def predict(
self,
data: MRData = None,
slope_quantile: Dict[str, float] = None,
ref_cov: Tuple[str, Any] = None,
):
if data is None:
data = self.data1
data._sort_by_data_id()
pred1 = self.model1.predict(data, slope_quantile=slope_quantile, ref_cov=ref_cov)
return self.model2.predict(data) + pred1
def test_data_id(df, study_id):
if study_id is not None:
df['study_id'] = study_id
col_study_id = 'study_id'
else:
col_study_id = None
d = MRData()
d.load_df(df,
col_obs='obs',
col_obs_se='obs_se',
col_covs=['cov0', 'cov1', 'cov2'],
col_study_id=col_study_id)
d._sort_by_data_id()
assert np.allclose(d.obs, df['obs'])
assert np.allclose(d.obs_se, df['obs_se'])
for i in range(3):
assert np.allclose(d.covs[f'cov{i}'], df[f'cov{i}'])
