Merge pull request #1 from ACCLAB/master

pull from upstream

Author: josesho

.gitignore

@@ -1,5 +1,6 @@
 # Byte-compiled / optimized / DLL files
 __pycache__/
+.idea/*
 *.py[cod]
 *$py.class
 

.travis.yml

@@ -5,6 +5,7 @@ env:
   - PYTHON=3.5 BACKEND=agg
   - PYTHON=3.6 BACKEND=agg
   - PYTHON=3.7 BACKEND=agg
+  # - PYTHON=3.8 BACKEND=agg
 
 
 before_install:
@@ -21,6 +22,7 @@ install:
   - conda update conda --yes
   - conda config --add channels conda-forge
   - conda create -n testenv --yes pip python=$PYTHON matplotlib
+  # - if [ "$PYTHON" = "3.8" ]; then conda create -n testenv --yes --channel conda-forge/label/pre-3.8 pip python=$PYTHON; else conda create -n testenv --yes pip python=$PYTHON matplotlib; fi
   - source activate testenv
   - pip install .[dev]
 

README.md

@@ -2,11 +2,10 @@
 [![Travis CI build status](https://travis-ci.org/ACCLAB/DABEST-python.svg?branch=master)](https://travis-ci.org/ACCLAB/DABEST-python)
 [![minimal Python version](https://img.shields.io/badge/Python%3E%3D-3.5-6666ff.svg)](https://www.anaconda.com/distribution/)
 [![PyPI version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)
-[![PyPI Downloads](https://img.shields.io/pypi/dm/dabest?color=bright-green)](https://pypistats.org/packages/dabest)
+[![Downloads](https://pepy.tech/badge/dabest/month)](https://pepy.tech/project/dabest/month)
 [![Free-to-view citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4)
 [![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html)
 
-
 ## About
 
 DABEST is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.

dabest/__init__.py

@@ -23,4 +23,4 @@
 from ._stats_tools import effsize as effsize
 from ._classes import TwoGroupsEffectSize 
 
-__version__ = "0.2.6"
+__version__ = "0.2.8"

dabest/_classes.py

@@ -143,6 +143,11 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples,
                                 value_vars=all_plot_groups,
                                 value_name=self.__yvar,
                                 var_name=self.__xvar)
+                                
+        # Added in v0.2.7.
+        # remove any NA rows.
+        plot_data.dropna(axis=0, how='any', subset=[self.__yvar], inplace=True)
+
 
         # Lines 131 to 140 added in v0.2.3.
         # Fixes a bug that jammed up when the xvar column was already 
@@ -444,6 +449,9 @@ def __init__(self, control, test, effect_size,
          'pct_low': -0.763588353717278,
          'pvalue_brunner_munzel': nan,
          'pvalue_kruskal': nan,
+         'pvalue_lqrt_paired': nan,
+         'pvalue_lqrt_unpaired_equal_variance': 0.36,
+         'pvalue_lqrt_unpaired_unequal_variance': 0.36,
          'pvalue_mann_whitney': 0.2600723060808019,
          'pvalue_paired_students_t': nan,
          'pvalue_students_t': 0.34743913903372836,
@@ -453,6 +461,9 @@ def __init__(self, control, test, effect_size,
          'resamples': 5000,
          'statistic_brunner_munzel': nan,
          'statistic_kruskal': nan,
+         'statistic_lqrt_paired': nan,
+         'statistic_lqrt_unpaired_equal_variance': 0.8894980773231964,
+         'statistic_lqrt_unpaired_unequal_variance': 0.8916901409507432,
          'statistic_mann_whitney': 406.0,
          'statistic_paired_students_t': nan,
          'statistic_students_t': 0.9472545159069105,
@@ -465,6 +476,7 @@ def __init__(self, control, test, effect_size,
         from numpy.random import choice, seed
 
         import scipy.stats as spstats
+        import lqrt
 
         # import statsmodels.stats.power as power
 
@@ -596,6 +608,12 @@ def __init__(self, control, test, effect_size,
             wilcoxon = spstats.wilcoxon(control, test)
             self.__pvalue_wilcoxon = wilcoxon.pvalue
             self.__statistic_wilcoxon = wilcoxon.statistic
+            
+            lqrt_result = lqrt.lqrtest_rel(control, test, 
+                                    random_state=random_seed)
+                            
+            self.__pvalue_paired_lqrt = lqrt_result.pvalue
+            self.__statistic_paired_lqrt = lqrt_result.statistic
 
             if effect_size != "median_diff":
                 # Paired Student's t-test.
@@ -653,7 +671,24 @@ def __init__(self, control, test, effect_size,
                 # in terms of rank (eg. all zeros.)
                 pass
 
