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ENH: use studentized range over statsmodels for pairwise tests #229

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Feb 9, 2022
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ENH: use studentized range over statsmodels for pairwise tests #229

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6443bd3
ENH: use studentized range over statsmodels for pvalue
swallan Feb 2, 2022
e8266bd
DOC: update docs and add to changelog
swallan Feb 4, 2022
fd37448
ENH: clip studentized_range pvalues to [0,1]
swallan Feb 4, 2022
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2 changes: 1 addition & 1 deletion docs/changelog.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ v0.6.0.dev
**Enhancements**

a. Faster implementation of :py:func:`pingouin.gzscore`, adding all options available in zscore: axis, ddof and nan_policy. Warning: this functions is deprecated and will be removed in pingouin 0.7.0 (use scipy.stats.gzscore instead). See `pull request 210 <https://github.com/raphaelvallat/pingouin/pull/210>`_.

b. Replace use of statsmodels' studentized range distribution functions with more SciPy's more accurate `scipy.stats.studentized_range`. See `pull request 229 <https://github.com/raphaelvallat/pingouin/pull/229>`_.

*************

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26 changes: 13 additions & 13 deletions pingouin/pairwise.py
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Expand Up @@ -9,6 +9,7 @@
from pingouin.multicomp import multicomp
from pingouin.effsize import compute_effsize, convert_effsize
from pingouin.utils import (_check_dataframe, _flatten_list, _postprocess_dataframe)
from scipy.stats import studentized_range

__all__ = ["pairwise_ttests", "pairwise_tukey", "pairwise_gameshowell",
"pairwise_corr"]
Expand Down Expand Up @@ -630,11 +631,10 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize='hedges'):
>>> df = pg.read_dataset('penguins')
>>> df.pairwise_tukey(dv='body_mass_g', between='species').round(3)
A B mean(A) mean(B) diff se T p-tukey hedges
0 Adelie Chinstrap 3700.662 3733.088 -32.426 67.512 -0.480 0.869 -0.070
1 Adelie Gentoo 3700.662 5076.016 -1375.354 56.148 -24.495 0.001 -2.967
2 Chinstrap Gentoo 3733.088 5076.016 -1342.928 69.857 -19.224 0.001 -2.894
0 Adelie Chinstrap 3700.662 3733.088 -32.426 67.512 -0.480 0.881 -0.070
1 Adelie Gentoo 3700.662 5076.016 -1375.354 56.148 -24.495 0.000 -2.967
2 Chinstrap Gentoo 3733.088 5076.016 -1342.928 69.857 -19.224 0.000 -2.894
"""
from statsmodels.stats.libqsturng import psturng

# First compute the ANOVA
# For max precision, make sure rounding is disabled
Expand Down Expand Up @@ -662,9 +662,9 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize='hedges'):
tval = mn / se

# Critical values and p-values
# from statsmodels.stats.libqsturng import qsturng
# crit = qsturng(1 - alpha, ng, df) / np.sqrt(2)
pval = psturng(np.sqrt(2) * np.abs(tval), ng, df)
# crit = studentized_range.ppf(1 - alpha, ng, df) / np.sqrt(2)
pval = studentized_range.sf(np.sqrt(2) * np.abs(tval), ng, df)
pval = np.clip(pval, 0, 1)

# Uncorrected p-values
# from scipy.stats import t
Expand Down Expand Up @@ -784,12 +784,11 @@ def pairwise_gameshowell(data=None, dv=None, between=None, effsize='hedges'):
>>> df = pg.read_dataset('penguins')
>>> pg.pairwise_gameshowell(data=df, dv='body_mass_g',
... between='species').round(3)
A B mean(A) mean(B) diff se T df pval hedges
0 Adelie Chinstrap 3700.662 3733.088 -32.426 59.706 -0.543 152.455 0.834 -0.079
1 Adelie Gentoo 3700.662 5076.016 -1375.354 58.811 -23.386 249.643 0.001 -2.833
2 Chinstrap Gentoo 3733.088 5076.016 -1342.928 65.103 -20.628 170.404 0.001 -3.105
A B mean(A) mean(B) diff se T df pval hedges
0 Adelie Chinstrap 3700.662 3733.088 -32.426 59.706 -0.543 152.455 0.85 -0.079
1 Adelie Gentoo 3700.662 5076.016 -1375.354 58.811 -23.386 249.643 0.00 -2.833
2 Chinstrap Gentoo 3733.088 5076.016 -1342.928 65.103 -20.628 170.404 0.00 -3.105
"""
from statsmodels.stats.libqsturng import psturng

# Check the dataframe
_check_dataframe(dv=dv, between=between, effects='between', data=data)
Expand Down Expand Up @@ -820,7 +819,8 @@ def pairwise_gameshowell(data=None, dv=None, between=None, effsize='hedges'):
(((gvars[g2] / n[g2])**2) / (n[g2] - 1)))

# Compute corrected p-values
pval = psturng(np.sqrt(2) * np.abs(tval), ng, df)
pval = studentized_range.sf(np.sqrt(2) * np.abs(tval), ng, df)
pval = np.clip(pval, 0, 1)

# Uncorrected p-values
# from scipy.stats import t
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