def test_transport(self, df_r): aipw = AIPSW(df_r, exposure='A', outcome='Y', selection='S', generalize=False) aipw.weight_model('L + W_sq', print_results=False) aipw.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L', print_results=False) aipw.fit() npt.assert_allclose(aipw.risk_difference, 0.05479, atol=1e-5) npt.assert_allclose(aipw.risk_ratio, 1.16352, atol=1e-4)
def test_transport_conf(self, df_iptw): aipw = AIPSW(df_iptw, exposure='A', outcome='Y', selection='S', generalize=False, weights='iptw') aipw.weight_model('L + W_sq', print_results=False) aipw.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L', print_results=False) aipw.fit() npt.assert_allclose(aipw.risk_difference, 0.041407, atol=1e-5) npt.assert_allclose(aipw.risk_ratio, 1.120556, atol=1e-4)
def test_no_model_error(self, df_c): aipw = AIPSW(df_c, exposure='A', outcome='Y', selection='S', generalize=True) with pytest.raises(ValueError): aipw.fit() aipw.weight_model('L', print_results=False) with pytest.raises(ValueError): aipw.fit() aipw = AIPSW(df_c, exposure='A', outcome='Y', selection='S', generalize=True) aipw.outcome_model('A + L') with pytest.raises(ValueError): aipw.fit()