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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
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