fit_linearmodels()

Fit a linearmodels model with output that is compatible with pymarginaleffects.

This function streamlines the process of fitting linearmodels panel models by:

  1. Parsing panel effects from the formula
  2. Handling missing values
  3. Creating model matrices
  4. Fitting the model with specified options

Parameters

formula: (str) Model formula with optional panel effects terms.

  • Supported effects are:
    • EntityEffects: Entity-specific fixed effects
    • TimeEffects: Time-specific fixed effects
    • FixedEffects: Alias for EntityEffects
  • Example: "y ~ x1 + x2 + EntityEffects"

data : (pandas.DataFrame) Panel data with MultiIndex (entity, time) or regular DataFrame with entity and time columns.

engine: (callable) linearmodels model class (e.g., PanelOLS, BetweenOLS, FirstDifferenceOLS)

kwargs_engine: (dict, default={}) Additional arguments passed to the model initialization.

  • Example: {'weights': weights_array}

kwargs_fit: (dict, default={}) Additional arguments passed to the model’s fit method.

  • Example: {'cov_type': 'robust'}

Returns

(ModelLinearmodels) A fitted model wrapped in the ModelLinearmodels class for compatibility with marginaleffects.

Examples

from linearmodels.panel import PanelOLS
from linearmodels.panel import generate_panel_data
from marginaleffects import *
data = generate_panel_data()
model_robust = fit_linearmodels(
    formula="y ~ x1 + EntityEffects",
    data=data.data,
    engine=PanelOLS,
    kwargs_fit={'cov_type': 'robust'}
)

predictions(model_robust)

Notes

The fitted model includes additional attributes:

  • data: The processed data after listwise deletion
  • formula: The original formula string
  • formula_engine: Set to “linearmodels”
  • model: The fitted linearmodels model object