code: def test_attributes(self):
      data = ds.df
  
      mask_drop = data["apply"] == "somewhat likely"
      data2 = data.loc[~mask_drop, :].copy()
      # we need to remove the category also from the Categorical Index
      data2["apply"] = data2["apply"].cat.remove_categories(
          "somewhat likely"
      )
  
      # standard fit with pandas input
      modp = OrderedModel(
          data2["apply"],
          data2[["pared", "public", "gpa"]],
          distr="logit",
      )
      resp = modp.fit(method="bfgs", disp=False)
  
      exog = add_constant(
          data2[["pared", "public", "gpa"]], prepend=False
      )
      mod_logit = Logit(data2["apply"].cat.codes, exog)
      res_logit = mod_logit.fit()
  
      attributes = "bse df_resid llf aic bic llnull".split()
      attributes += "llnull llr llr_pvalue prsquared".split()
      params = np.asarray(resp.params)
      logit_params = np.asarray(res_logit.params)
      assert_allclose(params[:3], logit_params[:3], rtol=1e-5)
      assert_allclose(params[3], -logit_params[3], rtol=1e-5)
      for attr in attributes:
          assert_allclose(
              getattr(resp, attr), getattr(res_logit, attr), rtol=1e-4
          )
  
      resp = modp.fit(
          method="bfgs",
          disp=False,
          cov_type="hac",
          cov_kwds={"maxlags": 2},
      )
      res_logit = mod_logit.fit(
          method="bfgs",
          disp=False,
          cov_type="hac",
          cov_kwds={"maxlags": 2},
      )
      for attr in attributes:
          assert_allclose(
              getattr(resp, attr), getattr(res_logit, attr), rtol=1e-4
          )
  
      resp = modp.fit(method="bfgs", disp=False, cov_type="hc1")
      res_logit = mod_logit.fit(
          method="bfgs", disp=False, cov_type="hc1"
      )
      for attr in attributes:
          assert_allclose(
              getattr(resp, attr), getattr(res_logit, attr), rtol=1e-4
          )
  
imports: import warnings
  import numpy
  from numpy.testing import assert_allclose, assert_equal
  import pandas
  import pytest
  import scipy.stats
  from statsmodels.discrete.discrete_model import Logit
  from statsmodels.miscmodels.ordinal_model import OrderedModel
  from statsmodels.tools.sm_exceptions import HessianInversionWarning
  from statsmodels.tools.tools import add_constant
  from .results.results_ordinal_model import data_store
label_desc: Use image node_id 1 for calling the TestLogitBinary obj's underlying member method code with example usage: obj.test_attributes() without return types