def deriv(self, t, *args):
"""First derivative of the dependence function
implemented through numerical differentiation
"""
t = np.atleast_1d(t)
return _approx_fprime_cs_scalar(t, self.evaluate)
def deriv2(self, p):
"""Second derivative of the link function g''(p)
implemented through numerical differentiation
"""
from statsmodels.tools.numdiff import _approx_fprime_cs_scalar
return _approx_fprime_cs_scalar(p, self.deriv)
def _deriv_score_obs_dendog(self, params):
"""derivative of score_obs w.r.t. endog
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
derivative : ndarray_2d
The derivative of the score_obs with respect to endog.
"""
from statsmodels.tools.numdiff import _approx_fprime_cs_scalar
def f(y):
if y.ndim == 2 and y.shape[1] == 1:
y = y[:, 0]
sf = self.score_factor(params, endog=y)
return np.column_stack(sf)
dsf = _approx_fprime_cs_scalar(self.endog[:, None], f)
# deriv is 2d vector
d1 = dsf[:, :1] * self.exog
d2 = dsf[:, 1:2] * self.exog_precision
return np.column_stack((d1, d2))
def test_vectorized():
def f(x):
return 2 * x
desired = np.array([2, 2])
# vectorized parameter, column vector
p = np.array([[1, 2]]).T
assert_allclose(_approx_fprime_scalar(p, f), desired[:, None], rtol=1e-8)
assert_allclose(_approx_fprime_scalar(p.squeeze(), f), desired, rtol=1e-8)
assert_allclose(_approx_fprime_cs_scalar(p, f),
desired[:, None],
rtol=1e-8)
assert_allclose(_approx_fprime_cs_scalar(p.squeeze(), f),
desired,
rtol=1e-8)
# check 2-d row, see #7680
# not allowed/implemented for approx_fprime, raises broadcast ValueError
# assert_allclose(approx_fprime(p.T, f), desired, rtol=1e-8)
# similar as used in MarkovSwitching unit test
assert_allclose(approx_fprime_cs(p.T, f).squeeze(), desired, rtol=1e-8)