utils.stats.compute_standardized_betas

utils.stats.compute_standardized_betas(model, X_encoded, y)

Computes standardized (beta) coefficients for a fitted statsmodels OLS model.

Parameters

Name	Type	Description	Default
model	`statsmodels`.`regression`.`linear_model`.`RegressionResultsWrapper`	The fitted OLS model.	required
X_encoded	pandas.DataFrame	The design matrix of independent variables.	required
y	pandas.Series	The dependent variable.	required

Returns

Name	Type	Description
	pd.DataFrame	pandas.DataFrame: A DataFrame containing the standardized beta coefficients.

Examples

>>> from spotpython.utils.stats import compute_standardized_betas
>>> import pandas as pd
>>> import statsmodels.api as sm
>>> data = pd.DataFrame({
...     'x1': [1, 2, 3, 4, 5],
...     'x2': [2, 4, 6, 8, 10],
...     'x3': [1, 3, 5, 7, 9]
... })
>>> y = pd.Series([1, 2, 3, 4, 5])
>>> X = sm.add_constant(data)
>>> model = sm.OLS(y, X).fit()
>>> compute_standardized_betas(model, data, y)
   Variable  Standardized Beta
0     const           0.000000
1       x1           0.000000
2       x2           0.000000
3       x3           0.000000