本文整理汇总了Python中skbio.stats.composition.ancom函数的典型用法代码示例。如果您正苦于以下问题：Python ancom函数的具体用法？Python ancom怎么用？Python ancom使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了ancom函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_ancom_tau
def test_ancom_tau(self):
exp1 = pd.DataFrame({'W': np.array([8, 7, 3, 3, 7, 3, 3, 3, 3]),
'reject': np.array([True, False, False, False,
False, False, False, False,
False], dtype=bool)})
exp2 = pd.DataFrame({'W': np.array([17, 17, 5, 6, 16, 5, 7, 5,
4, 5, 8, 4, 5, 16, 5, 11, 4, 6]),
'reject': np.array([True, True, False, False,
True, False, False, False,
False, False, False, False,
False, True, False, False,
False, False], dtype=bool)})
exp3 = pd.DataFrame({'W': np.array([16, 16, 17, 10, 17, 16, 16,
15, 15, 15, 13, 10, 10, 10,
9, 9, 9, 9]),
'reject': np.array([True, True, True, False,
True, True, True, True,
True, True, True, False,
False, False, False, False,
False, False], dtype=bool)})
result1 = ancom(self.table4, self.cats4, tau=0.25)
result2 = ancom(self.table9, self.cats9, tau=0.02)
result3 = ancom(self.table10, self.cats10, tau=0.02)
assert_data_frame_almost_equal(result1, exp1)
assert_data_frame_almost_equal(result2, exp2)
assert_data_frame_almost_equal(result3, exp3)示例2: test_ancom_tau
def test_ancom_tau(self):
exp1 = pd.DataFrame(
{'W': np.array([8, 7, 3, 3, 7, 3, 3, 3, 3]),
'Reject null hypothesis': np.array([True, False, False, False,
False, False, False, False,
False], dtype=bool)})
exp2 = pd.DataFrame(
{'W': np.array([17, 17, 5, 6, 16, 5, 7, 5,
4, 5, 8, 4, 5, 16, 5, 11, 4, 6]),
'Reject null hypothesis': np.array([True, True, False, False,
True, False, False, False,
False, False, False, False,
False, True, False, False,
False, False], dtype=bool)})
exp3 = pd.DataFrame(
{'W': np.array([16, 16, 17, 10, 17, 16, 16,
15, 15, 15, 13, 10, 10, 10,
9, 9, 9, 9]),
'Reject null hypothesis': np.array([True, True, True, False,
True, True, True, True,
True, True, True, False,
False, False, False, False,
False, False], dtype=bool)})
result1 = ancom(self.table4, self.cats4,
multiple_comparisons_correction=None, tau=0.25)
result2 = ancom(self.table9, self.cats9,
multiple_comparisons_correction=None, tau=0.02)
result3 = ancom(self.table10, self.cats10,
multiple_comparisons_correction=None, tau=0.02)
assert_data_frame_almost_equal(result1[0], exp1)
assert_data_frame_almost_equal(result2[0], exp2)
assert_data_frame_almost_equal(result3[0], exp3)示例3: test_ancom_duplicate_percentiles
def test_ancom_duplicate_percentiles(self):
table = pd.DataFrame([[12],
[9],
[1],
[22],
[20],
[23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
with self.assertRaises(ValueError):
ancom(table, grouping, percentiles=[10.0, 10.0])示例4: test_ancom_no_signal
def test_ancom_no_signal(self):
result = ancom(self.table3,
self.cats3,
multiple_comparisons_correction=None)
exp = pd.DataFrame({'W': np.array([0]*7),
'reject': np.array([False]*7, dtype=bool)})
assert_data_frame_almost_equal(result, exp)示例5: test_ancom_percentile_order_unimportant
def test_ancom_percentile_order_unimportant(self):
table = pd.DataFrame([[12],
[9],
[1],
[22],
[20],
[23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
# order of percentiles in unimportant after sorting
result1 = ancom(table, grouping, percentiles=[50.0, 42.0])[1]
result2 = ancom(table, grouping, percentiles=[42.0, 50.0])[1]
assert_data_frame_almost_equal(
result1.sort_index(axis=1), result2.sort_index(axis=1))示例6: test_ancom_percentiles
