def test_logistic_lmm(): df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv")) model = Lmer("DV_l ~ IV1+ (IV1|Group)", data=df, family="binomial") model.fit(summarize=False) assert model.coefs.shape == (2, 13) estimates = np.array([-0.16098421, 0.00296261]) assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001) assert isinstance(model.fixef, pd.core.frame.DataFrame) assert model.fixef.shape == (47, 2) assert isinstance(model.ranef, pd.core.frame.DataFrame) assert model.ranef.shape == (47, 2) assert np.allclose(model.coefs.loc[:, "Estimate"], model.fixef.mean(), atol=0.01) # Test prediction assert np.allclose(model.predict(model.data, use_rfx=True), model.data.fits) assert np.allclose( model.predict(model.data, use_rfx=True, pred_type="link"), logit(model.data.fits), ) # Test RFX only model = Lmer("DV_l ~ 0 + (IV1|Group)", data=df, family="binomial") model.fit(summarize=False) assert model.fixef.shape == (47, 2) model = Lmer("DV_l ~ 0 + (IV1|Group) + (1|IV3)", data=df, family="binomial") model.fit(summarize=False) assert isinstance(model.fixef, list) assert model.fixef[0].shape == (47, 2) assert model.fixef[1].shape == (3, 2)
def test_logistic_lmm(): df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv')) model = Lmer('DV_l ~ IV1+ (IV1|Group)', data=df, family='binomial') model.fit(summarize=False) assert model.coefs.shape == (2, 13) estimates = np.array([-0.16098421, 0.00296261]) assert np.allclose(model.coefs['Estimate'], estimates, atol=.001) assert isinstance(model.fixef, pd.core.frame.DataFrame) assert model.fixef.shape == (47, 2) assert isinstance(model.ranef, pd.core.frame.DataFrame) assert model.ranef.shape == (47, 2) assert np.allclose(model.coefs.loc[:, 'Estimate'], model.fixef.mean(), atol=.01) # Test prediction assert np.allclose(model.predict(model.data, use_rfx=True), model.data.fits) assert np.allclose( model.predict(model.data, use_rfx=True, pred_type='link'), logit(model.data.fits))
def test_gaussian_lmm(): df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv")) model = Lmer("DV ~ IV3 + IV2 + (IV2|Group) + (1|IV3)", data=df) opt_opts = "optimizer='Nelder_Mead', optCtrl = list(FtolAbs=1e-8, XtolRel=1e-8)" model.fit(summarize=False, control=opt_opts) assert model.coefs.shape == (3, 8) estimates = np.array([12.04334602, -1.52947016, 0.67768509]) assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001) assert isinstance(model.fixef, list) assert model.fixef[0].shape == (47, 3) assert model.fixef[1].shape == (3, 3) assert isinstance(model.ranef, list) assert model.ranef[0].shape == (47, 2) assert model.ranef[1].shape == (3, 1) assert model.ranef_corr.shape == (1, 3) assert model.ranef_var.shape == (4, 3) assert np.allclose(model.coefs.loc[:, "Estimate"], model.fixef[0].mean(), atol=0.01) # Test prediction assert np.allclose(model.predict(model.data, use_rfx=True), model.data.fits) # Smoketest for simulate model.simulate(2) model.simulate(2, use_rfx=True) # Smoketest for old_optimizer model.fit(summarize=False, old_optimizer=True)
def test_gaussian_lmm(): df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv')) model = Lmer('DV ~ IV3 + IV2 + (IV2|Group) + (1|IV3)', data=df) model.fit(summarize=False) assert model.coefs.shape == (3, 8) estimates = np.array([12.04334602, -1.52947016, 0.67768509]) assert np.allclose(model.coefs['Estimate'], estimates, atol=.001) assert isinstance(model.fixef, list) assert model.fixef[0].shape == (47, 3) assert model.fixef[1].shape == (3, 3) assert isinstance(model.ranef, list) assert model.ranef[0].shape == (47, 2) assert model.ranef[1].shape == (3, 1) assert model.ranef_corr.shape == (1, 3) assert model.ranef_var.shape == (4, 3) assert np.allclose(model.coefs.loc[:, 'Estimate'], model.fixef[0].mean(), atol=.01) # Test prediction assert np.allclose(model.predict(model.data, use_rfx=True), model.data.fits)
