class_docstr: Restricted general least squares model that handles linear constraints
  
  Parameters
  ----------
  endog : array_like
      n length array containing the dependent variable
  exog : array_like
      n-by-p array of independent variables
  constr : array_like
      k-by-p array of linear constraints
  param : array_like or scalar
      p-by-1 array (or scalar) of constraint parameters
  sigma (None): scalar or array_like
      The weighting matrix of the covariance. No scaling by default (OLS).
      If sigma is a scalar, then it is converted into an n-by-n diagonal
      matrix with sigma as each diagonal element.
      If sigma is an n-length array, then it is assumed to be a diagonal
      matrix with the given sigma on the diagonal (WLS).
  
  Notes
  -----
  endog = exog * beta + epsilon
  weights' * constr * beta = param
  
  See Greene and Seaks, "The Restricted Least Squares Estimator:
  A Pedagogical Note", The Review of Economics and Statistics, 1991.
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docstr: Estimated parameters
docstr_tok: ["Estimated","parameters"]
code: def coeffs(self):
      """Estimated parameters"""
      if self._coeffs is None:
          betaLambda = np.dot(self.inv_rwexog, self.rwendog)
          self._coeffs = betaLambda[: self.ncoeffs]
      return self._coeffs
  
file_path: statsmodels/statsmodels/sandbox/rls.py
imports: import numpy
  from statsmodels.regression.linear_model import GLS, RegressionResults
label_desc: Use image node_id 7 for calling the RLS obj's underlying member method code with example usage: obj.coeffs() and returns: self