihood maximization
est_vect : ndarray, (n,k)
Estimating equations vector
weights : 1darray
Observation weights
Returns
-------
params : 1xm array
Lagrange multiplier that maximizes the log-likelihood
"""
nobs = len(est_vect)
f = lambda x0: - np.sum(self._log_star(x0, est_vect, weights, nobs))
grad = lambda x0: - self._grad(x0, est_vect, weights, nobs)
hess = lambda x0: - self._hess(x0, est_vect, weights, nobs)
kwds = {'tol': 1e-8}
eta = eta.squeeze()
res = _fit_newton(f, grad, eta, (), kwds, hess=hess, maxiter=50, \
disp=0)
return res[0]