code: def test_quantreg(self):
      # test HuberT mquantile versus QuantReg
      # cov_params inference will not agree
      t_eps = 1e-6
  
      mod1 = quantreg("dens ~ temp + I(temp ** 2.0)", self.df)
      y = mod1.endog
      xx = mod1.exog
  
      for q in [0.25, 0.75]:
          # Note HuberT does not agree very closely with quantreg for g=0.5
  
          res1 = mod1.fit(q=q)
  
          mq_norm = norms.MQuantileNorm(q, norms.HuberT(t=t_eps))
          mod_rlm = RLM(y, xx, M=mq_norm)
          res_rlm = mod_rlm.fit()
  
          assert_allclose(res_rlm.params, res1.params, rtol=5e-4)
          assert_allclose(
              res_rlm.fittedvalues, res1.fittedvalues, rtol=1e-3
          )
  
      # for q=0.5, we compare with plain HuberT norm
      q = 0.5
      t_eps = (
          1e-2  # RuntimeWarning an bse are nan 0/0 if t_eps too small
      )
      mod1 = RLM(y, xx, M=norms.HuberT(t=t_eps))
      res1 = mod1.fit()
  
      mq_norm = norms.MQuantileNorm(q, norms.HuberT(t=t_eps))
      mod_rlm = RLM(y, xx, M=mq_norm)
      res_rlm = mod_rlm.fit()
  
      assert_allclose(res_rlm.params, res1.params, rtol=1e-10)
      assert_allclose(
          res_rlm.fittedvalues, res1.fittedvalues, rtol=1e-10
      )
  
file_path: statsmodels/statsmodels/robust/tests/test_mquantiles.py
imports: import numpy
  from numpy.testing import assert_allclose
  import pandas
  from statsmodels.regression.linear_model import OLS
  from statsmodels.regression.quantile_regression import QuantReg
  from statsmodels.robust import norms
  from statsmodels.robust.robust_linear_model import RLM
label_desc: Use image node_id 3 for calling the TestMQuantiles obj's underlying member method code with example usage: obj.test_quantreg() without return types