def _cov_params_approx(self,
approx_complex_step=True,
approx_centered=False):
evaluated_hessian = self.nobs_effective * self.model.hessian(
params=self.params,
transformed=True,
includes_fixed=True,
method="approx",
approx_complex_step=approx_complex_step,
approx_centered=approx_centered,
)
# TODO: Case with "not approx_complex_step" is not hit in
# tests as of 2017-05-19
if len(self.fixed_params) > 0:
mask = np.ix_(self._free_params_index, self._free_params_index)
if len(self.fixed_params) < self.k_params:
(tmp, singular_values) = pinv_extended(evaluated_hessian[mask])
else:
tmp, singular_values = np.nan, [np.nan]
neg_cov = np.zeros_like(evaluated_hessian) * np.nan
neg_cov[mask] = tmp
else:
(neg_cov, singular_values) = pinv_extended(evaluated_hessian)
self.model.update(self.params, transformed=True, includes_fixed=True)
if self._rank is None:
self._rank = np.linalg.matrix_rank(np.diag(singular_values))
return -neg_cov
def test_extendedpinv(self):
X = standard_normal((40, 10))
np_inv = np.linalg.pinv(X)
np_sing_vals = np.linalg.svd(X, 0, 0)
sm_inv, sing_vals = pinv_extended(X)
assert_almost_equal(np_inv, sm_inv)
assert_almost_equal(np_sing_vals, sing_vals)
def test_extendedpinv(self):
X = standard_normal((40, 10))
np_inv = np.linalg.pinv(X)
np_sing_vals = np.linalg.svd(X, 0, 0)
sm_inv, sing_vals = pinv_extended(X)
assert_almost_equal(np_inv, sm_inv)
assert_almost_equal(np_sing_vals, sing_vals)