def test_ancom_percentiles(self):
table = pd.DataFrame([[12, 11],
[9, 11],
[1, 11],
[22, 100],
[20, 53],
[23, 1]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1', 'b2'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
percentiles = [0.0, 25.0, 50.0, 75.0, 100.0]
groups = ['a', 'b']
tuples = [(p, g) for g in groups for p in percentiles]
exp_mi = pd.MultiIndex.from_tuples(tuples,
names=['Percentile', 'Group'])
exp_data = np.array(
[[1.0, 11.0], [5.0, 11.0], [9.0, 11.0], [10.5, 11.0], [12.0, 11.0],
[20.0, 1.0], [21.0, 27.0], [22.0, 53.0], [22.5, 76.5],
[23.0, 100.0]])
exp = pd.DataFrame(exp_data.T, columns=exp_mi, index=['b1', 'b2'])
result = ancom(table, grouping)[1]
assert_data_frame_almost_equal(result, exp)示例7: test_ancom_basic_counts_swapped
def test_ancom_basic_counts_swapped(self):
result = ancom(self.table8, self.cats8)
exp = pd.DataFrame({'W': np.array([5, 5, 2, 2, 2, 2, 2]),
'reject': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)示例8: test_ancom_alpha
def test_ancom_alpha(self):
result = ancom(self.table1, self.cats1, alpha=0.5)
exp = pd.DataFrame({'W': np.array([6, 6, 4, 5, 5, 4, 2]),
'reject': np.array([True, True, False, True,
True, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)示例9: test_ancom_theta
def test_ancom_theta(self):
result = ancom(self.table1, self.cats1, theta=0.3)
exp = pd.DataFrame(
{'W': np.array([5, 5, 2, 2, 2, 2, 2]),
'Reject null hypothesis': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], exp)示例10: test_ancom_multiple_comparisons
def test_ancom_multiple_comparisons(self):
result = ancom(self.table1,
self.cats1,
multiple_comparisons_correction='holm-bonferroni',
significance_test=scipy.stats.mannwhitneyu)
exp = pd.DataFrame({'W': np.array([0]*7),
'reject': np.array([False]*7, dtype=bool)})
assert_data_frame_almost_equal(result, exp)示例11: test_ancom_alpha
def test_ancom_alpha(self):
result = ancom(self.table1, self.cats1,
multiple_comparisons_correction=None, alpha=0.5)
exp = pd.DataFrame(
{'W': np.array([6, 6, 4, 5, 5, 4, 2]),
'Reject null hypothesis': np.array([True, True, False, True,
True, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], exp)示例12: test_ancom_noncontiguous
def test_ancom_noncontiguous(self):
result = ancom(self.table5,
self.cats5,
multiple_comparisons_correction=None)
exp = pd.DataFrame({'W': np.array([6, 2, 2, 2, 2, 6, 2]),
'reject': np.array([True, False, False, False,
False, True, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)示例13: test_ancom_letter_categories
def test_ancom_letter_categories(self):
result = ancom(self.table7,
self.cats7,
multiple_comparisons_correction=None)
exp = pd.DataFrame({'W': np.array([5, 3, 3, 2, 2, 5, 2]),
'reject': np.array([True, False, False, False,
False, True, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)示例14: test_ancom_unbalanced
def test_ancom_unbalanced(self):
result = ancom(self.table6,
self.cats6,
multiple_comparisons_correction=None)
exp = pd.DataFrame(
{'W': np.array([5, 3, 3, 2, 2, 5, 2]),
'Reject null hypothesis': np.array([True, False, False, False,
False, True, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], exp)示例15: test_ancom_alternative_test
def test_ancom_alternative_test(self):
result = ancom(self.table1,
self.cats1,
multiple_comparisons_correction=None,
significance_test=scipy.stats.ttest_ind)
exp = pd.DataFrame({'W': np.array([5, 5, 2, 2, 2, 2, 2]),
'reject': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)