code: def test_options(self):
      # regression tests for options,
      # many might not be implemented in other packages
      data = self.data
  
      # regression numbers from initial run
      statistic, p_value = 1.0173464626246675, 0.3763806150460239
      df = (2.0, 24.40374758005409)
      res = smo.test_scale_oneway(
          data,
          method="unequal",
          center="median",
          transform="abs",
          trim_frac_mean=0.2,
      )
      assert_allclose(res.pvalue, p_value, rtol=1e-13)
      assert_allclose(res.statistic, statistic, rtol=1e-13)
      assert_allclose(res.df, df)
  
      statistic, p_value = 1.0329722145270606, 0.3622778213868562
      df = (1.83153791573948, 30.6733640949525)
      p_value2 = 0.3679999679787619
      df2 = (2, 30.6733640949525)
      res = smo.test_scale_oneway(
          data,
          method="bf",
          center="median",
          transform="abs",
          trim_frac_mean=0.2,
      )
      assert_allclose(res.pvalue, p_value, rtol=1e-13)
      assert_allclose(res.statistic, statistic, rtol=1e-13)
      assert_allclose(res.df, df)
      assert_allclose(res.pvalue2, p_value2, rtol=1e-13)
      assert_allclose(res.df2, df2)
  
      statistic, p_value = 1.7252431333701745, 0.19112038168209514
      df = (2.0, 40.0)
      res = smo.test_scale_oneway(
          data,
          method="equal",
          center="mean",
          transform="square",
          trim_frac_mean=0.2,
      )
      assert_allclose(res.pvalue, p_value, rtol=1e-13)
      assert_allclose(res.statistic, statistic, rtol=1e-13)
      assert_equal(res.df, df)
  
      statistic, p_value = 0.4129696057329463, 0.6644711582864451
      df = (2.0, 40.0)
      res = smo.test_scale_oneway(
          data,
          method="equal",
          center="mean",
          transform=lambda x: np.log(x * x),  # noqa
          trim_frac_mean=0.2,
      )
      assert_allclose(res.pvalue, p_value, rtol=1e-13)
      assert_allclose(res.statistic, statistic, rtol=1e-13)
      assert_allclose(res.df, df)
  
      # compare no transform with standard anova
      res = smo.test_scale_oneway(
          data,
          method="unequal",
          center=0,
          transform="identity",
          trim_frac_mean=0.2,
      )
      res2 = anova_oneway(self.data, use_var="unequal")
  
      assert_allclose(res.pvalue, res2.pvalue, rtol=1e-13)
      assert_allclose(res.statistic, res2.statistic, rtol=1e-13)
      assert_allclose(res.df, res2.df)
  
imports: import numpy
  from numpy.testing import assert_allclose, assert_equal
  import pytest
  from statsmodels.regression.linear_model import OLS
  import statsmodels.stats.power
  import statsmodels.stats.oneway
  from statsmodels.stats.oneway import confint_effectsize_oneway, confint_noncentrality, effectsize_oneway, anova_oneway, anova_generic, equivalence_oneway, equivalence_oneway_generic, power_equivalence_oneway, _power_equivalence_oneway_emp, f2_to_wellek, fstat_to_wellek, wellek_to_f2
  from statsmodels.stats.robust_compare import scale_transform
  from statsmodels.stats.contrast import wald_test_noncent_generic, wald_test_noncent, _offset_constraint
label_desc: Use image node_id 4 for calling the TestOnewayScale obj's underlying member method code with example usage: obj.test_options() without return types