[FIX] p-values going to zero, replace with epsilon (#124)

* fix and test small p_values

* fix flake8

pymare/results.py

@@ -84,16 +84,18 @@ def get_fe_stats(self, alpha=0.05):
             =========== ==========================================================================
         """
         beta, se = self.fe_params, self.fe_se
+        epsilon = np.finfo(beta.dtype).eps
         z_se = ss.norm.ppf(1 - alpha / 2)
         z = beta / se
-
+        p = 1 - np.abs(0.5 - ss.norm.cdf(z)) * 2
+        p[p == 0] += epsilon
         stats = {
             "est": beta,
             "se": se,
             "ci_l": beta - z_se * se,
             "ci_u": beta + z_se * se,
             "z": z,
-            "p": 1 - np.abs(0.5 - ss.norm.cdf(z)) * 2,
+            "p": p,
         }
 
         return stats

pymare/tests/conftest.py

@@ -15,12 +15,27 @@ def variables():
     return (y, v, X)
 
 
+@pytest.fixture(scope="package")
+def small_variance_variables(variables):
+    """Make highly correlated variables."""
+    y, v, X = variables
+    y = X.copy()
+    v /= 10
+    return (y, v, X)
+
+
 @pytest.fixture(scope="package")
 def dataset(variables):
     """Build a Dataset compiled from the variables fixture."""
     return Dataset(*variables, X_names=["my_covariate"])
 
 
+@pytest.fixture(scope="package")
+def small_variance_dataset(small_variance_variables):
+    """Build a Dataset compiled from the small variance variables fixture."""
+    return Dataset(*small_variance_variables, X_names=["my_covariate"])
+
+
 @pytest.fixture(scope="package")
 def small_dataset_2d(variables):
     """Build a small Dataset with 2D data."""

pymare/tests/test_results.py

@@ -21,12 +21,25 @@ def fitted_estimator(dataset):
     return est.fit_dataset(dataset)
 
 
+@pytest.fixture
+def small_variance_estimator(small_variance_dataset):
+    """Create a fitted Estimator with small variances as a fixture."""
+    est = DerSimonianLaird()
+    return est.fit_dataset(small_variance_dataset)
+
+
 @pytest.fixture
 def results(fitted_estimator):
     """Create a results object as a fixture."""
     return fitted_estimator.summary()
 
 
+@pytest.fixture
+def small_variance_results(small_variance_estimator):
+    """Create a results object with small variances as a fixture."""
+    return small_variance_estimator.summary()
+
+
 @pytest.fixture
 def results_2d(fitted_estimator, dataset_2d):
     """Create a 2D results object as a fixture."""
@@ -169,6 +182,15 @@ def test_mrr_to_df(results):
     assert np.allclose(df["p-value"].values, [0.9678, 0.4369], atol=1e-4)
 
 
+def test_small_variance_mrr_to_df(small_variance_results):
+    """Test conversion of MetaRegressionResults to DataFrame."""
+    df = small_variance_results.to_df()
+    assert df.shape == (2, 7)
+    col_names = {"estimate", "p-value", "z-score", "ci_0.025", "ci_0.975", "se", "name"}
+    assert set(df.columns) == col_names
+    assert np.allclose(df["p-value"].values, [1, np.finfo(np.float64).eps], atol=1e-4)
+
+
 def test_estimator_summary(dataset):
     """Test Estimator's summary method."""
     est = WeightedLeastSquares()