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)