Update app.py

app.py

@@ -350,13 +350,13 @@ class RSM_BoxBehnken:
         if self.model_simplified is None:
             print("Error: Ajusta el modelo simplificado primero.")
             return None
-
+    
         # ANOVA del modelo simplificado
         anova_table = sm.stats.anova_lm(self.model_simplified, typ=2)
-
+    
         # Suma de cuadrados total
         ss_total = anova_table['sum_sq'].sum()
-
+    
         # Crear tabla de contribución
         contribution_table = pd.DataFrame({
             'Fuente de Variación': [],
@@ -370,24 +370,58 @@ class RSM_BoxBehnken:
 
         # Calcular estadísticos F y porcentaje de contribución para cada factor
         ms_error = anova_table.loc['Residual', 'sum_sq'] / anova_table.loc['Residual', 'df']
+        
+        # Agregar Block (si está disponible en los datos)
+        block_ss = self.data.groupby('Block')['AIA_ppm'].sum().var() if 'Block' in self.data.columns else 0
+        if block_ss > 0:
+            block_df = len(self.data['Block'].unique()) - 1 if 'Block' in self.data.columns else 1
+            block_ms = block_ss / block_df
+            block_f = block_ms / ms_error
+            block_p = f.sf(block_f, block_df, anova_table.loc['Residual', 'df'])
+            contribution_table = pd.concat([contribution_table, pd.DataFrame({
+                'Fuente de Variación': ['Block'],
+                'Suma de Cuadrados': [block_ss],
+                'Grados de Libertad': [block_df],
+                'Cuadrado Medio': [block_ms],
+                'F': [block_f],
+                'Valor p': [block_p],
+                '% Contribución': [(block_ss / ss_total) * 100]
+            })], ignore_index=True)
+        
+        # Agregar Model (suma de todos los términos del modelo excepto el residual)
+        model_ss = anova_table['sum_sq'][:-1].sum()  # Excluir residual
+        model_df = anova_table['df'][:-1].sum()
+        model_ms = model_ss / model_df
+        model_f = model_ms / ms_error
+        model_p = f.sf(model_f, model_df, anova_table.loc['Residual', 'df'])
+        contribution_table = pd.concat([contribution_table, pd.DataFrame({
+            'Fuente de Variación': ['Model'],
+            'Suma de Cuadrados': [model_ss],
+            'Grados de Libertad': [model_df],
+            'Cuadrado Medio': [model_ms],
+            'F': [model_f],
+            'Valor p': [model_p],
+            '% Contribución': [(model_ss / ss_total) * 100]
+        })], ignore_index=True)
 
+        # Agregar factores individuales y sus interacciones
         for index, row in anova_table.iterrows():
             if index != 'Residual':
                 factor_name = index
                 if factor_name == f'I({self.x1_name} ** 2)':
-                    factor_name = f'{self.x1_name}^2'
+                    factor_name = f'{self.x1_name}²'
                 elif factor_name == f'I({self.x2_name} ** 2)':
-                    factor_name = f'{self.x2_name}^2'
+                    factor_name = f'{self.x2_name}²'
                 elif factor_name == f'I({self.x3_name} ** 2)':
-                    factor_name = f'{self.x3_name}^2'
-
+                    factor_name = f'{self.x3_name}²'
+    
                 ss_factor = row['sum_sq']
                 df_factor = row['df']
                 ms_factor = ss_factor / df_factor
                 f_stat = ms_factor / ms_error
                 p_value = f.sf(f_stat, df_factor, anova_table.loc['Residual', 'df'])
                 contribution_percentage = (ss_factor / ss_total) * 100
-
+    
                 contribution_table = pd.concat([contribution_table, pd.DataFrame({
                     'Fuente de Variación': [factor_name],
                     'Suma de Cuadrados': [ss_factor],
@@ -397,22 +431,32 @@ class RSM_BoxBehnken:
                     'Valor p': [p_value],
                     '% Contribución': [contribution_percentage]
                 })], ignore_index=True)
-        
-        # Calcular estadístico F global y su valor p
-        f_global = anova_table['sum_sq'][:-1].sum() / anova_table['df'][:-1].sum() / ms_error
-        p_global = f.sf(f_global, anova_table['df'][:-1].sum(), anova_table.loc['Residual', 'df'])
-        
-        # Agregar fila para el estadístico F global
+    
+        # Agregar Residual
+        residual_ss = anova_table.loc['Residual', 'sum_sq']
+        residual_df = anova_table.loc['Residual', 'df']
+        residual_ms = residual_ss / residual_df
         contribution_table = pd.concat([contribution_table, pd.DataFrame({
-            'Fuente de Variación': ['F global'],
-            'Suma de Cuadrados': [np.nan],
-            'Grados de Libertad': [np.nan],
-            'Cuadrado Medio': [np.nan],
-            'F': [f_global],
-            'Valor p': [p_global],
-            '% Contribución': [np.nan]
+            'Fuente de Variación': ['Residual'],
+            'Suma de Cuadrados': [residual_ss],
+            'Grados de Libertad': [residual_df],
+            'Cuadrado Medio': [residual_ms],
+            'F': [None],
+            'Valor p': [None],
+            '% Contribución': [(residual_ss / ss_total) * 100]
         })], ignore_index=True)
-
+    
+        # Agregar Correlation Total
+        contribution_table = pd.concat([contribution_table, pd.DataFrame({
+            'Fuente de Variación': ['Cor Total'],
+            'Suma de Cuadrados': [ss_total],
+            'Grados de Libertad': [len(self.data) - 1],
+            'Cuadrado Medio': [None],
+            'F': [None],
+            'Valor p': [None],
+            '% Contribución': [100]
+        })], ignore_index=True)
+    
         return contribution_table.round(3)
 
     def calculate_detailed_anova(self):