def test_distribute_random_effect_over(diabetes_data):
# Random slopes
model = Model(diabetes_data)
model.add(random='C(age_grp)|BMI')
assert model.terms['C(age_grp)[T.1]|BMI'].data.shape == (442, 163)
# Nested or crossed random intercepts
model.reset()
model.add(random='0+C(age_grp)|BMI')
assert model.terms['C(age_grp)[0]|BMI'].data.shape == (442, 163)
def test_distribute_random_effect_over(diabetes_data):
# Random slopes
model = Model(diabetes_data)
model.add_term('age_grp', over='BMI', categorical=False, random=True)
assert model.terms['age_grp|BMI'].data.shape == (442, 163)
# Nested or crossed random intercepts
model.reset()
model.add_term('age_grp', over='BMI', categorical=True, random=True,
drop_first=False)
t = model.terms['age_grp|BMI'].data
assert isinstance(t, dict)
assert t['age_grp[0]'].shape == (442, 83)
def test_distribute_random_effect_over(diabetes_data):
# Random slopes
model = Model(diabetes_data)
model.add("BP ~ 1")
model.add(random="C(age_grp)|BMI")
model.build(backend="pymc")
assert model.terms["C(age_grp)[T.1]|BMI"].data.shape == (442, 163)
# Nested or crossed random intercepts
model.reset()
model.add("BP ~ 1")
model.add(random="0+C(age_grp)|BMI")
model.build(backend="pymc")
assert model.terms["C(age_grp)[0]|BMI"].data.shape == (442, 163)
def test_distribute_group_specific_effect_over(diabetes_data):
# 163 unique levels of BMI in diabetes_data
# With intercept
model = Model(diabetes_data)
model.fit("BP ~ (C(age_grp)|BMI)", run=False)
# Since intercept is present, it uses treatment encoding
lvls = sorted(list(diabetes_data["age_grp"].unique()))[1:]
for lvl in lvls:
assert model.terms[f"C(age_grp)[{lvl}]|BMI"].data.shape == (442, 163)
assert "1|BMI" in model.terms
# Without intercept
model.reset()
model.fit("BP ~ (0 + C(age_grp)|BMI)", run=False)
assert model.terms["C(age_grp)[0]|BMI"].data.shape == (442, 163)
assert model.terms["C(age_grp)[1]|BMI"].data.shape == (442, 163)
assert model.terms["C(age_grp)[2]|BMI"].data.shape == (442, 163)
assert not "1|BMI" in model.terms
