Feglm

Feglm(
    FixestFormula,
    data,
    ssc_dict,
    drop_singletons,
    drop_intercept,
    weights,
    weights_type,
    collin_tol,
    lookup_demeaned_data,
    tol,
    maxiter,
    solver,
    family,
    demeaner=None,
    lookup_preconditioner=None,
    store_data=True,
    copy_data=True,
    lean=False,
    sample_split_var=None,
    sample_split_value=None,
    separation_check=None,
    context=0,
    accelerate=True,
)

Base class for the estimation of a fixed-effects GLM model.

Returned by feglm(). Fixed effects are handled via iteratively reweighted least squares with demeaning, following Stammann (2018), arXiv:1707.01815. The family is set with the family argument and implemented by a subclass. poisson dispatches to Fepois.

Examples

import numpy as np
import pyfixest as pf

data = pf.get_data()
data["Y_bin"] = np.where(data["Y"] > 0, 1, 0)

fit = pf.feglm("Y_bin ~ X1 + X2 | f1", data, family="logit")
fit.tidy()

	Estimate	Std. Error	t value	Pr(>\|t\|)	2.5%	97.5%
Coefficient
X1	-1.016344	0.109325	-9.296552	0.000000e+00	-1.230617	-0.802071
X2	-0.165899	0.028450	-5.831291	5.500026e-09	-0.221660	-0.110139

Methods

Name	Description
Feglm.get_fit	Fit the GLM via IRLS and write results onto self.* attributes.
Feglm.predict	Return predicted values from regression model.
Feglm.prepare_model_matrix	Prepare model inputs for estimation.
Feglm.resid	Return residuals from a fitted GLM.
Feglm.residualize	Residualize v and X by flist using weights.
Feglm.to_array	Turn estimation DataFrames to np arrays.

Feglm.get_fit

get_fit()

Fit the GLM via IRLS and write results onto self.* attributes.

Feglm.predict

predict(
    newdata=None,
    atol=1e-06,
    btol=1e-06,
    type='link',
    se_fit=False,
    interval=None,
    alpha=0.05,
)

Return predicted values from regression model.

Return a flat np.array with predicted values of the regression model. If new fixed effect levels are introduced in newdata, predicted values for such observations will be set to NaN.

Parameters

Name	Type	Description	Default
newdata	Union[None, pd.DataFrame]	A pd.DataFrame with the new data, to be used for prediction. If None (default), uses the data used for fitting the model.	`None`
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`
type	str	The type of prediction to be computed. Can be either “response” (default) or “link”. If type=“response”, the output is at the level of the response variable, i.e., it is the expected predictor E(Y\|X). If “link”, the output is at the level of the explanatory variables, i.e., the linear predictor X @ beta.	`'link'`
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`
se_fit	bool \| None	If True, the standard error of the prediction is computed. Only feasible for models without fixed effects. GLMs are not supported. Defaults to False.	`False`
interval	PredictionErrorOptions \| None	The type of interval to compute. Can be either ‘prediction’ or None.	`None`
alpha	float	The alpha level for the confidence interval. Defaults to 0.05. Only used if interval = “prediction” is not None.	`0.05`

Returns

Name	Type	Description
	Union[np.ndarray, pd.DataFrame]	Returns a pd.Dataframe with columns “fit”, “se_fit” and CIs if argument “interval=prediction”. Otherwise, returns a np.ndarray with the predicted values of the model or the prediction standard errors if argument “se_fit=True”.

Feglm.prepare_model_matrix

prepare_model_matrix()

Prepare model inputs for estimation.

Feglm.resid

resid(type='response')

Return residuals from a fitted GLM.

Parameters

Name	Type	Description	Default
type	str	The type of residuals to return. Either “response” (default) or “working”.	`'response'`

Returns

Name	Type	Description
	np.ndarray	A flat array with the requested residuals.

Feglm.residualize

residualize(v, X, flist, weights, tol)

Residualize v and X by flist using weights.

Feglm.to_array

to_array()

Turn estimation DataFrames to np arrays.