Update mini meta terminology

> Changed mini_meta attribute to is_mini_meta to better fit with other attributes (such as is_paired and is_proportional)
> Changed mini_meta_delta class to mini_meta to better fit with other attributes such as delta_delta

Author: JAnns98

dabest/_dabest_object.py

@@ -55,7 +55,7 @@ def __init__(
         self.__resamples = resamples
         self.__random_seed = random_seed
         self.__is_proportional = proportional
-        self.__mini_meta = mini_meta
+        self.__is_mini_meta = mini_meta
 
         # after this call the attributes self.__experiment_label and self.__x1_level are updated
         self._check_errors(x, y, idx, experiment, experiment_label, x1_level)
@@ -114,7 +114,7 @@ def __repr__(self):
                 )
             )
 
-        if self.__mini_meta:
+        if self.__is_mini_meta:
             comparisons.append("weighted delta (only for mean difference)")
 
         for j, g in enumerate(comparisons):
@@ -399,11 +399,11 @@ def is_proportional(self):
         return self.__is_proportional
 
     @property
-    def mini_meta(self):
+    def is_mini_meta(self):
         """
         Returns the mini_meta boolean parameter.
         """
-        return self.__mini_meta
+        return self.__is_mini_meta
 
     @property
     def _all_plot_groups(self):
@@ -426,7 +426,7 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
                 raise ValueError(err0)
 
         # Check if it is a valid mini_meta case
-        if self.__mini_meta:
+        if self.__is_mini_meta:
             # Only mini_meta calculation but not proportional and delta-delta function
             if self.__is_proportional:
                 err0 = "`proportional` and `mini_meta` cannot be True at the same time."
@@ -692,7 +692,7 @@ def _compute_effectsize_dfs(self):
             experiment_label=self.__experiment_label,
             x1_level=self.__x1_level,
             x2=self.__x2,
-            mini_meta=self.__mini_meta,
+            mini_meta=self.__is_mini_meta,
         )
 
         self.__mean_diff = EffectSizeDataFrame(

dabest/_effsize_objects.py

@@ -847,7 +847,7 @@ def __init__(
         self.__experiment_label = experiment_label
         self.__x2 = x2
         self.__delta2 = delta2
-        self.__mini_meta = mini_meta
+        self.__is_mini_meta = mini_meta
 
     def __pre_calc(self):
         from .misc_tools import print_greeting, get_varname
@@ -920,7 +920,7 @@ def __pre_calc(self):
                     if self.__delta2 and self.__effect_size in ["mean_diff", "delta_g"]:
                         resamp_count = False
                         def_pval = False
-                    elif self.__mini_meta and self.__effect_size == "mean_diff":
+                    elif self.__is_mini_meta and self.__effect_size == "mean_diff":
                         resamp_count = False
                         def_pval = False
                     else:
@@ -1016,17 +1016,17 @@ def __pre_calc(self):
             )
 
         # Create and compute the weighted average statistics
-        if self.__mini_meta and self.__effect_size == "mean_diff":
-            self.__mini_meta_delta = MiniMetaDelta(
+        if self.__is_mini_meta and self.__effect_size == "mean_diff":
+            self.__mini_meta = MiniMetaDelta(
                 self, self.__permutation_count, self.__ci
             )
-            reprs.append(self.__mini_meta_delta.__repr__(header=False))
-        elif self.__mini_meta and self.__effect_size != "mean_diff":
-            self.__mini_meta_delta = "Weighted delta is not supported for {}.".format(
+            reprs.append(self.__mini_meta.__repr__(header=False))
+        elif self.__is_mini_meta and self.__effect_size != "mean_diff":
+            self.__mini_meta = "Weighted delta is not supported for {}.".format(
                 self.__effect_size
             )
         else:
-            self.__mini_meta_delta = (
+            self.__mini_meta = (
                 "`mini_meta` is False; weighted delta is therefore not calculated."
             )
 
@@ -1579,27 +1579,27 @@ def lqrt(self):
             return self.__lqrt_results
 
     @property
-    def mini_meta(self):
+    def is_mini_meta(self):
         """
         Returns the mini_meta boolean parameter.
         """
-        return self.__mini_meta
+        return self.__is_mini_meta
 
