class_docstr: OLS regression for knockoff analysis.
  
  Parameters
  ----------
  parent : RegressionFDR
      The RegressionFDR instance to which this effect size is
      applied.
  
  Notes
  -----
  This class implements the ordinary least squares regression
  approach to constructing test statistics for a knockoff analysis,
  as described under (2) in section 2.2 of the Barber and Candes
  paper.
class_docstr_tok: ["OLS","regression","for","knockoff","analysis",".","Parameters","--","--","--","--","--","parent",":","RegressionFDR","The","RegressionFDR","instance","to","which","this","effect","size","is","applied",".","Notes","--","--","-","This","class","implements","the","ordinary","least","squares","regression","approach","to","constructing","test","statistics","for","a","knockoff","analysis",",","as","described","under","(","2",")","in","section","2.2","of","the","Barber","and","Candes","paper","."]
code: def stats(self, parent):
      from statsmodels.regression.linear_model import OLS
  
      model = OLS(parent.endog, parent.exog)
      result = model.fit()
      q = len(result.params) // 2
      stats = np.abs(result.params[0:q]) - np.abs(result.params[q:])
      return stats
  
file_path: statsmodels/statsmodels/stats/knockoff_regeffects.py
imports: import numpy
label_desc: Use image node_id 1 for calling the OLSEffects obj's underlying member method code with example usage: obj.stats(parent) and returns: stats