original_docstring: 
          Calculates the P value of two models.
      
          Args:
              tp1: true positives of model 1. 
          tp1: true positives of model 1. 
              tp1: true positives of model 1. 
              tn1: true negatives of model 1.
              tp2: true positives of model 2. 
          tp2: true positives of model 2. 
              tp2: true positives of model 2. 
              tn2: true negatives of model 2.
              auc1: AUC value of model 1.
              auc2: AUC value of model 2.
          
docstring: Calculates the P value of two models.
docstring_tokens: Calculates the P value of two models .
code: def calc_pval(self, tp1, tn1, tp2, tn2, auc1, auc2):
          """
          Calculates the P value of two models.
      
          Args:
              tp1: true positives of model 1. 
          tp1: true positives of model 1. 
              tp1: true positives of model 1. 
              tn1: true negatives of model 1.
              tp2: true positives of model 2. 
          tp2: true positives of model 2. 
              tp2: true positives of model 2. 
              tn2: true negatives of model 2.
              auc1: AUC value of model 1.
              auc2: AUC value of model 2.
          """
  
          # Calculate the standard error.
          se1 = self.standard_error(tp1, tn1, auc1)
          se2 = self.standard_error(tp2, tn2, auc2)
  
          # Calculate the zscore.
          r = 0
          se_dif = np.sqrt(se1 ** 2 + se2 ** 2 - 2 * r * se1 * se2)
          z = (auc1 - auc2) / se_dif
  
          # Calculate the pvalue.
          pval = norm.sf(abs(z))*2 
          pval = norm.sf(abs(z))*2 
          pval = norm.sf(abs(z))*2 
          return pval
code_tokens: def calc_pval ( self , tp1 , tn1 , tp2 , tn2 , auc1 , auc2 ) : se1 = self . standard_error ( tp1 , tn1 , auc1 ) se2 = self . standard_error ( tp2 , tn2 , auc2 ) r = 0 se_dif = np . sqrt ( se1 ** 2 + se2 ** 2 - 2 * r * se1 * se2 ) z = ( auc1 - auc2 ) / se_dif pval = norm . sf ( abs ( z ) ) * 2 pval = norm . sf ( abs ( z ) ) * 2 pval = norm . sf ( abs ( z ) ) * 2 return pval
short_docstring: Calculates the P value of two models.
short_docstring_tokens: Calculates the P value of two models .
comment: """
          Calculates the P value of two models.
      
          Args:
              tp1: true positives of model 1. 
          tp1: true positives of model 1. 
              tp1: true positives of model 1. 
              tn1: true negatives of model 1.
              tp2: true positives of model 2. 
          tp2: true positives of model 2. 
              tp2: true positives of model 2. 
              tn2: true negatives of model 2.
              auc1: AUC value of model 1.
              auc2: AUC value of model 2.
          """$SEP$# Calculate the standard error.$SEP$# Calculate the zscore.$SEP$# Calculate the pvalue.