     @property
-    def mini_meta_delta(self):
+    def mini_meta(self):
         """
         Returns the mini_meta results.
         """
         try:
-            return self.__mini_meta_delta
+            return self.__mini_meta
         except AttributeError:
             self.__pre_calc()
-            return self.__mini_meta_delta
+            return self.__mini_meta
 
     @property
     def delta_delta(self):
         """
-        Returns the mini_meta results.
+        Returns the delta_delta results.
         """
         try:
             return self.__delta_delta

dabest/forest_plot.py

@@ -55,12 +55,12 @@ def load_plot_data(
     first_contrast = data[0]
     if first_contrast.delta2 is True:
         contrast_type = "delta2"
-    elif first_contrast.mini_meta is True:
+    elif first_contrast.is_mini_meta is True:
         contrast_type = "mini_meta"
     else:
         raise ValueError("The `contrast_type` parameter must be specified as either `delta2` or `mini_meta`.")
 
-    contrast_attr_map = {"delta2": "delta_delta", "mini_meta": "mini_meta_delta"}
+    contrast_attr_map = {"delta2": "delta_delta", "mini_meta": "mini_meta"}
     contrast_attr = contrast_attr_map.get(contrast_type)
 
     if idx is not None and contrast_type == 'delta2':

dabest/misc_tools.py

@@ -115,7 +115,7 @@ def get_params(
     yvar = effectsize_df.yvar
     is_paired = effectsize_df.is_paired
     delta2 = effectsize_df.delta2
-    mini_meta = effectsize_df.mini_meta
+    is_mini_meta = effectsize_df.is_mini_meta
     effect_size = effectsize_df.effect_size
     proportional = effectsize_df.is_proportional
     results = effectsize_df.results
@@ -130,7 +130,7 @@ def get_params(
     else:
         show_delta2 = plot_kwargs["show_delta2"]
 
-    if effect_size != "mean_diff" or not mini_meta:
+    if effect_size != "mean_diff" or not is_mini_meta:
         show_mini_meta = False
     else:
         show_mini_meta = plot_kwargs["show_mini_meta"]

dabest/plot_tools.py

@@ -984,7 +984,7 @@ def contrast_bars_plotter(
         color_col: str, 
         plot_palette_raw: dict, 
         show_mini_meta: bool, 
-        mini_meta_delta: object, 
+        mini_meta: object, 
         show_delta2: bool, 
         delta_delta: object, 
         show_pairs: bool,
@@ -1012,7 +1012,7 @@ def contrast_bars_plotter(
         Dictionary of colors used in the plot.
     show_mini_meta : bool   
         Whether to show the mini meta-analysis.
-    mini_meta_delta : object    
+    mini_meta : object    
         Mini meta-analysis object.
     show_delta2 : bool
         Whether to show the delta-delta.
@@ -1058,7 +1058,7 @@ def contrast_bars_plotter(
             ax_to_plot.add_patch(mpatches.Rectangle((contrast_bars_x-0.25, 0), 0.5, contrast_bars_y, color=contrast_bars_colors[idx_selector], **contrast_bars_kwargs))
 
     if show_mini_meta or show_delta2:
-        diff = mini_meta_delta.difference if show_mini_meta else delta_delta.difference
+        diff = mini_meta.difference if show_mini_meta else delta_delta.difference
         if horizontal:
             ax_to_plot.add_patch(mpatches.Rectangle((0, max(swarm_plot_ax.get_yticks())-0.5), diff, 0.5, color='black', **contrast_bars_kwargs))
         else:
@@ -1150,7 +1150,7 @@ def delta_text_plotter(
         proportional: bool, 
         float_contrast: bool,
         show_mini_meta: bool, 
-        mini_meta_delta: object, 
+        mini_meta: object, 
         show_delta2: bool, 
         delta_delta: object, 
         idx: list
@@ -1182,7 +1182,7 @@ def delta_text_plotter(
         Whether the DABEST plot uses Gardner-Altman or Cummings
     show_mini_meta : bool
         Whether to show the mini meta-analysis.
-    mini_meta_delta : object
+    mini_meta : object
         Mini meta-analysis object.
     show_delta2 : bool
         Whether to show the delta-delta.
@@ -1226,7 +1226,7 @@ def delta_text_plotter(
     for j, tick in enumerate(ticks_to_plot):
         Delta_Values.append(results.difference[int(j)])
     if show_delta2: Delta_Values.append(delta_delta.difference)
-    if show_mini_meta: Delta_Values.append(mini_meta_delta.difference)
+    if show_mini_meta: Delta_Values.append(mini_meta.difference)
 
