docstr: Fit the model by maximum likelihood.
  
  Parameters
  ----------
  start_params : array-like
      A vector of starting values for the regression
      coefficients.  If None, a default is chosen.
  maxiter : integer
      The maximum number of iterations
  disp : bool
      Show convergence stats.
  method : str
      The optimization method to use.
  kwds :
      Keyword arguments for the optimizer.
  
  Returns
  -------
  BetaResults instance.
docstr_tok: ["Fit","the","model","by","maximum","likelihood",".","Parameters","--","--","--","--","--","start_params",":","array-like","A","vector","of","starting","values","for","the","regression","coefficients",".","If","None",",","a","default","is","chosen",".","maxiter",":","integer","The","maximum","number","of","iterations","disp",":","bool","Show","convergence","stats",".","method",":","str","The","optimization","method","to","use",".","kwds",":","Keyword","arguments","for","the","optimizer",".","Returns","--","--","--","-","BetaResults","instance","."]
code: def fit(
      self,
      start_params=None,
      maxiter=1000,
      disp=False,
      method="bfgs",
      **kwds,
  ):
      """
      Fit the model by maximum likelihood.
  
      Parameters
      ----------
      start_params : array-like
          A vector of starting values for the regression
          coefficients.  If None, a default is chosen.
      maxiter : integer
          The maximum number of iterations
      disp : bool
          Show convergence stats.
      method : str
          The optimization method to use.
      kwds :
          Keyword arguments for the optimizer.
  
      Returns
      -------
      BetaResults instance.
      """
  
      if start_params is None:
          start_params = self._start_params()
      #           # http://www.ime.usp.br/~sferrari/beta.pdf suggests starting phi
      #           # on page 8
  
      if "cov_type" in kwds:
          # this is a workaround because we cannot tell super to use eim
          if kwds["cov_type"].lower() == "eim":
              self.hess_type = "eim"
              del kwds["cov_type"]
      else:
          self.hess_type = "oim"
  
      res = super().fit(
          start_params=start_params,
          maxiter=maxiter,
          method=method,
          disp=disp,
          **kwds,
      )
      if not isinstance(res, BetaResultsWrapper):
          # currently GenericLikelihoodModel doe not add wrapper
          res = BetaResultsWrapper(res)
      return res
  
imports: import numpy
  from scipy.special import gammaln
  import patsy
  import statsmodels.base.wrapper
  import statsmodels.regression.linear_model
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.base.model import GenericLikelihoodModel, GenericLikelihoodModelResults, _LLRMixin
  from statsmodels.genmod import families
label_desc: Use image node_id 17 for calling the BetaModel obj's underlying member method code with example usage: obj.fit(start_params, maxiter, disp, method) and returns: res