+            # Likelihood Q-Ratio test:
+            lqrt_equal_var_result = lqrt.lqrtest_ind(control, test, 
+                                        random_state=random_seed,
+                                        equal_var=True)
+                            
+            self.__pvalue_lqrt_equal_var = lqrt_equal_var_result.pvalue
+            self.__statistic_lqrt_equal_var = lqrt_equal_var_result.statistic
+            
+            lqrt_unequal_var_result = lqrt.lqrtest_ind(control, test, 
+                                        random_state=random_seed,
+                                        equal_var=False)
+                                        
+            self.__pvalue_lqrt_unequal_var = lqrt_unequal_var_result.pvalue
+            self.__statistic_lqrt_unequal_var = lqrt_unequal_var_result.statistic
+                    
+
             standardized_es = es.cohens_d(control, test, is_paired=False)
+            
             # self.__power = power.tt_ind_solve_power(standardized_es,
             #                                         len(control),
             #                                         alpha=self.__alpha,
@@ -963,6 +998,65 @@ def statistic_mann_whitney(self):
 
 
 
+    
+    @property
+    def pvalue_lqrt_paired(self):
+        from numpy import nan as npnan
+        try:
+            return self.__pvalue_paired_lqrt
+        except AttributeError:
+            return npnan
+
+
+
+    @property
+    def statistic_lqrt_paired(self):
+        from numpy import nan as npnan
+        try:
+            return self.__statistic_paired_lqrt
+        except AttributeError:
+            return npnan
+            
+    
+    @property
+    def pvalue_lqrt_unpaired_equal_variance(self):
+        from numpy import nan as npnan
+        try:
+            return self.__pvalue_lqrt_equal_var
+        except AttributeError:
+            return npnan
+
+
+
+    @property
+    def statistic_lqrt_unpaired_equal_variance(self):
+        from numpy import nan as npnan
+        try:
+            return self.__statistic_lqrt_equal_var
+        except AttributeError:
+            return npnan
+            
+            
+    @property
+    def pvalue_lqrt_unpaired_unequal_variance(self):
+        from numpy import nan as npnan
+        try:
+            return self.__pvalue_lqrt_unequal_var
+        except AttributeError:
+            return npnan
+
+
+
+    @property
+    def statistic_lqrt_unpaired_unequal_variance(self):
+        from numpy import nan as npnan
+        try:
+            return self.__statistic_lqrt_unequal_var
+        except AttributeError:
+            return npnan
+    
+    
+    
     # @property
     # def power(self):
     #     from numpy import nan as npnan
@@ -1084,7 +1178,16 @@ def __pre_calc(self):
                             'statistic_paired_students_t',
 
                             'pvalue_kruskal',
-                            'statistic_kruskal']
+                            'statistic_kruskal',
+                            
+                            'pvalue_lqrt_paired',
+                            'statistic_lqrt_paired', 
+                            
+                            'pvalue_lqrt_unpaired_equal_variance', 
+                            'statistic_lqrt_unpaired_equal_variance',
+                            
+                            'pvalue_lqrt_unpaired_unequal_variance',
+                            'statistic_lqrt_unpaired_unequal_variance']
 
         self.__results   = out_.reindex(columns=columns_in_order)
         self.__results.dropna(axis="columns", how="all", inplace=True)
@@ -1204,6 +1307,12 @@ def plot(self, color_col=None,
             pyplot.violinplot` command here, as a dict. If None, the following
             keywords are passed to violinplot : {'widths':0.5, 'vert':True,
             'showextrema':False, 'showmedians':False}.
+        slopegraph_kwargs : dict, default None
+            This will change the appearance of the lines used to join each pair
+            of observations when `show_pairs=True`. Pass any keyword arguments
+            accepted by matplotlib `plot()` function here, as a dict.
+            If None, the following keywords are
+            passed to plot() : {'linewidth':1, 'alpha':0.5}.
         reflines_kwargs : dict, default None
             This will change the appearance of the zero reference lines. Pass
             any keyword arguments accepted by the matplotlib Axes `hlines`

dabest/plotter.py

@@ -130,7 +130,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
     if plot_kwargs["slopegraph_kwargs"] is None:
         slopegraph_kwargs = default_slopegraph_kwargs
     else:
-        slopegraph_kwargs = merge_two_dicts(slopegraph_kwargs,
+        slopegraph_kwargs = merge_two_dicts(default_slopegraph_kwargs,
                                             plot_kwargs["slopegraph_kwargs"])
 
 
@@ -697,7 +697,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
         else:
             contrast_ylim_high, contrast_ylim_low = contrast_axes_ylim
         if contrast_ylim_low < 0 < contrast_ylim_high:
-            contrast_axes.axhline(y=0, lw=0.75, color=ytick_color)
+            contrast_axes.axhline(y=0, **reflines_kwargs)
 
         # Compute the end of each x-axes line.
         rightend_ticks = np.array([len(i)-1 for i in idx]) + np.array(ticks_to_skip)