Improve effect sizes calculation

Author: Jacobluke-

dabest/_effsize_objects.py

@@ -363,6 +363,16 @@ def _perform_statistical_test(self):
             self.__pvalue_mcnemar = _mcnemar.pvalue
             self.__statistic_mcnemar = _mcnemar.statistic
 
+        elif self.__proportional:
+            # The Cohen's h calculation is for binary categorical data
+            try:
+                self.__proportional_difference = es.cohens_h(
+                    self.__control, self.__test
+                )
+            except ValueError as e:
+                warnings.warn(f"Calculation of Cohen's h failed. This method is applicable "
+                  f"only for binary data (0's and 1's). Details: {e}")
+
         elif self.__effect_size == "cliffs_delta":
             # Let's go with Brunner-Munzel!
             brunner_munzel = spstats.brunnermunzel(
@@ -411,14 +421,6 @@ def _perform_statistical_test(self):
 
             standardized_es = es.cohens_d(self.__control, self.__test, is_paired=None)
 
-            # The Cohen's h calculation is for binary categorical data
-            try:
-                self.__proportional_difference = es.cohens_h(
-                    self.__control, self.__test
-                )
-            except ValueError as e:
-                warnings.warn(f"Calculation of Cohen's h failed. This method is applicable "
-                  f"only for binary data (0's and 1's). Details: {e}")
 
     def to_dict(self):
         """

nbs/API/effsize_objects.ipynb

@@ -425,6 +425,16 @@
     "            self.__pvalue_mcnemar = _mcnemar.pvalue\n",
     "            self.__statistic_mcnemar = _mcnemar.statistic\n",
     "\n",
+    "        elif self.__proportional:\n",
+    "            # The Cohen's h calculation is for binary categorical data\n",
+    "            try:\n",
+    "                self.__proportional_difference = es.cohens_h(\n",
+    "                    self.__control, self.__test\n",
+    "                )\n",
+    "            except ValueError as e:\n",
+    "                warnings.warn(f\"Calculation of Cohen's h failed. This method is applicable \"\n",
+    "                  f\"only for binary data (0's and 1's). Details: {e}\")\n",
+    "\n",
     "        elif self.__effect_size == \"cliffs_delta\":\n",
     "            # Let's go with Brunner-Munzel!\n",
     "            brunner_munzel = spstats.brunnermunzel(\n",
@@ -473,14 +483,6 @@
     "\n",
     "            standardized_es = es.cohens_d(self.__control, self.__test, is_paired=None)\n",
     "\n",
-    "            # The Cohen's h calculation is for binary categorical data\n",
-    "            try:\n",
-    "                self.__proportional_difference = es.cohens_h(\n",
-    "                    self.__control, self.__test\n",
-    "                )\n",
-    "            except ValueError as e:\n",
-    "                warnings.warn(f\"Calculation of Cohen's h failed. This method is applicable \"\n",
-    "                  f\"only for binary data (0's and 1's). Details: {e}\")\n",
     "\n",
     "    def to_dict(self):\n",
     "        \"\"\"\n",