def test_nested_linear(self):
family = Gaussian()
endog, exog, group = load_data("gee_nested_linear_1.csv")
group_n = []
for i in range(endog.shape[0] // 10):
group_n.extend([
0,
] * 5)
group_n.extend([
1,
] * 5)
group_n = np.array(group_n)[:, None]
dp = Independence()
md = GEE(endog, exog, group, None, family, dp)
mdf1 = md.fit()
# From statsmodels.GEE (not an independent test)
cf = np.r_[-0.1671073, 1.00467426, -2.01723004, 0.97297106]
se = np.r_[0.08629606, 0.04058653, 0.04067038, 0.03777989]
assert_almost_equal(mdf1.params, cf, decimal=6)
assert_almost_equal(mdf1.standard_errors(), se, decimal=6)
ne = Nested()
md = GEE(endog, exog, group, None, family, ne, dep_data=group_n)
mdf2 = md.fit(start_params=mdf1.params)
# From statsmodels.GEE (not an independent test)
cf = np.r_[-0.16655319, 1.02183688, -2.00858719, 1.00101969]
se = np.r_[0.08632616, 0.02913582, 0.03114428, 0.02893991]
assert_almost_equal(mdf2.params, cf, decimal=6)
assert_almost_equal(mdf2.standard_errors(), se, decimal=6)
def gendat_nested1():
ns = Nested_simulator()
ns.error_sd = 2.
ns.params = np.r_[0, 1, 1.3, -0.8, -1.2]
ns.ngroups = 50
ns.nest_sizes = [10, 5]
ns.dparams = [1., 3.]
ns.simulate()
return ns, Nested(ns.id_matrix)
def gendat_nested0():
ns = Nested_simulator()
ns.error_sd = 1.
ns.params = np.r_[0., 1, 1, -1, -1]
ns.ngroups = 50
ns.nest_sizes = [10, 5]
ns.dparams = [2., 1.]
ns.simulate()
return ns, Nested(ns.id_matrix)
