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| """ | |
| Diagnostic checks for Linear Regression models. | |
| """ | |
| from typing import Dict, Any | |
| import statsmodels.api as sm | |
| from statsmodels.stats.diagnostic import het_breuschpagan, normal_ad | |
| from statsmodels.stats.stattools import jarque_bera | |
| from statsmodels.regression.linear_model import RegressionResultsWrapper | |
| import pandas as pd | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| def run_lr_diagnostics(results: RegressionResultsWrapper, X: pd.DataFrame) -> Dict[str, Any]: | |
| """ | |
| Runs diagnostic checks on a fitted OLS model. | |
| Args: | |
| results: A fitted statsmodels OLS results object. | |
| X: The design matrix (including constant) used for the regression. | |
| Needed for heteroskedasticity tests. | |
| Returns: | |
| Dictionary containing diagnostic metrics. | |
| """ | |
| diagnostics = {} | |
| try: | |
| diagnostics['r_squared'] = results.rsquared | |
| diagnostics['adj_r_squared'] = results.rsquared_adj | |
| diagnostics['f_statistic'] = results.fvalue | |
| diagnostics['f_p_value'] = results.f_pvalue | |
| diagnostics['n_observations'] = int(results.nobs) | |
| diagnostics['degrees_of_freedom_resid'] = int(results.df_resid) | |
| # --- Normality of Residuals (Jarque-Bera) --- | |
| try: | |
| jb_value, jb_p_value, skew, kurtosis = jarque_bera(results.resid) | |
| diagnostics['residuals_normality_jb_stat'] = jb_value | |
| diagnostics['residuals_normality_jb_p_value'] = jb_p_value | |
| diagnostics['residuals_skewness'] = skew | |
| diagnostics['residuals_kurtosis'] = kurtosis | |
| diagnostics['residuals_normality_status'] = "Normal" if jb_p_value > 0.05 else "Non-Normal" | |
| except Exception as e: | |
| logger.warning(f"Could not run Jarque-Bera test: {e}") | |
| diagnostics['residuals_normality_status'] = "Test Failed" | |
| # --- Homoscedasticity (Breusch-Pagan) --- | |
| # Requires the design matrix X used in the model fitting | |
| try: | |
| lm_stat, lm_p_value, f_stat, f_p_value = het_breuschpagan(results.resid, X) | |
| diagnostics['homoscedasticity_bp_lm_stat'] = lm_stat | |
| diagnostics['homoscedasticity_bp_lm_p_value'] = lm_p_value | |
| diagnostics['homoscedasticity_bp_f_stat'] = f_stat | |
| diagnostics['homoscedasticity_bp_f_p_value'] = f_p_value | |
| diagnostics['homoscedasticity_status'] = "Homoscedastic" if lm_p_value > 0.05 else "Heteroscedastic" | |
| except Exception as e: | |
| logger.warning(f"Could not run Breusch-Pagan test: {e}") | |
| diagnostics['homoscedasticity_status'] = "Test Failed" | |
| # --- Linearity (Basic check - often requires visual inspection) --- | |
| # No standard quantitative test implemented here. Usually assessed via residual plots. | |
| diagnostics['linearity_check'] = "Requires visual inspection (e.g., residual vs fitted plot)" | |
| # --- Multicollinearity (Placeholder - requires VIF calculation) --- | |
| # VIF requires iterating through predictors, more involved | |
| diagnostics['multicollinearity_check'] = "Not Implemented (Requires VIF)" | |
| return {"status": "Success", "details": diagnostics} | |
| except Exception as e: | |
| logger.error(f"Error running LR diagnostics: {e}") | |
| return {"status": "Failed", "error": str(e), "details": diagnostics} # Return partial results if possible | |