some new features

.venv/lib/python3.12/site-packages/statsmodels/robust/tests/test_mquantiles.py

@@ -0,0 +1,94 @@
+'''
+Created on Nov. 29, 2022
+
+Author: Josef Perktold
+License: BSD-3
+'''
+
+import numpy as np
+from numpy.testing import assert_allclose
+import pandas as pd
+
+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
+
+ols = OLS.from_formula
+quantreg = QuantReg.from_formula
+
+
+def mean_func(x):
+    """mean function for example"""
+    return x + 0.25 * x**2
+
+
+def std_func(x):
+    """standard deviation function for example"""
+    return 0.1 * np.exp(2.5 + 0.75 * np.abs(x))
+
+
+class TestMQuantiles():
+
+    @classmethod
+    def setup_class(cls):
+
+        np.random.seed(654123)
+
+        # generate an interesting dataset with heteroscedasticity
+        nobs = 200
+        x = np.random.uniform(-4, 4, nobs)
+        y = mean_func(x) + std_func(x) * np.random.randn(nobs)
+
+        cls.df = pd.DataFrame({'temp': x, 'dens': y})
+
+    def test_ols(self):
+        # test expectile at q=0.5 versus OLS
+
+        res_ols = ols('dens ~ temp + I(temp ** 2.0)', self.df).fit(use_t=False)
+
+        y = res_ols.model.endog
+        xx = res_ols.model.exog
+
+        mq_norm = norms.MQuantileNorm(0.5, norms.LeastSquares())
+        mod_rlm = RLM(y, xx, M=mq_norm)
+        res_rlm = mod_rlm.fit()
+
+        assert_allclose(res_rlm.params, res_ols.params, rtol=1e-10)
+        assert_allclose(res_rlm.bse, res_ols.bse, rtol=1e-10)
+        assert_allclose(res_rlm.pvalues, res_ols.pvalues, rtol=1e-10)
+
+    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)