Update app.py

app.py

@@ -367,33 +367,36 @@ class RSM_BoxBehnken:
             'Valor p': [],
             '% Contribución': []
         })
-
+    
         # 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
+
+        # Agregar fila para el bloque
+        block_ss = self.data.groupby('Exp.')[self.y_name].sum().var() * len(self.data)
+        block_df = 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'])
+        block_contribution = (block_ss / ss_total) * 100
+    
+        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_contribution]
+        })], ignore_index=True)
+    
+        # Agregar fila para el modelo
+        model_ss = anova_table['sum_sq'][:-1].sum()  # Suma todo excepto 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'])
+        model_contribution = (model_ss / ss_total) * 100
+    
         contribution_table = pd.concat([contribution_table, pd.DataFrame({
             'Fuente de Variación': ['Model'],
             'Suma de Cuadrados': [model_ss],
@@ -401,19 +404,19 @@ class RSM_BoxBehnken:
             'Cuadrado Medio': [model_ms],
             'F': [model_f],
             'Valor p': [model_p],
-            '% Contribución': [(model_ss / ss_total) * 100]
+            '% Contribución': [model_contribution]
         })], ignore_index=True)
 
-        # Agregar factores individuales y sus interacciones
+        # Agregar filas para cada término del modelo
         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']
@@ -432,28 +435,17 @@ class RSM_BoxBehnken:
                     '% Contribución': [contribution_percentage]
                 })], ignore_index=True)
     
-        # 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': ['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 fila para Cor Total
+        cor_total_ss = ss_total
+        cor_total_df = len(self.data) - 1
     
-        # 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],
+            'Suma de Cuadrados': [cor_total_ss],
+            'Grados de Libertad': [cor_total_df],
+            'Cuadrado Medio': [np.nan],
+            'F': [np.nan],
+            'Valor p': [np.nan],
             '% Contribución': [100]
         })], ignore_index=True)