def test_pairwise_tukey(self):
"""Test function pairwise_tukey"""
df = read_dataset('anova')
stats = pairwise_tukey(dv='Pain threshold', between='Hair color',
data=df)
assert np.allclose([0.074, 0.435, 0.415, 0.004, 0.789, 0.037],
stats.loc[:, 'p-tukey'].values.round(3), atol=0.05)
def test_pairwise_tukey(self):
"""Test function pairwise_tukey.
The p-values are slightly different because of a different algorithm
used to calculate the studentized range approximation, but
significance should be the same.
"""
# Compare with R package `userfriendlyscience` - Hair color dataset
# Update Feb 2021: The userfriendlyscience package has been removed
# from CRAN.
df = read_dataset('anova')
stats = pairwise_tukey(dv='Pain threshold',
between='Hair color',
data=df)
# JASP: [0.0741, 0.4356, 0.4147, 0.0037, 0.7893, 0.0366]
# Pingouin: [0.0742, 0.4369, 0.4160, 0.0037, 0.7697, 0.0367]
assert np.allclose([0.074, 0.435, 0.415, 0.004, 0.789, 0.037],
stats.loc[:, 'p-tukey'].to_numpy().round(3),
atol=0.05)
# Compare with JASP in the Palmer Penguins dataset
# The between factor (Species) is unbalanced.
df = read_dataset("penguins")
stats = df.pairwise_tukey(dv="body_mass_g", between="species").round(4)
assert np.array_equal(stats['A'], ["Adelie", "Adelie", "Chinstrap"])
assert np.array_equal(stats['B'], ["Chinstrap", "Gentoo", "Gentoo"])
assert np.array_equal(stats['diff'], [-32.426, -1375.354, -1342.928])
# SE is different for each group (Tukey-Kramer)
assert np.array_equal(stats['se'], [67.5117, 56.1480, 69.8569])
assert np.array_equal(stats['T'], [-0.4803, -24.4952, -19.2240])
# P-values JASP: [0.8807, 0.0000, 0.0000]
# P-values Pingouin: [0.8694, 0.0010, 0.0010]
sig = stats['p-tukey'].apply(lambda x: 'Yes'
if x < 0.05 else 'No').to_numpy()
assert np.array_equal(sig, ['No', 'Yes', 'Yes'])
# Same but with balanced group
df_balanced = df.groupby('species').head(20).copy()
# To complicate things, let's encode between as a categorical
df_balanced['species'] = df_balanced['species'].astype('category')
stats = df_balanced.pairwise_tukey(dv="body_mass_g",
between="species").round(4)
assert np.array_equal(stats['A'], ["Adelie", "Adelie", "Chinstrap"])
assert np.array_equal(stats['B'], ["Chinstrap", "Gentoo", "Gentoo"])
assert np.array_equal(stats['diff'], [-142.5, -1457.5, -1315.])
# SE is the same for all groups (Tukey HSD)
assert np.array_equal(stats['se'], [142.9475, 142.9475, 142.9475])
assert np.array_equal(stats['T'], [-0.9969, -10.1961, -9.1992])
# P-values JASP: [0.5818, 0.0000, 0.0000]
# P-values Pingouin: [0.5766, 0.0010, 0.0010]
sig = stats['p-tukey'].apply(lambda x: 'Yes'
if x < 0.05 else 'No').to_numpy()
assert np.array_equal(sig, ['No', 'Yes', 'Yes'])