feat: implement logistic regression pipeline with automated JASP-formatted report generation and data inspection utilities

Project_Code_and_Results/4_Logistic_Regression/Logistic_Results_Decision_Final.csv

@@ -1,25 +1,25 @@
-,Beta (B),P-value,Odds Ratio EXP(B),Significance
-const,-13.3839,0.0078,0.0,**
-Frequency,0.7183,0.0098,2.051,**
-Career_6,4.1539,0.0105,63.6799,*
-Rooftop,1.7017,0.0123,5.4833,*
-Income,0.0045,0.0229,1.0045,*
-Transportation_4,4.6473,0.0284,104.3069,*
-MEAN CES,1.8488,0.0288,6.3523,*
-Career_4,2.8862,0.0334,17.9242,*
-Career_3,3.4267,0.0368,30.7762,*
-Garden,-1.4026,0.0446,0.246,*
-MEAN DES,-0.4982,0.0586,0.6076,
-Transportation_3,2.1343,0.0739,8.4512,
-Literacy,0.5519,0.0906,1.7365,
-Gender_2,1.1137,0.1406,3.0455,
-Nature,-0.6135,0.2466,0.5415,
-MEAN RES,-0.6547,0.2723,0.5196,
-Residential,0.6836,0.391,1.981,
-Time,-0.1722,0.5721,0.8418,
-Distance,-0.161,0.6202,0.8513,
-Transportation_2,0.6629,0.6341,1.9404,
-Recreation,-0.1187,0.7927,0.8881,
-Career_2,0.347,0.8054,1.4148,
-Park,-0.1024,0.8656,0.9027,
-Agriculture,-0.0665,0.8894,0.9356,
+,Beta (B),S.E.,P-value,Odds Ratio EXP(B),Significance
+const,-13.3839,5.0267,0.0078,0.0,**
+Frequency,0.7183,0.278,0.0098,2.051,**
+Career_6,4.1539,1.6229,0.0105,63.6799,*
+Rooftop,1.7017,0.6798,0.0123,5.4833,*
+Income,0.0045,0.002,0.0229,1.0045,*
+Transportation_4,4.6473,2.1208,0.0284,104.3069,*
+MEAN CES,1.8488,0.8458,0.0288,6.3523,*
+Career_4,2.8862,1.3569,0.0334,17.9242,*
+Career_3,3.4267,1.6413,0.0368,30.7762,*
+Garden,-1.4026,0.6984,0.0446,0.246,*
+MEAN DES,-0.4982,0.2635,0.0586,0.6076,
+Transportation_3,2.1343,1.1942,0.0739,8.4512,
+Literacy,0.5519,0.3261,0.0906,1.7365,
+Gender_2,1.1137,0.7557,0.1406,3.0455,
+Nature,-0.6135,0.5294,0.2466,0.5415,
+MEAN RES,-0.6547,0.5964,0.2723,0.5196,
+Residential,0.6836,0.7969,0.391,1.981,
+Time,-0.1722,0.3048,0.5721,0.8418,
+Distance,-0.161,0.3249,0.6202,0.8513,
+Transportation_2,0.6629,1.3928,0.6341,1.9404,
+Recreation,-0.1187,0.4515,0.7927,0.8881,
+Career_2,0.347,1.4082,0.8054,1.4148,
+Park,-0.1024,0.6051,0.8656,0.9027,
+Agriculture,-0.0665,0.4784,0.8894,0.9356,

Project_Code_and_Results/4_Logistic_Regression/export_jasp_layout.py

@@ -0,0 +1,75 @@
+import sys
+sys.stdout.reconfigure(encoding='utf-8')
+import pandas as pd
+import numpy as np
+import os
+
+base_dir = r'c:\Users\NASPC\Documents\Du án tại SG tháng 8\Project_Code_and_Results\4_Logistic_Regression'
+
+file_donation = os.path.join(base_dir, 'Logistic_Results_Donation_Firth.csv')
+file_decision = os.path.join(base_dir, 'Logistic_Results_Decision_Final.csv')
+
+def process_results(filepath, model_name="M₁"):
+    df = pd.read_csv(filepath, index_col=0)
+    
+    # Check if we have S.E. or std err
+    se_col = 'S.E.' if 'S.E.' in df.columns else ('std err' if 'std err' in df.columns else None)
+    if not se_col:
+        # For some files it might be 'SE'
+        if 'SE' in df.columns: se_col = 'SE'
+    
+    # Recreate necessary metrics for JASP format
+    estimate = df['Beta (B)']
+    se = df[se_col] if se_col else 0
+    odds_ratio = df['Odds Ratio EXP(B)']
+    p_val = df['P-value']
+    
+    # Calculate Z and Wald
+    z_stat = estimate / se
+    wald = z_stat ** 2
+    lower = np.exp(estimate - 1.96 * se)
+    upper = np.exp(estimate + 1.96 * se)
+    
+    # Rename index to match JASP
+    # JASP formats: e.g. "Gender_2" -> "Gender (2)", "const" -> "(Intercept)"
+    def map_name(name):
+        if name == 'const': return '(Intercept)'
+        if '_' in name:
+            parts = name.split('_')
+            return f"{parts[0]} ({parts[1]})"
+        return name
+    
+    new_index = [map_name(str(i)) for i in df.index]
+    
+    # Build the target dataframe
+    out_df = pd.DataFrame({
+        'Model': [model_name] + [np.nan] * (len(df) - 1),
+        '': new_index,
+        'Estimate': estimate.values,
+        'Standard Error': se.values if se_col else [np.nan]*len(df),
+        'Odds Ratio': odds_ratio.values,
+        'z': z_stat.values,
+        'Wald Statistic': wald.values,
+        'df': [1] * len(df),
+        'p': p_val.values,
+        'Lower bound': lower.values,
+        'Upper bound': upper.values
+    })
+    
+    # Format p-values < .00001
+    out_df['p'] = out_df['p'].apply(lambda x: '< .00001' if pd.notnull(x) and x < 0.00001 else (round(x, 5) if pd.notnull(x) else x))
+    out_df = out_df.round(5)
+    return out_df
+
+out_path = r'c:\Users\NASPC\Documents\Du án tại SG tháng 8\Project_Code_and_Results\4_Logistic_Regression\KẾT_QUẢ_FINAL_JASP_LAYOUT.xlsx'
+
+with pd.ExcelWriter(out_path, engine='openpyxl') as writer:
+    if os.path.exists(file_donation):
+        df_don = process_results(file_donation, "M₁")
+        df_don.to_excel(writer, sheet_name='Donation_New', index=False)
+    
+    if os.path.exists(file_decision):
+        df_dec = process_results(file_decision, "M₁")
+        df_dec.to_excel(writer, sheet_name='Decision_New', index=False)
+
+print(f"Exported successfully to {out_path}")

Project_Code_and_Results/4_Logistic_Regression/run_final_logistic.py

@@ -69,6 +69,7 @@ model_dec = sm.Logit(y_dec, X)
 res_dec = model_dec.fit(method='newton', maxiter=1000, disp=False)
 sum_dec = pd.DataFrame({
     'Beta (B)': res_dec.params,
+    'S.E.': res_dec.bse,
     'P-value': res_dec.pvalues,
     'Odds Ratio EXP(B)': np.exp(res_dec.params)
 }).round(4)