diff --git a/BayesValidRox/PostProcessing/PostProcessing.py b/BayesValidRox/PostProcessing/PostProcessing.py
index c50be9a86038dbeafb77f2687949a9926cfa84f6..4c3cd1f5e848da579e6fab6029255c7508f7e135 100644
--- a/BayesValidRox/PostProcessing/PostProcessing.py
+++ b/BayesValidRox/PostProcessing/PostProcessing.py
@@ -91,7 +91,7 @@ class PostProcessing:
         # Get the variables
         Keys = list(self.PCEMeans.keys())
         index = self.PCEModel.index
-        cases = ['Calib'] if index == -1 else ['Calib', 'Valid']
+        cases = [''] if index is None else ['Calib', 'Valid']
         
         for case in cases:
             # Open a pdf for the plots
@@ -342,7 +342,7 @@ class PostProcessing:
         
         # Run the original model with the generated samples
         ModelOutputs = self.eval_Model(Samples, keyString='validSet') if validOutputsDict is None else validOutputsDict
-        
+
         # Run the PCE model with the generated samples
         PCEOutputs, PCEOutputs_std = metaModel.eval_metamodel(samples=Samples)
         
@@ -576,7 +576,7 @@ class PostProcessing:
             
             # Calculate the RMSE
             RMSE = mean_squared_error(Y_PC_Val_, Y_Val_, squared=False)
-            R2 = r2_score(Y_Val_[idx,:].reshape(-1,1), Y_Val_[idx,:].reshape(-1,1))
+            R2 = r2_score(Y_PC_Val_[idx,:].reshape(-1,1), Y_Val_[idx,:].reshape(-1,1))
             
             plt.annotate('RMSE = '+ str(round(RMSE, 3)) + '\n' + r'$R^2$ = '+ str(round(R2, 3)),
                          xy=(0.2, 0.75), xycoords='axes fraction')
@@ -1030,7 +1030,7 @@ class PostProcessing:
                     plt.xlabel(x_axis)
         
                 plt.title('Sensitivity analysis of '+ Output)
-                plt.legend(loc='best')
+                plt.legend(loc='best', frameon=True)
                 
                 
                 if SaveFig: