def gendat_nominal():
ns = nominal_simulator()
# The last component of params must be identically zero
ns.params = [np.r_[0., 1], np.r_[-1., 0], np.r_[0., 0]]
ns.ngroups = 200
ns.dparams = [
1.,
]
ns.simulate()
data = np.concatenate((ns.endog[:, None], ns.exog, ns.group[:, None]),
axis=1)
ns.endog_ex, ns.exog_ex, ns.exog_ne, ns.nlevel = \
gee_setup_nominal(data, 0, [3,])
ns.group_ex = ns.exog_ne[:, 0]
va = GlobalOddsRatio(3, "nominal")
lhs = np.array([
[0., 1., 1, 0],
])
rhs = np.r_[0., ]
return ns, va, Multinomial(3), (lhs, rhs)
def test_nominal(self):
family = Multinomial(3)
endog_orig, exog_orig, groups = load_data("gee_nominal_1.csv", icept=False)
data = np.concatenate((endog_orig[:, None], exog_orig, groups[:, None]), axis=1)
# Recode as indicators
endog, exog, exog_ne, nlevel = gee_setup_nominal(data, 0, [3])
groups = exog_ne[:, 0]
# Test with independence correlation
v = Independence()
md = GEE(endog, exog, groups, None, family, v)
mdf1 = md.fit()
# From statsmodels.GEE (not an independent test)
cf1 = np.r_[0.44944752, 0.45569985, -0.92007064, -0.46766728]
se1 = np.r_[0.09801821, 0.07718842, 0.13229421, 0.08544553]
assert_almost_equal(mdf1.params, cf1, decimal=5)
assert_almost_equal(mdf1.standard_errors(), se1, decimal=5)
# Test with global odds ratio dependence
v = GlobalOddsRatio(nlevel, "nominal")
md = GEE(endog, exog, groups, None, family, v)
mdf2 = md.fit(start_params=mdf1.params)
# From statsmodels.GEE (not an independent test)
cf2 = np.r_[0.45397549, 0.42278345, -0.91997131, -0.50115943]
se2 = np.r_[0.09646057, 0.07405713, 0.1324629, 0.09025019]
assert_almost_equal(mdf2.params, cf2, decimal=5)
assert_almost_equal(mdf2.standard_errors(), se2, decimal=5)
def test_nominal(self):
family = Multinomial(3)
endog_orig, exog_orig, groups = load_data("gee_nominal_1.csv",
icept=False)
data = np.concatenate(
(endog_orig[:, None], exog_orig, groups[:, None]), axis=1)
# Recode as indicators
endog, exog, exog_ne, nlevel = gee_setup_nominal(data, 0, [
3,
])
groups = exog_ne[:, 0]
# Test with independence correlation
v = Independence()
md = GEE(endog, exog, groups, None, family, v)
mdf1 = md.fit()
# From statsmodels.GEE (not an independent test)
cf1 = np.r_[0.44944752, 0.45569985, -0.92007064, -0.46766728]
se1 = np.r_[0.09801821, 0.07718842, 0.13229421, 0.08544553]
assert_almost_equal(mdf1.params, cf1, decimal=5)
assert_almost_equal(mdf1.standard_errors(), se1, decimal=5)
# Test with global odds ratio dependence
v = GlobalOddsRatio(nlevel, "nominal")
md = GEE(endog, exog, groups, None, family, v)
mdf2 = md.fit(start_params=mdf1.params)
# From statsmodels.GEE (not an independent test)
cf2 = np.r_[0.45397549, 0.42278345, -0.91997131, -0.50115943]
se2 = np.r_[0.09646057, 0.07405713, 0.1324629, 0.09025019]
assert_almost_equal(mdf2.params, cf2, decimal=5)
assert_almost_equal(mdf2.standard_errors(), se2, decimal=5)
def gendat_nominal():
ns = nominal_simulator()
# The last component of params must be identically zero
ns.params = [np.r_[0., 1], np.r_[-1., 0], np.r_[0., 0]]
ns.ngroups = 200
ns.dparams = [1., ]
ns.simulate()
data = np.concatenate((ns.endog[:,None], ns.exog,
ns.group[:,None]), axis=1)
ns.endog_ex, ns.exog_ex, ns.exog_ne, ns.nlevel = \
gee_setup_nominal(data, 0, [3,])
ns.group_ex = ns.exog_ne[:,0]
va = GlobalOddsRatio(3, "nominal")
lhs = np.array([[0., 1., 1, 0],])
rhs = np.r_[0.,]
return ns, va, Multinomial(3), (lhs, rhs)
