Cleaned up ANOVA alpha thresholds

requirements.txt

@@ -1,2 +1,3 @@
 numpy
-tqdm
+tqdm
+pandas

run_anova_tests.ipynb

@@ -436,15 +436,15 @@
     "    out = {}\n",
     "    \n",
     "    # Run two-way ANOVA with interaction.\n",
-    "    anova = run_two_way_anova_with_interaction(group)\n",
+    "    anova = run_two_way_anova_with_interaction(group, alpha=0.05)\n",
     "    out[\"anova_int\"] = anova.to_dict()\n",
     "\n",
     "    # Depending on interaction significance, perform additional tests.\n",
     "    if anova[\"is_significant\"][\"X1:X2\"]:\n",
-    "        simple_main_1 = run_simple_main_effects(group, \"X1\")\n",
-    "        simple_main_2 = run_simple_main_effects(group, \"X2\")\n",
-    "        tukey_1 = run_tukey_tests(group, \"X1\", True)\n",
-    "        tukey_2 = run_tukey_tests(group, \"X2\", True)\n",
+    "        simple_main_1 = run_simple_main_effects(group, \"X1\", alpha=0.05)\n",
+    "        simple_main_2 = run_simple_main_effects(group, \"X2\", alpha=0.05)\n",
+    "        tukey_1 = run_tukey_tests(group, \"X1\", True, alpha=0.01)\n",
+    "        tukey_2 = run_tukey_tests(group, \"X2\", True, alpha=0.01)\n",
     "        \n",
     "        out[\"simple_main\"] = {\n",
     "            \"X1\": simple_main_1.to_dict(),\n",
@@ -456,9 +456,9 @@
     "            \"X2\": tukey_2.to_dict(\"records\") if tukey_2 is not None else [],\n",
     "        }\n",
     "    else:\n",
-    "        anova_noint = run_two_way_anova_without_interaction(group)\n",
-    "        tukey_1 = run_tukey_tests(group, \"X1\", False)\n",
-    "        tukey_2 = run_tukey_tests(group, \"X2\", False)\n",
+    "        anova_noint = run_two_way_anova_without_interaction(group, alpha=0.05)\n",
+    "        tukey_1 = run_tukey_tests(group, \"X1\", False, alpha=0.01)\n",
+    "        tukey_2 = run_tukey_tests(group, \"X2\", False, alpha=0.01)\n",
     "        \n",
     "        out[\"anova_noint\"] =  anova_noint.to_dict()\n",
     "        \n",

scripts/anova.py

@@ -84,7 +84,7 @@ def calculate_skewness(df):
 
     return coeff*numerator/denominator/se
 
-def test_skewness(df, alpha=0.05):
+def test_skewness(df, alpha=0.01):
     """Test for normality using standardized skewness."""
     # Get data subsets
     subsets = get_subsets(df)
@@ -110,7 +110,7 @@ def test_skewness(df, alpha=0.05):
     table = pd.DataFrame(results, columns=columns)
     return table
 
-def test_shapiro(df, alpha=0.05):
+def test_shapiro(df, alpha=0.01):
     """Test for normality using Shapiro-Wilks."""
 
     # Get data subsets
@@ -135,7 +135,7 @@ def test_shapiro(df, alpha=0.05):
     table = pd.DataFrame(results, columns=columns)
     return table
 
-def test_levene(df, alpha=0.05):
+def test_levene(df, alpha=0.01):
     """Test for homogeneity of variance using Levene's."""
 
     # Get data subsets
@@ -214,7 +214,7 @@ def calculate_effect_means(df):
 
 # ANOVA ========================================================================
 
-def run_two_way_anova_with_interaction(df, alpha=0.05):
+def run_two_way_anova_with_interaction(df, alpha=0.01):
     """Run two-way ANOVA with interaction."""
 
     # Get data levels
@@ -268,7 +268,7 @@ def run_two_way_anova_with_interaction(df, alpha=0.05):
 
     return table
 
-def run_two_way_anova_without_interaction(df, alpha=0.05):
+def run_two_way_anova_without_interaction(df, alpha=0.01):
     """Run two-way ANOVA without interaction."""
 
     # Get data levels
@@ -321,7 +321,7 @@ def run_two_way_anova_without_interaction(df, alpha=0.05):
 
     return table
 
-def run_simple_main_effects(df, factor, alpha=0.05):
+def run_simple_main_effects(df, factor, alpha=0.01):
     """Run simple main effects testing."""
 
     # Get data levels
@@ -380,7 +380,7 @@ def run_simple_main_effects(df, factor, alpha=0.05):
 
     return table
 
-def run_tukey_tests(df, factor, interaction, alpha=0.05):
+def run_tukey_tests(df, factor, interaction, alpha=0.01):
     """Run pairwise Tukey tests."""
 
     # Get data levels
@@ -410,14 +410,14 @@ def run_tukey_tests(df, factor, interaction, alpha=0.05):
             df_subset = df[df[factor_subset] == subset]
 
             # Get results
-            results_summary = pairwise_tukeyhsd(df_subset.Y, df_subset[factor]).summary()
+            results_summary = pairwise_tukeyhsd(df_subset.Y, df_subset[factor], alpha=alpha).summary()
             results_as_csv = results_summary.as_csv().replace(" ", "").split("\n")[2:]
             [results.append([subset] + row.split(",")) for row in results_as_csv]
 
         df = pd.DataFrame(results, columns=columns)
     else:
         columns = ['group1', 'group2', 'meandiff', 'p-adj', 'lower', 'upper', 'is_significant']
-        results_summary = pairwise_tukeyhsd(df.Y, df[factor]).summary()
+        results_summary = pairwise_tukeyhsd(df.Y, df[factor], alpha=alpha).summary()
         results_as_csv = results_summary.as_csv().replace(" ", "").split("\n")[2:]
         results = [row.split(",") for row in results_as_csv]
         df = pd.DataFrame(results, columns=columns)