Feols.predict

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

Predict values of the model on new data.

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	DataFrameType	A narwhals compatible DataFrame (polars, pandas, duckdb, etc). If None (default), the data used for fitting the model is used.	None
type	str	The type of prediction to be computed. Can be either “response” (default) or “link”. For linear models, both are identical.	'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
type	PredictionType	The type of prediction to be made. Can be either ‘link’ or ‘response’. Defaults to ‘link’. ‘link’ and ‘response’ lead to identical results for linear models.	'link'
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”.

Examples

In-sample predictions:

import pyfixest as pf

data = pf.get_data()
fit = pf.feols("Y ~ X1 + X2 | f1", data)
fit.predict()[:5]

array([ 1.84475454, -0.17106206,  0.46970178, -0.74191438, -1.52651336])

Pass newdata to predict out of sample. Fixed effect levels that do not appear in the estimation sample return missing values.

fit.predict(newdata=data.head())

array([ 1.80731416,         nan,         nan,  1.84475484, -0.17106194])