class_docstr: Class to compute the empirical null normal fit to the data.
  
  The data which is used to estimate the FDR, assuming a Gaussian null
  from Schwartzmann et al., NeuroImage 44 (2009) 71--82
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docstr: Compute the threshold corresponding to a specificity alpha for x
  
  Parameters
  -----------
  alpha : float, optional
      the chosen false discovery rate (FDR) threshold.
  verbose : boolean, optional
      the verbosity level, if True a plot is generated.
  
  Returns
  -------
  theta: float
      the critical value associated with the provided p-value
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code: def uncorrected_threshold(self, alpha=0.001, verbose=0):
      """Compute the threshold corresponding to a specificity alpha for x
  
      Parameters
      -----------
      alpha : float, optional
          the chosen false discovery rate (FDR) threshold.
      verbose : boolean, optional
          the verbosity level, if True a plot is generated.
  
      Returns
      -------
      theta: float
          the critical value associated with the provided p-value
      """
      if self.learned == 0:
          self.learn()
      threshold = st.norm.isf(alpha, self.mu, self.sigma)
      if not np.isfinite(threshold):
          threshold = np.inf
      if verbose:
          self.plot()
      return threshold
  
file_path: nipy/nipy/algorithms/statistics/empirical_pvalue.py
imports: import numpy
  import scipy.stats
  from numpy.linalg import pinv
label_desc: Use image node_id 5 for calling the NormalEmpiricalNull obj's underlying member method code with example usage: obj.uncorrected_threshold(alpha, verbose) and returns: threshold