class_docstr: Empirical Likelihood inference and estimation for linear regression
  through the origin.
  
  Parameters
  ----------
  endog: nx1 array
      Array of response variables.
  
  exog: nxk array
      Array of exogenous variables.  Assumes no array of ones
  
  Attributes
  ----------
  endog : nx1 array
      Array of response variables
  
  exog : nxk array
      Array of exogenous variables.  Assumes no array of ones.
  
  nobs : float
      Number of observations.
  
  nvar : float
      Number of exogenous regressors.
docstr: Fits the model and provides regression results.
  
  Returns
  -------
  Results : class
      Empirical likelihood regression class.
docstr_tok: ["Fits","the","model","and","provides","regression","results",".","Returns","--","--","--","-","Results",":","class","Empirical","likelihood","regression","class","."]
code: def fit(self):
      """
      Fits the model and provides regression results.
  
      Returns
      -------
      Results : class
          Empirical likelihood regression class.
      """
      exog_with = add_constant(self.exog, prepend=True)
      restricted_model = OLS(self.endog, exog_with)
      restricted_fit = restricted_model.fit()
      restricted_el = restricted_fit.el_test(
          np.array([0]), np.array([0]), ret_params=1
      )
      params = np.squeeze(restricted_el[3])
      beta_hat_llr = restricted_el[0]
      llf = np.sum(np.log(restricted_el[2]))
      return OriginResults(restricted_model, params, beta_hat_llr, llf)
  
file_path: statsmodels/statsmodels/emplike/originregress.py
imports: import numpy
  from scipy import optimize
  from scipy.stats import chi2
  from statsmodels.regression.linear_model import OLS, RegressionResults
  from statsmodels.tools.tools import add_constant
label_desc: Use image node_id 2 for calling the ELOriginRegress obj's underlying member method code with example usage: obj.fit() and returns: OriginResults