def gendat_ordinal():
os = ordinal_simulator()
os.params = np.r_[0., 1]
os.ngroups = 200
os.thresholds = [1, 0, -1]
os.dparams = [1.,]
os.simulate()
data = np.concatenate((os.endog[:,None], os.exog,
os.group[:,None]), axis=1)
os.endog_ex, os.exog_ex, os.intercepts, os.nthresh = \
gee_setup_ordinal(data, 0)
os.group_ex = os.exog_ex[:,-1]
os.exog_ex = os.exog_ex[:,0:-1]
os.exog_ex = np.concatenate((os.intercepts, os.exog_ex),
axis=1)
va = GlobalOddsRatio(4, "ordinal")
lhs = np.array([[0., 0., 0, 1., 0.], [0., 0, 0, 0, 1]])
rhs = np.r_[0., 1]
return os, va, Binomial(), (lhs, rhs)
def gendat_ordinal():
os = ordinal_simulator()
os.params = np.r_[0., 1]
os.ngroups = 200
os.thresholds = [1, 0, -1]
os.dparams = [
1.,
]
os.simulate()
data = np.concatenate((os.endog[:, None], os.exog, os.group[:, None]),
axis=1)
os.endog_ex, os.exog_ex, os.intercepts, os.nthresh = \
gee_setup_ordinal(data, 0)
os.group_ex = os.exog_ex[:, -1]
os.exog_ex = os.exog_ex[:, 0:-1]
os.exog_ex = np.concatenate((os.intercepts, os.exog_ex), axis=1)
va = GlobalOddsRatio(4, "ordinal")
lhs = np.array([[0., 0., 0, 1., 0.], [0., 0, 0, 0, 1]])
rhs = np.r_[0., 1]
return os, va, Binomial(), (lhs, rhs)
def test_ordinal_pandas(self):
family = Binomial()
endog_orig, exog_orig, groups = load_data("gee_ordinal_1.csv",
icept=False)
data = np.concatenate(
(endog_orig[:, None], exog_orig, groups[:, None]), axis=1)
data = pd.DataFrame(data)
data.columns = ["endog", "x1", "x2", "x3", "x4", "x5", "group"]
# Recode as cumulative indicators
endog, exog, intercepts, nlevel = \
gee_setup_ordinal(data, "endog")
exog1 = np.concatenate((intercepts, exog), axis=1)
groups = exog1[:, -1]
exog1 = exog1[:, 0:-1]
v = GlobalOddsRatio(nlevel, "ordinal")
beta = gee_ordinal_starting_values(endog_orig, exog_orig.shape[1])
md = GEE(endog, exog1, groups, None, family, v)
mdf = md.fit(start_params=beta)
cf = np.r_[1.09238131, 0.02148193, -0.39879146, -0.01855666,
0.02983409, 1.18123172, 0.01845318, -1.10233886]
se = np.r_[0.10878752, 0.10326078, 0.11171241, 0.05488705, 0.05995019,
0.0916574, 0.05951445, 0.08539281]
assert_almost_equal(mdf.params, cf, decimal=2)
assert_almost_equal(mdf.bse, se, decimal=2)
def test_ordinal(self):
family = Binomial()
endog_orig, exog_orig, groups = load_data("gee_ordinal_1.csv",
icept=False)
data = np.concatenate((endog_orig[:,None], exog_orig,
groups[:,None]), axis=1)
# Recode as cumulative indicators
endog, exog, intercepts, nlevel = gee_setup_ordinal(data, 0)
exog1 = np.concatenate((intercepts, exog), axis=1)
groups = exog1[:,-1]
exog1 = exog1[:,0:-1]
v = GlobalOddsRatio(nlevel, "ordinal")
beta = gee_ordinal_starting_values(endog_orig,
exog_orig.shape[1])
md = GEE(endog, exog1, groups, None, family, v)
mdf = md.fit(start_params = beta)
cf = np.r_[1.09238131, 0.02148193, -0.39879146, -0.01855666,
0.02983409, 1.18123172, 0.01845318, -1.10233886]
se = np.r_[0.10878752, 0.10326078, 0.11171241, 0.05488705,
0.05995019, 0.0916574, 0.05951445, 0.08539281]
assert_almost_equal(mdf.params, cf, decimal=5)
assert_almost_equal(mdf.bse, se, decimal=5)
