class_docstr: Class to handle multiple one-sample mixed effects models
      
docstr: Set the effects and first-level variance,
  and initialize related quantities
  
  Parameters
  ----------
  X: array of shape(n_samples, n_effects),
     the design matrix
  n_iter: int, optional,
         number of iterations of the EM algorithm
  verbose: bool, optional, verbosity mode
docstr_tok: ["Set","the","effects","and","first-level","variance",",","and","initialize","related","quantities","Parameters","--","--","--","--","--","X",":","array","of","shape","(","n_samples",",","n_effects",")",",","the","design","matrix","n_iter",":","int",",","optional",",","number","of","iterations","of","the","EM","algorithm","verbose",":","bool",",","optional",",","verbosity","mode"]
code: def __init__(self, X, n_iter=5, verbose=False):
      """
      Set the effects and first-level variance,
      and initialize related quantities
  
      Parameters
      ----------
      X: array of shape(n_samples, n_effects),
         the design matrix
      n_iter: int, optional,
             number of iterations of the EM algorithm
      verbose: bool, optional, verbosity mode
      """
      self.n_iter = n_iter
      self.verbose = verbose
      self.X = X
      self.pinv_X = np.linalg.pinv(X)
  
file_path: nipy/nipy/algorithms/statistics/mixed_effects_stat.py
imports: import numpy
label_desc: Use image node_id 1 to create a new MixedEffectsModel object  with example: obj = MixedEffectsModel(X, n_iter, verbose)