def test_cell_means_with_random_intercepts(crossed_data):
# using formula
model0 = Model(crossed_data)
model0.fit('Y ~ 0 + threecats', random=['subj'], run=False)
model0.build()
model0.fit(samples=1)
# using add_term
model1 = Model(crossed_data, intercept=False)
model1.add_y('Y')
model1.add_term('threecats', categorical=True, drop_first=False)
model1.add_term('subj', categorical=True, random=True, drop_first=False)
model1.build()
model1.fit(samples=1)
# check that they have the same random terms
assert set(model0.random_terms) == set(model1.random_terms)
# check that fixed effect design matries are the same,
# even if term names / level names / order of columns is different
X0 = set([tuple(t.data[:,lev]) for t in model0.fixed_terms.values() for lev in range(len(t.levels))])
X1 = set([tuple(t.data[:,lev]) for t in model1.fixed_terms.values() for lev in range(len(t.levels))])
assert X0 == X1
# check that add_formula and add_term models have same priors for fixed effects
priors0 = {x.name:x.prior.args for x in model0.terms.values() if not x.random}
priors1 = {x.name:x.prior.args for x in model1.terms.values() if not x.random}
assert set(priors0) == set(priors1)
# check that add_formula and add_term models have same priors for random effects
priors0 = {x.name:x.prior.args['sd'].args for x in model0.terms.values() if x.random}
priors1 = {x.name:x.prior.args['sd'].args for x in model1.terms.values() if x.random}
assert set(priors0) == set(priors1)
def test_cell_means_with_covariate(crossed_data):
# build model using formula
model0 = Model(crossed_data)
model0.fit('Y ~ 0 + threecats + continuous', run=False)
model0.build()
model0.fit(samples=1)
# build model using add_term
model1 = Model(crossed_data)
model1.add_y('Y')
model1.add_term('threecats', drop_first=False)
model1.add_term('continuous')
model1.build()
model1.fit(samples=1)
# check that design matries are the same,
# even if term names / level names / order of columns is different
X0 = set([tuple(t.data[:,lev]) for t in model0.fixed_terms.values() for lev in range(len(t.levels))])
X1 = set([tuple(t.data[:,lev]) for t in model1.fixed_terms.values() for lev in range(len(t.levels))])
assert X0 == X1
# check that threecats priors have finite variance
assert not any(np.isinf(model0.terms['threecats'].prior.args['sd']))
# check that add_formula and add_term models have same priors for fixed effects
priors0 = {x.name:x.prior.args for x in model0.terms.values() if not x.random}
priors1 = {x.name:x.prior.args for x in model1.terms.values() if not x.random}
assert set(priors0) == set(priors1)
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_slope_only_model(crossed_data):
# using formula
model0 = Model(crossed_data)
model0.fit('Y ~ 0 + continuous', run=False)
model0.build()
model0.fit(samples=1)
# using add_term
model1 = Model(crossed_data)
model1.add_y('Y')
model1.add_term('continuous')
model1.build()
model1.fit(samples=1)
# check that term names agree
assert set(model0.term_names) == set(model1.term_names)
# check that add_formula and add_term models have same priors for fixed effects
priors0 = {x.name:x.prior.args for x in model0.terms.values() if not x.random}
priors1 = {x.name:x.prior.args for x in model1.terms.values() if not x.random}
assert set(priors0) == set(priors1)
def test_many_fixed_effects(crossed_data):
# build model using formula
model0 = Model(crossed_data)
model0.fit('Y ~ continuous + dummy + threecats', run=False)
model0.build()
model0.fit(samples=1)
# build model using add_term
model1 = Model(crossed_data)
model1.add_y('Y')
model1.add_intercept()
model1.add_term('continuous')
model1.add_term('dummy')
model1.add_term('threecats')
model1.build()
model1.fit(samples=1)
# check that term names agree
assert set(model0.term_names) == set(model1.term_names)
# check that design matries are the same,
# even if term names / level names / order of columns is different
X0 = set([tuple(t.data[:,lev]) for t in model0.fixed_terms.values() for lev in range(len(t.levels))])
X1 = set([tuple(t.data[:,lev]) for t in model1.fixed_terms.values() for lev in range(len(t.levels))])
assert X0 == X1
# check that add_formula and add_term models have same priors for fixed effects
priors0 = {x.name:x.prior.args for x in model0.terms.values() if not x.random}
priors1 = {x.name:x.prior.args for x in model1.terms.values() if not x.random}
assert set(priors0) == set(priors1)
def test_update_term_priors_after_init(diabetes_data):
model = Model(diabetes_data)
model.add_term('BMI')
model.add_term('S1')
model.add_term('age_grp', random=True, over='BP')
p1 = Prior('Normal', mu=-10, sd=10)
p2 = Prior('Beta', alpha=2, beta=2)
model.set_priors({'BMI': 0.3, 'S1': p2})
assert model.terms['S1'].prior.args['beta'] == 2
assert model.terms['BMI'].prior == 0.3
model.set_priors({('S1', 'BMI'): p1})
assert model.terms['S1'].prior.args['sd'] == 10
assert model.terms['BMI'].prior.args['mu'] == -10
p3 = Prior('Normal', mu=0, sd=Prior('Normal', mu=0, sd=7))
model.set_priors(fixed=0.4, random=p3)
assert model.terms['BMI'].prior == 0.4
assert model.terms['age_grp|BP'].prior.args['sd'].args['sd'] == 7
def test_random_intercepts(crossed_data):
# using formula and '1|' syntax
model0 = Model(crossed_data)
model0.fit('Y ~ continuous', random=['1|subj','1|item','1|site'], run=False)
model0.build()
# model0.fit(samples=1)
# # using formula and 'subj' syntax
# model1 = Model(crossed_data)
# model1.fit('Y ~ continuous', random=['subj','item','site'], run=False)
# model1.build()
# # model1.fit(samples=1)
# # check that they have the same random terms
# assert set(model1.random_terms) == set(model0.random_terms)
# using add_term
model2 = Model(crossed_data)
model2.add_y('Y')
model2.add_intercept()
model2.add_term('continuous')
model2.add_term('subj', random=True)
model2.add_term('item', random=True)
model2.add_term('site', random=True)
model2.build()
# model2.fit(samples=1)
# check that this has the same random terms as above
assert set(model0.random_terms) == set(model2.random_terms)
# check that add_formula and add_term models have same priors for fixed effects
priors0 = {x.name:x.prior.args for x in model0.terms.values() if not x.random}
priors2 = {x.name:x.prior.args for x in model2.terms.values() if not x.random}
assert set(priors0) == set(priors2)
# check that add_formula and add_term models have same priors for random effects
priors0 = {x.name:x.prior.args['sd'].args for x in model0.terms.values() if x.random}
priors2 = {x.name:x.prior.args['sd'].args for x in model2.terms.values() if x.random}
assert set(priors0) == set(priors2)
def test_model_term_names_property(diabetes_data):
model = Model(diabetes_data)
model.add_term('BMI')
model.add_term('BP')
model.add_term('S1')
assert model.term_names == ['BMI', 'BP', 'S1']
def test_cell_means_with_many_random_effects(crossed_data):
# build model using formula
model0 = Model(crossed_data)
model0.fit('Y ~ 0 + threecats',
random=['0+threecats|subj','continuous|item','dummy|item','threecats|site'], run=False)
model0.build()
# model0.fit(samples=1)
# build model using add_term
model1 = Model(crossed_data)
model1.add_y('Y')
# fixed effects
model1.add_term('threecats', drop_first=False)
# random effects
model1.add_term('threecats', over='subj', drop_first=False, random=True,
categorical=True)
model1.add_term('item', random=True, categorical=True)
model1.add_term('continuous', over='item', random=True)
model1.add_term('dummy', over='item', random=True)
model1.add_term('site', random=True, categorical=True)
model1.add_term('threecats', over='site', random=True, categorical=True)
model1.build()
# model1.fit(samples=1)
# check that the random effects design matrices have the same shape
X0 = pd.concat([pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.random_terms.values()], axis=1)
X1 = pd.concat([pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.random_terms.values()], axis=1)
assert X0.shape == X1.shape
# check that the random effect design matrix contain the same columns,
# even if term names / columns names / order of columns is different
X0_set = set(tuple(X0.iloc[:,i]) for i in range(len(X0.columns)))
X1_set = set(tuple(X1.iloc[:,i]) for i in range(len(X1.columns)))
assert X0_set == X1_set
# check that fixed effect design matries are the same,
# even if term names / level names / order of columns is different
X0 = set([tuple(t.data[:,lev]) for t in model0.fixed_terms.values() for lev in range(len(t.levels))])
X1 = set([tuple(t.data[:,lev]) for t in model1.fixed_terms.values() for lev in range(len(t.levels))])
assert X0 == X1
# check that add_formula and add_term models have same priors for fixed effects
priors0 = {x.name:x.prior.args for x in model0.terms.values() if not x.random}
priors1 = {x.name:x.prior.args for x in model1.terms.values() if not x.random}
assert set(priors0) == set(priors1)
# check that add_formula and add_term models have same priors for random effects
priors0 = {x.name:x.prior.args['sd'].args for x in model0.terms.values() if x.random}
priors1 = {x.name:x.prior.args['sd'].args for x in model1.terms.values() if x.random}
assert set(priors0) == set(priors1)