class_docstr: Control FDR in a regression procedure.
  
  Parameters
  ----------
  endog : array_like
      The dependent variable of the regression
  exog : array_like
      The independent variables of the regression
  regeffects : RegressionEffects instance
      An instance of a RegressionEffects class that can compute
      effect sizes for the regression coefficients.
  method : str
      The approach used to assess and control FDR, currently
      must be 'knockoff'.
  
  Returns
  -------
  Returns an instance of the RegressionFDR class.  The `fdr` attribute
  holds the estimated false discovery rates.
  
  Notes
  -----
  This class Implements the knockoff method of Barber and Candes.
  This is an approach for controlling the FDR of a variety of
  regression estimation procedures, including correlation
  coefficients, OLS regression, OLS with forward selection, and
  LASSO regression.
  
  For other approaches to FDR control in regression, see the
  statsmodels.stats.multitest module.  Methods provided in that
  module use Z-scores or p-values, and therefore require standard
  errors for the coefficient estimates to be available.
  
  The default method for constructing the augmented design matrix is
  the 'equivariant' approach, set `design_method='sdp'` to use an
  alternative approach involving semidefinite programming.  See
  Barber and Candes for more information about both approaches.  The
  sdp approach requires that the cvxopt package be installed.
docstr: Returns the threshold statistic for a given target FDR.
code: def threshold(self, tfdr):
      """
      Returns the threshold statistic for a given target FDR.
      """
  
      if np.min(self._ufdr) <= tfdr:
          return self._unq[self._ufdr <= tfdr][0]
      else:
          return np.inf
  
file_path: statsmodels/statsmodels/stats/_knockoff.py
imports: import numpy
  import pandas
  from statsmodels.iolib import summary2
label_desc: Use image node_id 2 for calling the RegressionFDR obj's underlying member method code with example usage: obj.threshold(tfdr) and returns: self, np