import pyfixest as pf
fit = pf.feols("Y ~ X1 + X2 | f1", pf.get_data())
fe = fit.fixef()
fe.keys()dict_keys(['C(f1)'])Feols.fixef
fixef(atol=1e-06, btol=1e-06)Compute the coefficients of (swept out) fixed effects for a regression model.
This method creates the following attributes: - _alpha (pd.DataFrame): A DataFrame with the estimated fixed effects. - _sumFE (np.array): An array with the sum of fixed effects for each observation (i = 1, …, N).
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| atol | Float | Stopping tolerance for scipy.sparse.linalg.lsqr(). See https://docs.scipy.org/doc/ scipy/reference/generated/scipy.sparse.linalg.lsqr.html | 1e-6 |
| btol | Float | Another stopping tolerance for scipy.sparse.linalg.lsqr(). See https://docs.scipy.org/doc/ scipy/reference/generated/scipy.sparse.linalg.lsqr.html | 1e-6 |
Returns
| Name | Type | Description |
|---|---|---|
| dict[str, dict[str, float]] | A dictionary with the estimated fixed effects. |
Examples
Fixed effects are swept out during estimation and are not part of the coefficient table. fixef() computes them. The result is keyed by fixed effect term, then by level.
list(fe["C(f1)"].items())[:5][('0.0', np.float64(0.4837574151832394)),
('1.0', np.float64(3.0661419612921605)),
('2.0', np.float64(-1.0947871507593956)),
('3.0', np.float64(0.33109523121756435)),
('4.0', np.float64(-1.2872533793542074))]