create_model_matrix

create_model_matrix(
    formula,
    data,
    weights=None,
    offset=None,
    drop_singletons=False,
    drop_intercept=False,
    ensure_full_rank=True,
    context=0,
)

Create a ModelMatrix from a formula and data.

This function constructs model matrices for econometric estimation by parsing formulas and extracting the necessary components (dependent/independent variables, fixed effects, instruments, weights) from the provided data.

Parameters

Name	Type	Description	Default
formula	Formula	A Formula object specifying the model structure, including dependent and independent variables, fixed effects, and instrumental variables.	required
data	pd.DataFrame	The input data containing all variables referenced in the formula. The index will be reset during processing.	required
weights	str or None	Column name in data to use as observation weights. Weights must be non-negative numeric values. If None, no weighting is applied.	`None`
offset	str or None	Formulaic expression that evaluates to one numeric offset column. The offset is added to the linear predictor with a fixed coefficient of 1. Rows with missing offset values are dropped together with missing rows in the rest of the formula.	`None`
drop_singletons	bool	If True, observations that are singletons in any fixed effect category are dropped from the model.	`False`
drop_intercept	bool	If True, the intercept column is removed from the independent variables and instruments matrices. The intercept is always removed when fixed effects are present, regardless of this parameter.	`False`
ensure_full_rank	bool	If True, formulaic will ensure the design matrix is full rank by dropping collinear columns.	`True`
context	int or Mapping[str, Any]	Additional context variables for formulaic during model matrix creation. Can be an integer (stack frame depth) or a dictionary of variables to make available in the formula environment (e.g., custom transformations).	`0`

Returns

Name	Type	Description
	ModelMatrix	A ModelMatrix object containing the processed dependent and independent variables, fixed effects, instruments, weights, and metadata about dropped observations.

Examples

import pyfixest as pf
from pyfixest.estimation.formula.model_matrix import create_model_matrix
from pyfixest.estimation.formula.parse import Formula

data = pf.get_data()
formula = Formula.parse("Y ~ X1 + f1 + f2")[0]
model_matrix = create_model_matrix(formula=formula, data=data)