     # Collect the X-coordinates for the delta text
     delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates')
@@ -1518,13 +1518,13 @@ def plot_minimeta_or_deltadelta_violins(
 
     # Plot the curve
     if show_mini_meta:
-        mini_meta_delta = effectsize_df.mini_meta_delta
-        data = mini_meta_delta.bootstraps_weighted_delta
-        difference = mini_meta_delta.difference
+        mini_meta = effectsize_df.mini_meta
+        data = mini_meta.bootstraps_weighted_delta
+        difference = mini_meta.difference
         if ci_type == "bca":
-            ci_low, ci_high = mini_meta_delta.bca_low, mini_meta_delta.bca_high
+            ci_low, ci_high = mini_meta.bca_low, mini_meta.bca_high
         else:
-            ci_low, ci_high = mini_meta_delta.pct_low, mini_meta_delta.pct_high
+            ci_low, ci_high = mini_meta.pct_low, mini_meta.pct_high
     else:
         delta_delta = effectsize_df.delta_delta
         data = delta_delta.bootstraps_delta_delta
@@ -1803,7 +1803,7 @@ def gridkey_plotter(
         float_contrast: bool, 
         horizontal: bool, 
         delta_delta: object, 
-        mini_meta_delta: object, 
+        mini_meta: object, 
         effect_size: str, 
         gridkey_kwargs: dict,
     ):
@@ -1846,7 +1846,7 @@ def gridkey_plotter(
         If the plot is horizontal.
     delta_delta : object
         delta-delta object.
-    mini_meta_delta : object
+    mini_meta : object
         Mini meta-analysis object.
     effect_size : str
         Type of effect size to plot
@@ -1982,7 +1982,7 @@ def gridkey_plotter(
             if group_idx == 0:
                 added_group = ['', gridkey_marker]
             elif gridkey_show_es and (group_idx == len(table_cellcols)-1) and not horizontal:
-                added_delta_effectsize = delta_delta.difference if show_delta2 else mini_meta_delta.difference
+                added_delta_effectsize = delta_delta.difference if show_delta2 else mini_meta.difference
                 added_delta_effectsize_str = np.format_float_positional(
                                                                         added_delta_effectsize,
                                                                         precision=2,
@@ -2226,7 +2226,7 @@ def table_for_horizontal_plots(
     for n in np.arange(0, len(effectsize_df.results.difference), 1):
         lst.append([effectsize_df.results.difference[n],0])
     if show_mini_meta:
-        lst.append([effectsize_df.mini_meta_delta.difference,0])
+        lst.append([effectsize_df.mini_meta.difference,0])
     elif show_delta2:
         lst.append([effectsize_df.delta_delta.difference,0])
     tab = pd.DataFrame(lst, columns=cols)

dabest/plotter.py

@@ -248,7 +248,7 @@ def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.fi
             )  # Default asymmetric side is right
 
             # swarmplot() plots swarms based on current size of ax
-            # Therefore, since the ax size for mini_meta and show_delta changes later on, there has to be increased jitter
+            # Therefore, since the ax size for show_mini_meta and show_delta changes later on, there has to be increased jitter
             rawdata_plot = swarmplot(
                             data = plot_data,
                             x = xvar,
@@ -501,7 +501,7 @@ def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.fi
                         color_col = color_col, 
                         plot_palette_raw = plot_palette_raw, 
                         show_mini_meta = show_mini_meta, 
-                        mini_meta_delta = effectsize_df.mini_meta_delta if show_mini_meta else None, 
+                        mini_meta = effectsize_df.mini_meta if show_mini_meta else None, 
                         show_delta2 = show_delta2, 
                         delta_delta = effectsize_df.delta_delta if show_delta2 else None, 
                         show_pairs = show_pairs,
@@ -541,7 +541,7 @@ def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.fi
                     proportional = proportional, 
                     float_contrast = float_contrast, 
                     show_mini_meta = show_mini_meta, 
-                    mini_meta_delta = effectsize_df.mini_meta_delta if show_mini_meta else None, 
+                    mini_meta = effectsize_df.mini_meta if show_mini_meta else None, 
                     show_delta2 = show_delta2, 
                     delta_delta = effectsize_df.delta_delta if show_delta2 else None,
                     idx = idx
@@ -582,7 +582,7 @@ def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.fi
                 float_contrast = float_contrast,
                 horizontal = horizontal,
                 delta_delta = effectsize_df.delta_delta if show_delta2 else None,
-                mini_meta_delta = effectsize_df.mini_meta_delta if show_mini_meta else None,
+                mini_meta = effectsize_df.mini_meta if show_mini_meta else None,
                 effect_size = effect_size,
                 gridkey_kwargs = gridkey_kwargs,
                 )

