def test_nominal(self):
family = Multinomial(3)
endog, exog, groups = load_data("gee_nominal_1.csv",
icept=False)
# Test with independence correlation
v = Independence()
md = NominalGEE(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("nominal")
md = NominalGEE(endog, exog, groups, None, family, v)
mdf2 = md.fit(start_params=mdf1.params)
# From statsmodels.GEE (not an independent test)
cf2 = np.r_[0.45448248, 0.41945568, -0.92008924, -0.50485758]
se2 = np.r_[0.09632274, 0.07433944, 0.13264646, 0.0911768]
assert_almost_equal(mdf2.params, cf2, decimal=5)
assert_almost_equal(mdf2.standard_errors(), se2, decimal=5)
def test_nominal(self):
endog, exog, groups = load_data("gee_nominal_1.csv",
icept=False)
# Test with independence correlation
va = Independence()
mod1 = NominalGEE(endog, exog, groups, cov_struct=va)
rslt1 = mod1.fit()
# Regression 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(rslt1.params, cf1, decimal=5)
assert_almost_equal(rslt1.standard_errors(), se1, decimal=5)
# Test with global odds ratio dependence
va = GlobalOddsRatio("nominal")
mod2 = NominalGEE(endog, exog, groups, cov_struct=va)
rslt2 = mod2.fit(start_params=rslt1.params)
# Regression test
cf2 = np.r_[0.45448248, 0.41945568, -0.92008924, -0.50485758]
se2 = np.r_[0.09632274, 0.07433944, 0.13264646, 0.0911768]
assert_almost_equal(rslt2.params, cf2, decimal=5)
assert_almost_equal(rslt2.standard_errors(), se2, decimal=5)
# Make sure we get the correct results type
assert_equal(type(rslt1), NominalGEEResultsWrapper)
assert_equal(type(rslt1._results), NominalGEEResults)
def test_nominal(self):
endog, exog, groups = load_data("gee_nominal_1.csv",
icept=False)
# Test with independence correlation
va = Independence()
mod1 = NominalGEE(endog, exog, groups, cov_struct=va)
rslt1 = mod1.fit()
# Regression test
cf1 = np.r_[0.450009, 0.451959, -0.918825, -0.468266]
se1 = np.r_[0.08915936, 0.07005046, 0.12198139, 0.08281258]
assert_allclose(rslt1.params, cf1, rtol=1e-5, atol=1e-5)
assert_allclose(rslt1.standard_errors(), se1, rtol=1e-5, atol=1e-5)
# Test with global odds ratio dependence
va = GlobalOddsRatio("nominal")
mod2 = NominalGEE(endog, exog, groups, cov_struct=va)
rslt2 = mod2.fit(start_params=rslt1.params)
# Regression test
cf2 = np.r_[0.455365, 0.415334, -0.916589, -0.502116]
se2 = np.r_[0.08803614, 0.06628179, 0.12259726, 0.08411064]
assert_allclose(rslt2.params, cf2, rtol=1e-5, atol=1e-5)
assert_allclose(rslt2.standard_errors(), se2, rtol=1e-5, atol=1e-5)
# Make sure we get the correct results type
assert_equal(type(rslt1), NominalGEEResultsWrapper)
assert_equal(type(rslt1._results), NominalGEEResults)
def test_wrapper(self):
endog, exog, groups = load_data("gee_nominal_1.csv", icept=False)
endog = pd.Series(endog, name='yendog')
exog = pd.DataFrame(exog)
groups = pd.Series(groups, name='the_group')
va = Independence()
mod = NominalGEE(endog, exog, groups, cov_struct=va)
rslt2 = mod.fit()
check_wrapper(rslt2)
def test_wrapper(self):
endog, exog, groups = load_data("gee_nominal_1.csv",
icept=False)
endog = pd.Series(endog, name='yendog')
exog = pd.DataFrame(exog)
groups = pd.Series(groups, name='the_group')
va = Independence()
mod = NominalGEE(endog, exog, groups, cov_struct=va)
rslt2 = mod.fit()
check_wrapper(rslt2)
def setup_class(cls):
endog, exog, groups = load_data("gee_nominal_1.csv", icept=False)
# Test with independence correlation
va = Independence()
cls.mod = NominalGEE(endog, exog, groups, cov_struct=va)
cls.start_params = np.array(
[0.44944752, 0.45569985, -0.92007064, -0.46766728])
def test_nominal(self):
family = Multinomial(3)
endog, exog, groups = load_data("gee_nominal_1.csv", icept=False)
# Test with independence correlation
va = Independence()
mod1 = NominalGEE(endog, exog, groups, None, family, va)
rslt1 = mod1.fit()
# Regression 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(rslt1.params, cf1, decimal=5)
assert_almost_equal(rslt1.standard_errors(), se1, decimal=5)
# Test with global odds ratio dependence
va = GlobalOddsRatio("nominal")
mod2 = NominalGEE(endog, exog, groups, None, family, va)
rslt2 = mod2.fit(start_params=rslt1.params)
# Regression test
cf2 = np.r_[0.45448248, 0.41945568, -0.92008924, -0.50485758]
se2 = np.r_[0.09632274, 0.07433944, 0.13264646, 0.0911768]
assert_almost_equal(rslt2.params, cf2, decimal=5)
assert_almost_equal(rslt2.standard_errors(), se2, decimal=5)
# Make sure we get the correct results type
assert_equal(type(rslt1), NominalGEEResultsWrapper)
assert_equal(type(rslt1._results), NominalGEEResults)
def test_nominal(self):
family = Multinomial(3)
endog, exog, groups = load_data("gee_nominal_1.csv", icept=False)
# Test with independence correlation
v = Independence()
md = NominalGEE(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("nominal")
md = NominalGEE(endog, exog, groups, None, family, v)
mdf2 = md.fit(start_params=mdf1.params)
# From statsmodels.GEE (not an independent test)
cf2 = np.r_[0.45448248, 0.41945568, -0.92008924, -0.50485758]
se2 = np.r_[0.09632274, 0.07433944, 0.13264646, 0.0911768]
assert_almost_equal(mdf2.params, cf2, decimal=5)
assert_almost_equal(mdf2.standard_errors(), se2, decimal=5)
def test_nominal(self):
endog, exog, groups = load_data("gee_nominal_1.csv",
icept=False)
# Test with independence correlation
va = Independence()
mod1 = NominalGEE(endog, exog, groups, cov_struct=va)
rslt1 = mod1.fit()
# Regression test
cf1 = np.r_[0.450009, 0.451959, -0.918825, -0.468266]
se1 = np.r_[0.08915936, 0.07005046, 0.12198139, 0.08281258]
assert_allclose(rslt1.params, cf1, rtol=1e-5, atol=1e-5)
assert_allclose(rslt1.standard_errors(), se1, rtol=1e-5, atol=1e-5)
# Test with global odds ratio dependence
va = GlobalOddsRatio("nominal")
mod2 = NominalGEE(endog, exog, groups, cov_struct=va)
rslt2 = mod2.fit(start_params=rslt1.params)
# Regression test
cf2 = np.r_[0.455365, 0.415334, -0.916589, -0.502116]
se2 = np.r_[0.08803614, 0.06628179, 0.12259726, 0.08411064]
assert_allclose(rslt2.params, cf2, rtol=1e-5, atol=1e-5)
assert_allclose(rslt2.standard_errors(), se2, rtol=1e-5, atol=1e-5)
# Make sure we get the correct results type
assert_equal(type(rslt1), NominalGEEResultsWrapper)
assert_equal(type(rslt1._results), NominalGEEResults)
