74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022 Lee Yingtong Li (RunasSudo)
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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import pandas as pd
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from scipy import stats
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import statsmodels.api as sm
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import functools
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import warnings
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from .utils import Estimate, as_2groups, check_nan, fmt_p_html, fmt_p_text
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# ----------------
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# Student's t test
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class TTestResult:
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"""
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Result of a Student's t test
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delta: Mean difference
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"""
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def __init__(self, statistic, dof, pvalue, delta, delta_direction):
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self.statistic = statistic
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self.dof = dof
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self.pvalue = pvalue
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self.delta = delta
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self.delta_direction = delta_direction
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def _repr_html_(self):
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return '<i>t</i>({:.0f}) = {:.2f}; <i>p</i> {}<br><i>δ</i> (95% CI) = {}, {}'.format(self.dof, self.statistic, fmt_p_html(self.pvalue), self.delta.summary(), self.delta_direction)
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def summary(self):
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return 't({:.0f}) = {:.2f}; p {}\nδ (95% CI) = {}, {}'.format(self.dof, self.statistic, fmt_p_text(self.pvalue), self.delta.summary(), self.delta_direction)
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def ttest_ind(df, dep, ind, *, nan_policy='warn'):
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"""Perform an independent-sample Student's t test"""
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# Check for/clean NaNs
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df = check_nan(df[[ind, dep]], nan_policy)
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# Ensure 2 groups for ind
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group1, data1, group2, data2 = as_2groups(df, dep, ind)
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# Do t test
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# Use statsmodels rather than SciPy because this provides the mean difference automatically
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d1 = sm.stats.DescrStatsW(data1)
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d2 = sm.stats.DescrStatsW(data2)
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cm = sm.stats.CompareMeans(d1, d2)
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statistic, pvalue, dof = cm.ttest_ind()
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delta = d1.mean - d2.mean
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ci0, ci1 = cm.tconfint_diff()
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# t test is symmetric so take absolute values
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return TTestResult(
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statistic=abs(statistic), dof=dof, pvalue=pvalue,
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delta=abs(Estimate(delta, ci0, ci1)),
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delta_direction=('{0} > {1}' if d1.mean > d2.mean else '{1} > {0}').format(group1, group2))
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