ENH: Add optional parameters for summary_col to indicate FEs

statsmodels/iolib/summary2.py

@@ -471,7 +471,8 @@
 
 def summary_col(results, float_format='%.4f', model_names=(), stars=False,
                 info_dict=None, regressor_order=(), drop_omitted=False,
-                include_r2=True):
+                include_r2=True, fixed_effects=None, fe_present='Yes',
+                fe_absent=''):
     """
     Summarize multiple results instances side-by-side (coefs and SEs)
 
@@ -504,6 +505,14 @@
         If True, only regressors in regressor_order will be included.
     include_r2 : bool, optional
         Includes R2 and adjusted R2 in the summary table.
+    fixed_effects : list[str], optional
+        List of categorical variables for which to indicate presence of
+        fixed effects.
+    fe_present : str, optional
+        String to indicate the presence of fixed effects. Default is "Yes".
+    fe_absent : str, optional
+        String to indicate the absence of fixed effects. Default is empty
+        string.
     """
 
     if not isinstance(results, list):
@@ -562,6 +571,19 @@
             idx.append(index[i + 1])
     summ.index = idx
 
+    # add fixed effects info
+    if fixed_effects:
+        if not info_dict:
+            info_dict = {}
+        for fe in fixed_effects:
+            info_dict[fe + ' FE'] = (lambda x,
+                                     fe=fe,
+                                     fe_present=fe_present,
+                                     fe_absent=fe_absent: fe_present
+                                        if any(param.startswith(f'C({fe})')
+                                            for param in x.params.index)
+                                        else fe_absent)
+
     # add infos about the models.
     if info_dict:
         cols = [_col_info(x, info_dict.get(x.model.__class__.__name__,
@@ -581,6 +603,20 @@
 
     summ = summ.fillna('')
 
+    # fixed effects processing
+    if fixed_effects:
+        index_series = pd.Series(summ.index, index=summ.index)
+        skip_flag = index_series.apply(lambda x: any(x.startswith(f'C({fe})')
+                                                for fe in fixed_effects))
+        skip_next_flag = skip_flag.shift(fill_value=False)
+        final_skip = skip_flag | skip_next_flag
+        summ = summ[~final_skip]
+
+        r_squared_rows = summ.index[summ.index.str.contains('R-squared')]
+        r_squared_section = summ.loc[r_squared_rows]
+        summ = summ.drop(index=r_squared_rows)
+        summ = pd.concat([summ, r_squared_section])
+
     smry = Summary()
     smry._merge_latex = True
     smry.add_df(summ, header=True, align='l')