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)
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)
