Implement mannwhitney

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RunasSudo 2022-10-13 13:14:51 +11:00
parent 1fad506a5e
commit edc82c1658
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
3 changed files with 119 additions and 1 deletions

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tests/test_mannwhitney.py Normal file
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# scipy-yli: Helpful SciPy utilities and recipes
# Copyright © 2022 Lee Yingtong Li (RunasSudo)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from pytest import approx
import numpy as np
import pandas as pd
import yli
def test_mannwhitney_ol6_6():
"""Compare yli.mannwhitney for Ott & Longnecker (2016) example 6.6"""
df = pd.DataFrame({
'Sample': ['Before'] * 12 + ['After'] * 12,
'Oxygen': [11.0, 11.2, 11.2, 11.2, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 11.9, 12.1, 10.2, 10.3, 10.4, 10.6, 10.6, 10.7, 10.8, 10.8, 10.9, 11.1, 11.1, 11.3]
})
result = yli.mannwhitney(df, 'Oxygen', 'Sample', method='asymptotic', alternative='less')
assert result.pvalue == approx(0.00007, abs=0.00001)

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# along with this program. If not, see <https://www.gnu.org/licenses/>. # along with this program. If not, see <https://www.gnu.org/licenses/>.
from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
from .sig_tests import ttest_ind from .sig_tests import mannwhitney, ttest_ind
def reload_me(): def reload_me():
import importlib import importlib

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@ -71,3 +71,87 @@ def ttest_ind(df, dep, ind, *, nan_policy='warn'):
statistic=abs(statistic), dof=dof, pvalue=pvalue, statistic=abs(statistic), dof=dof, pvalue=pvalue,
delta=abs(Estimate(delta, ci0, ci1)), delta=abs(Estimate(delta, ci0, ci1)),
delta_direction=('{0} > {1}' if d1.mean > d2.mean else '{1} > {0}').format(group1, group2)) delta_direction=('{0} > {1}' if d1.mean > d2.mean else '{1} > {0}').format(group1, group2))
# -----------------
# Mann-Whitney test
class MannWhitneyResult:
"""
Result of a Mann-Whitney test
brunnermunzel: BrunnerMunzelResult on same data
"""
def __init__(self, statistic, pvalue, rank_biserial, direction, brunnermunzel=None):
self.statistic = statistic
self.pvalue = pvalue
self.rank_biserial = rank_biserial
self.direction = direction
self.brunnermunzel = brunnermunzel
def _repr_html_(self):
line1 = '<i>U</i> = {:.1f}; <i>p</i> {}<br><i>r</i> = {:.2f}, {}'.format(self.statistic, fmt_p_html(self.pvalue), self.rank_biserial, self.direction)
if self.brunnermunzel:
return line1 + '<br>' + self.brunnermunzel._repr_html_()
else:
return line1
def summary(self):
line1 = 'U = {:.1f}; p {}\nr = {}, {}'.format(self.statistic, fmt_p_text(self.pvalue), self.rank_biserial, self.direction)
if self.brunnermunzel:
return line1 + '\n' + self.brunnermunzel.summary()
else:
return line1
class BrunnerMunzelResult:
"""Result of a Brunner-Munzel test"""
def __init__(self, statistic, pvalue):
self.statistic = statistic
self.pvalue = pvalue
def _repr_html_(self):
return '<i>W</i> = {:.1f}; <i>p</i> {}'.format(self.statistic, fmt_p_html(self.pvalue))
def summary(self):
return 'W = {:.1f}; p {}'.format(self.statistic, fmt_p_text(self.pvalue))
def mannwhitney(df, dep, ind, *, nan_policy='warn', brunnermunzel=True, use_continuity=False, alternative='two-sided', method='auto'):
"""
Perform a Mann-Whitney test
brunnermunzel: Set to False to skip the Brunner-Munzel test
use_continuity, alternative, method: See scipy.stats.mannwhitneyu
"""
# Check for/clean NaNs
df = check_nan(df[[ind, dep]], nan_policy)
# Ensure 2 groups for ind
group1, data1, group2, data2 = as_2groups(df, dep, ind)
# Do Mann-Whitney test
# Stata does not perform continuity correction
result = stats.mannwhitneyu(data1, data2, use_continuity=use_continuity, alternative=alternative, method=method)
u1 = result.statistic
u2 = len(data1) * len(data2) - u1
r = abs(2*u1 / (len(data1) * len(data2)) - 1) # rank-biserial
# If significant, perform a Brunner-Munzel test for our interest
if result.pvalue < 0.05 and brunnermunzel:
result_bm = stats.brunnermunzel(data1, data2)
if result_bm.pvalue >= 0.05:
warnings.warn('Mann-Whitney test is significant but Brunner-Munzel test is not. This could be due to a difference in shape, rather than location.')
return MannWhitneyResult(
statistic=min(u1, u2), pvalue=result.pvalue,
#med1=data1.median(), med2=data2.median(),
rank_biserial=r, direction=('{1} > {0}' if u1 < u2 else '{0} > {1}').format(group1, group2),
brunnermunzel=BrunnerMunzelResult(statistic=result_bm.statistic, pvalue=result_bm.pvalue))
return MannWhitneyResult(
statistic=min(u1, u2), pvalue=result.pvalue,
#med1=data1.median(), med2=data2.median(),
rank_biserial=r, direction=('{1} > {0}' if u1 < u2 else '{0} > {1}').format(group1, group2))