class_docstr: Binomial variance function
  
  Parameters
  ----------
  n : int, optional
      The number of trials for a binomial variable.  The default is 1 for
      p in (0,1)
  
  Methods
  -------
  call
      Returns the binomial variance
  
  Notes
  -----
  Formulas :
  
     V(mu) = p * (1 - p) * n
  
  where p = mu / n
  
  Alias for Binomial:
  binary = Binomial()
  
  A private method _clean trims the data by machine epsilon so that p is
  in (0,1)
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docstr: Binomial variance function
  
  Parameters
  ----------
  mu : array_like
      mean parameters
  
  Returns
  -------
  variance : ndarray
     variance = mu/n * (1 - mu/n) * self.n
docstr_tok: ["Binomial","variance","function","Parameters","--","--","--","--","--","mu",":","array_like","mean","parameters","Returns","--","--","--","-","variance",":","ndarray","variance","=","mu\/n","*","(","1","-","mu\/n",")","*","self.n"]
code: def __call__(self, mu):
      """
      Binomial variance function
  
      Parameters
      ----------
      mu : array_like
          mean parameters
  
      Returns
      -------
      variance : ndarray
         variance = mu/n * (1 - mu/n) * self.n
      """
      p = self._clean(mu / self.n)
      return p * (1 - p) * self.n
  
file_path: statsmodels/statsmodels/genmod/families/varfuncs.py
imports: import numpy
label_desc: Use image node_id 3 for calling the Binomial obj's underlying member method code with example usage: obj.__call__(mu) and returns: unknown