RunasSudo/scipy-yli
1
Fork 0
Code Issues 1 Pull Requests Releases Activity

Show descriptives in Mann-Whitney test output

Browse Source
This commit is contained in:
RunasSudo 2022-11-09 22:08:27 +11:00
parent ea2bf53ace
commit 0f63b7eb40
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
2 changed files with 50 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
tests/test_mannwhitney.py
View File

@ -33,7 +33,11 @@ def test_mannwhitney_ol6_6():
assert result.pvalue == approx(0.00007, abs=0.00001)
expected_summary = '''U = 6.0; p < 0.001*
expected_summary = ''' After Before
Median (IQR) 10.75 (10.55–10.95) 11.55 (11.20–11.83)
Median (range) 10.75 (11.00–12.10) 11.55 (11.00–12.10)
U = 6.0; p < 0.001*
r = 0.92, Before > After'''
assert result.summary() == expected_summary

50
yli/sig_tests.py
View File

@ -23,7 +23,7 @@ import functools
import warnings
from .config import config
from .utils import Estimate, PValueStyle, as_2groups, check_nan, convert_pandas_nullable, fmt_p
from .utils import ConfidenceInterval, Estimate, PValueStyle, as_2groups, check_nan, convert_pandas_nullable, fmt_p
# ----------------
# Student's t test
@ -42,6 +42,7 @@ class TTestResult:
self.dof = dof
#: *p* value for the *t* statistic (*float*)
self.pvalue = pvalue
#: Name of the first group (*str*)
self.group1 = group1
#: Name of the second group (*str*)
@ -54,6 +55,7 @@ class TTestResult:
self.sd1 = sd1
#: Standard deviation of the second group (*float*)
self.sd2 = sd2
#: Absolute value of the mean difference (:class:`yli.utils.Estimate`)
self.delta = delta
#: Description of the direction of the effect (*str*)
@ -252,25 +254,58 @@ class MannWhitneyResult:
See :func:`yli.mannwhitney`.
"""
def __init__(self, statistic, pvalue, rank_biserial, direction, brunnermunzel=None):
def __init__(self, statistic, pvalue, group1, group2, med1, med2, iqr1, iqr2, range1, range2, rank_biserial, direction, brunnermunzel=None):
#: Lesser of the two Mann–Whitney *U* statistics (*float*)
self.statistic = statistic
#: *p* value for the *U* statistic (*float*)
self.pvalue = pvalue
#: Name of the first group (*str*)
self.group1 = group1
#: Name of the second group (*str*)
self.group2 = group2
#: Median of the first group (*float*)
self.med1 = med1
#: Median of the second group (*float*)
self.med2 = med2
#: Interquartile range of the first group (:class:`yli.utils.ConfidenceInterval`)
self.iqr1 = iqr1
#: Interquartile range of the second group (:class:`yli.utils.ConfidenceInterval`)
self.iqr2 = iqr2
#: Range of the first group (:class:`yli.utils.ConfidenceInterval`)
self.range1 = range2
#: Range of the second group (:class:`yli.utils.ConfidenceInterval`)
self.range2 = range2
#: Absolute value of the rank-biserial correlation (*float*)
self.rank_biserial = rank_biserial
#: Description of the direction of the effect (*str*)
self.direction = direction
#: :class:`BrunnerMunzelResult` on the same data, or *None* if N/A
self.brunnermunzel = brunnermunzel
def _comparison_table(self, html):
"""Return a table showing the medians/IQRs/ranges for each group"""
table_data = {
self.group1: ['{:.2f} ({})'.format(self.med1, self.iqr1.summary()), '{:.2f} ({})'.format(self.med1, self.range1.summary())],
self.group2: ['{:.2f} ({})'.format(self.med2, self.iqr2.summary()), '{:.2f} ({})'.format(self.med2, self.range2.summary())],
}
table = pd.DataFrame(table_data, index=['Median (IQR)', 'Median (range)'])
if html:
return table._repr_html_()
else:
return str(table)
def __repr__(self):
if config.repr_is_summary:
return self.summary()
return super().__repr__()
def _repr_html_(self):
line1 = '<i>U</i> = {:.1f}; <i>p</i> {}<br><i>r</i> = {:.2f}, {}'.format(self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION | PValueStyle.HTML), self.rank_biserial, self.direction)
line1 = '{}<br><i>U</i> = {:.1f}; <i>p</i> {}<br><i>r</i> = {:.2f}, {}'.format(self._comparison_table(True), self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION | PValueStyle.HTML), self.rank_biserial, self.direction)
if self.brunnermunzel:
return line1 + '<br>' + self.brunnermunzel._repr_html_()
else:
@ -283,7 +318,7 @@ class MannWhitneyResult:
:rtype: str
"""
line1 = 'U = {:.1f}; p {}\nr = {:.2f}, {}'.format(self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION), self.rank_biserial, self.direction)
line1 = '{}\n\nU = {:.1f}; p {}\nr = {:.2f}, {}'.format(self._comparison_table(False), self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION), self.rank_biserial, self.direction)
if self.brunnermunzel:
return line1 + '\n' + self.brunnermunzel.summary()
else:
@ -359,6 +394,10 @@ def mannwhitney(df, dep, ind, *, nan_policy='warn', brunnermunzel=True, use_cont
.. code-block:: text
After Before
Median (IQR) 10.75 (10.55–10.95) 11.55 (11.20–11.83)
Median (range) 10.75 (11.00–12.10) 11.55 (11.00–12.10)
U = 6.0; p < 0.001*
r = 0.92, Before > After
@ -398,7 +437,8 @@ def mannwhitney(df, dep, ind, *, nan_policy='warn', brunnermunzel=True, use_cont
return MannWhitneyResult(
statistic=min(u1, u2), pvalue=result.pvalue,
#med1=data1.median(), med2=data2.median(),
group1=group1, group2=group2,
med1=data1.median(), med2=data2.median(), iqr1=ConfidenceInterval(data1.quantile(0.25), data1.quantile(0.75)), iqr2=ConfidenceInterval(data2.quantile(0.25), data2.quantile(0.75)), range1=ConfidenceInterval(data1.min(), data1.max()), range2=ConfidenceInterval(data2.min(), data2.max()),
rank_biserial=r, direction=('{1} > {0}' if u1 < u2 else '{0} > {1}').format(group1, group2))
# ------------------------