From 647f2578e9e74ff0b6d952c8c0db25ef9fb5bd9a Mon Sep 17 00:00:00 2001
From: HUANGERIC970823 <eric.huang.personal@outlook.com>
Date: Sun, 18 Feb 2024 12:06:21 +0800
Subject: [PATCH] dunnett alter arg and function test

---
 scikit_posthocs/_posthocs.py | 10 ++++++++--
 tests/test_posthocs.py       | 15 +++++++++++++++
 2 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/scikit_posthocs/_posthocs.py b/scikit_posthocs/_posthocs.py
index 137c01d..23f24b3 100644
--- a/scikit_posthocs/_posthocs.py
+++ b/scikit_posthocs/_posthocs.py
@@ -1,5 +1,5 @@
 import itertools as it
-from typing import Tuple, Union
+from typing import Tuple, Union, Literal
 import numpy as np
 import scipy.stats as ss
 from statsmodels.sandbox.stats.multicomp import multipletests
@@ -1565,6 +1565,7 @@ def posthoc_dunnett(a: Union[list, np.ndarray, DataFrame],
                     val_col: str = None,
                     group_col: str = None,
                     control: str = None,
+                    alternative: Literal['two-sided', 'less', 'greater'] = 'two-sided',
                     sort: bool = False,
                     to_matrix: bool = True) -> Series | DataFrame:
     """
@@ -1591,6 +1592,11 @@ def posthoc_dunnett(a: Union[list, np.ndarray, DataFrame],
         Name of the control group within the `group_col` column. Values should
         have a nominal scale (categorical). Must be specified if `a` is a pandas
 
+    alternative : ['two-sided', 'less', or 'greater'], optional
+        Whether to get the p-value for the one-sided hypothesis
+        ('less' or 'greater') or for the two-sided hypothesis ('two-sided').
+        Defaults to 'two-sided'.
+
     sort : bool, optional
         Specifies whether to sort DataFrame by group_col or not. Recommended
         unless you sort your data manually.
@@ -1618,7 +1624,7 @@ def posthoc_dunnett(a: Union[list, np.ndarray, DataFrame],
     control_data = x_embedded.loc[control]
     treatment_data = x_embedded.drop(control)
 
-    pvals = ss.dunnett(*treatment_data, control=control_data).pvalue
+    pvals = ss.dunnett(*treatment_data, control=control_data, alternative=alternative).pvalue
 
     multi_index = MultiIndex.from_product([[control], treatment_data.index.tolist()])
     dunnett_sr = Series(pvals, index=multi_index)
diff --git a/tests/test_posthocs.py b/tests/test_posthocs.py
index 2cb19d4..d601caf 100644
--- a/tests/test_posthocs.py
+++ b/tests/test_posthocs.py
@@ -597,6 +597,21 @@ def test_posthoc_tukey(self):
         self.assertTrue(np.allclose(results, r_results, atol=1.e-3))
 
 
+    def test_posthoc_dunnett(self):
+        r_results = [8.125844e-11, 2.427434e-01]
+        results = sp.posthoc_dunnett(self.df.sort_index(), val_col='pulse', group_col='kind',
+                                     control='rest', to_matrix=False)
+
+        # scipy use randomized Quasi-Monte Carlo integration of the multivariate-t distribution
+        # to compute the p-values. The result may vary slightly from run to run.
+        # we run the test 1000 times (maximum absolute tolerance = 1.e-4 for example data)
+        is_close = []
+        for i in range(1000):
+            is_close.append(np.allclose(results, r_results, atol=1.e-4))
+        is_close = all(is_close)
+        self.assertTrue(is_close)
+
 
 if __name__ == '__main__':
     unittest.main()
+