class_docstr: A class for ANOVA and comparing means.
  
  Parameters
  ----------
  
  endog : list of arrays
      endog should be a list containing 1 dimensional arrays.  Each array
      is the data collected from a certain group.
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docstr: Returns -2 log likelihood, the pvalue and the maximum likelihood
  estimate for a common mean.
  
  Parameters
  ----------
  
  mu : float
      If a mu is specified, ANOVA is conducted with mu as the
      common mean.  Otherwise, the common mean is the maximum
      empirical likelihood estimate of the common mean.
      Default is None.
  
  mu_start : float
      Starting value for commean mean if specific mu is not specified.
      Default = 0.
  
  return_weights : bool
      if TRUE, returns the weights on observations that maximize the
      likelihood.  Default is FALSE.
  
  Returns
  -------
  
  res: tuple
      The log-likelihood, p-value and estimate for the common mean.
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code: def compute_ANOVA(self, mu=None, mu_start=0, return_weights=0):
      """
      Returns -2 log likelihood, the pvalue and the maximum likelihood
      estimate for a common mean.
  
      Parameters
      ----------
  
      mu : float
          If a mu is specified, ANOVA is conducted with mu as the
          common mean.  Otherwise, the common mean is the maximum
          empirical likelihood estimate of the common mean.
          Default is None.
  
      mu_start : float
          Starting value for commean mean if specific mu is not specified.
          Default = 0.
  
      return_weights : bool
          if TRUE, returns the weights on observations that maximize the
          likelihood.  Default is FALSE.
  
      Returns
      -------
  
      res: tuple
          The log-likelihood, p-value and estimate for the common mean.
      """
      if mu is not None:
          llr = self._opt_common_mu(mu)
          pval = 1 - chi2.cdf(llr, self.num_groups - 1)
          if return_weights:
              return llr, pval, mu, self.new_weights
          else:
              return llr, pval, mu
      else:
          res = optimize.fmin_powell(
              self._opt_common_mu, mu_start, full_output=1, disp=False
          )
          llr = res[1]
          mu_common = float(np.squeeze(res[0]))
          pval = 1 - chi2.cdf(llr, self.num_groups - 1)
          if return_weights:
              return llr, pval, mu_common, self.new_weights
          else:
              return llr, pval, mu_common
  
file_path: statsmodels/statsmodels/emplike/elanova.py
imports: import numpy
  from .descriptive import _OptFuncts
  from scipy import optimize
  from scipy.stats import chi2
label_desc: Use image node_id 2 for calling the ANOVA obj's underlying member method code with example usage: obj.compute_ANOVA(mu, mu_start, return_weights) and returns: llr,  pval,  mu,  self, llr,  pval,  mu, llr,  pval,  mu_common,  self, llr,  pval,  mu_common