reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/emplike/tests/test_descriptive.py

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+import numpy as np
+from numpy.testing import assert_almost_equal
+
+from statsmodels.datasets import star98
+from statsmodels.emplike.descriptive import DescStat
+
+from .results.el_results import DescStatRes
+
+
+class GenRes:
+    """
+    Reads in the data and creates class instance to be tested
+    """
+    @classmethod
+    def setup_class(cls):
+        data = star98.load()
+        data.exog = np.asarray(data.exog)
+        desc_stat_data = data.exog[:50, 5]
+        mv_desc_stat_data = data.exog[:50, 5:7]  # mv = multivariate
+        cls.res1 = DescStat(desc_stat_data)
+        cls.res2 = DescStatRes()
+        cls.mvres1 = DescStat(mv_desc_stat_data)
+
+
+class TestDescriptiveStatistics(GenRes):
+    @classmethod
+    def setup_class(cls):
+        super().setup_class()
+
+    def test_test_mean(self):
+        assert_almost_equal(self.res1.test_mean(14),
+                            self.res2.test_mean_14, 4)
+
+    def test_test_mean_weights(self):
+        assert_almost_equal(self.res1.test_mean(14, return_weights=1)[2],
+                            self.res2.test_mean_weights, 4)
+
+    def test_ci_mean(self):
+        assert_almost_equal(self.res1.ci_mean(), self.res2.ci_mean, 4)
+
+    def test_test_var(self):
+        assert_almost_equal(self.res1.test_var(3),
+                            self.res2.test_var_3, 4)
+
+    def test_test_var_weights(self):
+        assert_almost_equal(self.res1.test_var(3, return_weights=1)[2],
+                            self.res2.test_var_weights, 4)
+
+    def test_ci_var(self):
+        assert_almost_equal(self.res1.ci_var(), self.res2.ci_var, 4)
+
+    def test_mv_test_mean(self):
+        assert_almost_equal(self.mvres1.mv_test_mean(np.array([14, 56])),
+                            self.res2.mv_test_mean, 4)
+
+    def test_mv_test_mean_weights(self):
+        assert_almost_equal(self.mvres1.mv_test_mean(np.array([14, 56]),
+                                                     return_weights=1)[2],
+                            self.res2.mv_test_mean_wts, 4)
+
+    def test_test_skew(self):
+        assert_almost_equal(self.res1.test_skew(0),
+                            self.res2.test_skew, 4)
+
+    def test_ci_skew(self):
+        # This will be tested in a round about way since MATLAB fails when
+        # computing CI with multiple nuisance parameters.  The process is:
+        #
+        # (1) Get CI for skewness from ci.skew()
+        # (2) In MATLAB test the hypotheis that skew=results of test_skew.
+        # (3) If p-value approx .05, test confirmed
+        skew_ci = self.res1.ci_skew()
+        lower_lim = skew_ci[0]
+        upper_lim = skew_ci[1]
+        ul_pval = self.res1.test_skew(lower_lim)[1]
+        ll_pval = self.res1.test_skew(upper_lim)[1]
+        assert_almost_equal(ul_pval, .050000, 4)
+        assert_almost_equal(ll_pval, .050000, 4)
+
+    def test_ci_skew_weights(self):
+        assert_almost_equal(self.res1.test_skew(0, return_weights=1)[2],
+                            self.res2.test_skew_wts, 4)
+
+    def test_test_kurt(self):
+        assert_almost_equal(self.res1.test_kurt(0),
+                            self.res2.test_kurt_0, 4)
+
+    def test_ci_kurt(self):
+        # Same strategy for skewness CI
+        kurt_ci = self.res1.ci_kurt(upper_bound=.5, lower_bound=-1.5)
+        lower_lim = kurt_ci[0]
+        upper_lim = kurt_ci[1]
+        ul_pval = self.res1.test_kurt(upper_lim)[1]
+        ll_pval = self.res1.test_kurt(lower_lim)[1]
+        assert_almost_equal(ul_pval, .050000, 4)
+        assert_almost_equal(ll_pval, .050000, 4)
+
+    def test_joint_skew_kurt(self):
+        assert_almost_equal(self.res1.test_joint_skew_kurt(0, 0),
+                            self.res2.test_joint_skew_kurt, 4)
+
+    def test_test_corr(self):
+        assert_almost_equal(self.mvres1.test_corr(.5),
+                            self.res2.test_corr, 4)
+
+    def test_ci_corr(self):
+        corr_ci = self.mvres1.ci_corr()
+        lower_lim = corr_ci[0]
+        upper_lim = corr_ci[1]
+        ul_pval = self.mvres1.test_corr(upper_lim)[1]
+        ll_pval = self.mvres1.test_corr(lower_lim)[1]
+        assert_almost_equal(ul_pval, .050000, 4)
+        assert_almost_equal(ll_pval, .050000, 4)
+
+    def test_test_corr_weights(self):
+        assert_almost_equal(self.mvres1.test_corr(.5, return_weights=1)[2],
+                            self.res2.test_corr_weights, 4)