def test_gaussian_lmm(): df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv")) model = Lmer("DV ~ IV3 + IV2 + (IV2|Group) + (1|IV3)", data=df) opt_opts = "optimizer='Nelder_Mead', optCtrl = list(FtolAbs=1e-8, XtolRel=1e-8)" model.fit(summarize=False, control=opt_opts) assert model.coefs.shape == (3, 8) estimates = np.array([12.04334602, -1.52947016, 0.67768509]) assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001) assert isinstance(model.fixef, list) assert (model.fixef[0].index.astype(int) == df.Group.unique()).all() assert (model.fixef[1].index.astype(float) == df.IV3.unique()).all() assert model.fixef[0].shape == (47, 3) assert model.fixef[1].shape == (3, 3) assert isinstance(model.ranef, list) assert model.ranef[0].shape == (47, 2) assert model.ranef[1].shape == (3, 1) assert (model.ranef[1].index == ["0.5", "1", "1.5"]).all() assert model.ranef_corr.shape == (1, 3) assert model.ranef_var.shape == (4, 3) assert np.allclose(model.coefs.loc[:, "Estimate"], model.fixef[0].mean(), atol=0.01) # Test prediction assert np.allclose(model.predict(model.data, use_rfx=True), model.data.fits) # Test simulate out = model.simulate(2) assert isinstance(out, pd.DataFrame) assert out.shape == (model.data.shape[0], 2) out = model.simulate(2, use_rfx=True) assert isinstance(out, pd.DataFrame) assert out.shape == (model.data.shape[0], 2) # Smoketest for old_optimizer model.fit(summarize=False, old_optimizer=True) # test fixef code for 1 fixed effect model = Lmer("DV ~ IV3 + IV2 + (IV2|Group)", data=df) model.fit(summarize=False, control=opt_opts) assert (model.fixef.index.astype(int) == df.Group.unique()).all() assert model.fixef.shape == (47, 3) assert np.allclose(model.coefs.loc[:, "Estimate"], model.fixef.mean(), atol=0.01) # test fixef code for 0 fixed effects model = Lmer("DV ~ (IV2|Group) + (1|IV3)", data=df) model.fit(summarize=False, control=opt_opts) assert isinstance(model.fixef, list) assert (model.fixef[0].index.astype(int) == df.Group.unique()).all() assert (model.fixef[1].index.astype(float) == df.IV3.unique()).all() assert model.fixef[0].shape == (47, 2) assert model.fixef[1].shape == (3, 2)
"base_atom_order ~ 1.0 + uid_b_a_logit + rig_b_a_logit + (1.0|language_family) + (1.0|Subfamily)", data=df_uid, family="binomial") #model = Lmer("base_atom_order ~ rig_b_a_prob + (rig_b_a_prob|language_family) + (rig_b_a_prob|Subfamily)", data=df) model_uid_fit = model_uid.fit() model_rig_fit = model_rig.fit() model_total_fit = model_total.fit() print(model_total_fit) model_total_fit.plot_summary() assert False #table = anova_lm(model_uid.model_obj, model_rig.model_obj) #print(table) #assert False model_preds_uid = model_uid.predict(df_uid) model_preds_rig = model_rig.predict(df_rig) error_rig = model_preds_rig - df_rig["base_atom_order"] error_uid = model_preds_uid - df_uid["base_atom_order"] SE = np.square(error_rig) # squared errors SE_uid = np.square(error_uid) MSE = np.mean(SE) # mean squared errors MSE_uid = np.mean(SE_uid) # mean squared errors RMSE = np.sqrt(MSE) # Root Mean Squared Error, RMSE RMSE_uid = np.sqrt(MSE_uid) # Root Mean Squared Error, RMSE F = np.var(error_uid) / np.var(error_rig) Rsquared = 1.0 - (np.var(error_rig) / np.var(df_rig["base_atom_order"])) Rsquared_uid = 1.0 - (np.var(error_uid) /