nbs/API/dabest_object.ipynb

@@ -131,7 +131,7 @@
     "        self.__resamples = resamples\n",
     "        self.__random_seed = random_seed\n",
     "        self.__is_proportional = proportional\n",
-    "        self.__mini_meta = mini_meta\n",
+    "        self.__is_mini_meta = mini_meta\n",
     "\n",
     "        # after this call the attributes self.__experiment_label and self.__x1_level are updated\n",
     "        self._check_errors(x, y, idx, experiment, experiment_label, x1_level)\n",
@@ -190,7 +190,7 @@
     "                )\n",
     "            )\n",
     "\n",
-    "        if self.__mini_meta:\n",
+    "        if self.__is_mini_meta:\n",
     "            comparisons.append(\"weighted delta (only for mean difference)\")\n",
     "\n",
     "        for j, g in enumerate(comparisons):\n",
@@ -475,11 +475,11 @@
     "        return self.__is_proportional\n",
     "\n",
     "    @property\n",
-    "    def mini_meta(self):\n",
+    "    def is_mini_meta(self):\n",
     "        \"\"\"\n",
     "        Returns the mini_meta boolean parameter.\n",
     "        \"\"\"\n",
-    "        return self.__mini_meta\n",
+    "        return self.__is_mini_meta\n",
     "\n",
     "    @property\n",
     "    def _all_plot_groups(self):\n",
@@ -502,7 +502,7 @@
     "                raise ValueError(err0)\n",
     "\n",
     "        # Check if it is a valid mini_meta case\n",
-    "        if self.__mini_meta:\n",
+    "        if self.__is_mini_meta:\n",
     "            # Only mini_meta calculation but not proportional and delta-delta function\n",
     "            if self.__is_proportional:\n",
     "                err0 = \"`proportional` and `mini_meta` cannot be True at the same time.\"\n",
@@ -768,7 +768,7 @@
     "            experiment_label=self.__experiment_label,\n",
     "            x1_level=self.__x1_level,\n",
     "            x2=self.__x2,\n",
-    "            mini_meta=self.__mini_meta,\n",
+    "            mini_meta=self.__is_mini_meta,\n",
     "        )\n",
     "\n",
     "        self.__mean_diff = EffectSizeDataFrame(\n",

nbs/API/delta_objects.ipynb

@@ -1068,7 +1068,7 @@
     "                   'Control 2' : c2,     'Test 2' : t2,\n",
     "                   'Control 3' : c3,     'Test 3' : t3})\n",
     "my_dabest_object = dabest.load(my_df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)\n",
-    "my_dabest_object.mean_diff.mini_meta_delta"
+    "my_dabest_object.mean_diff.mini_meta"
    ]
   },
   {
@@ -1077,12 +1077,17 @@
    "source": [
     "As of version 2023.02.14, weighted delta can only be calculated for mean difference, and not for standardized measures such as Cohen's *d*.\n",
     "\n",
-    "Details about the calculated weighted delta are accessed as attributes of the ``mini_meta_delta`` class. See the `minimetadelta` for details on usage.\n",
+    "Details about the calculated weighted delta are accessed as attributes of the ``mini_meta`` class. See the `minimetadelta` for details on usage.\n",
     "\n",
     "Refer to Chapter 10 of the Cochrane handbook for further information on meta-analysis: \n",
     "https://training.cochrane.org/handbook/current/chapter-10\n",
     "\t\t"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {