def test_no_model_error(self, df_c):
gtf = GTransportFormula(df_c,
exposure='A',
outcome='Y',
selection='S',
generalize=True)
with pytest.raises(ValueError):
gtf.fit()
def test_transport_conf(self, df_c):
gtf = GTransportFormula(df_c,
exposure='A',
outcome='Y',
selection='S',
generalize=False)
gtf.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L',
print_results=False)
gtf.fit()
npt.assert_allclose(gtf.risk_difference, 0.042574, atol=1e-5)
npt.assert_allclose(gtf.risk_ratio, 1.124257, atol=1e-4)
def test_generalize(self, df_r):
gtf = GTransportFormula(df_r,
exposure='A',
outcome='Y',
selection='S',
generalize=True)
gtf.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L',
print_results=False)
gtf.fit()
npt.assert_allclose(gtf.risk_difference, 0.064038, atol=1e-5)
npt.assert_allclose(gtf.risk_ratio, 1.203057, atol=1e-4)
def test_generalize_weight(self, df_c):
gtf = GTransportFormula(df_c,
exposure='A',
outcome='Y',
selection='S',
generalize=True,
weights='weight')
gtf.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L',
print_results=False)
gtf.fit()
npt.assert_allclose(gtf.risk_difference, 0.048949, atol=1e-5)
npt.assert_allclose(gtf.risk_ratio, 1.149556, atol=1e-4)