def test_rm_anova2(self):
"""Test function rm_anova2.
Compare with JASP.
"""
data = read_dataset('rm_anova2')
aov = rm_anova(data=data, subject='Subject', within=['Time', 'Metric'],
dv='Performance').round(3)
array_equal(aov.loc[:, 'MS'], [828.817, 682.617, 112.217])
array_equal(aov.loc[:, 'F'], [33.852, 26.959, 12.632])
array_equal(aov.loc[:, 'np2'], [0.790, 0.750, 0.584])
array_equal(aov.loc[:, 'eps'], [1., 0.969, 0.727])
# With different effect sizes
aov = rm_anova(data=data, subject='Subject', within=['Time', 'Metric'],
dv='Performance', effsize="n2").round(3)
array_equal(aov.loc[:, 'n2'], [0.255, 0.419, 0.069])
aov = rm_anova(data=data, subject='Subject', within=['Time', 'Metric'],
dv='Performance', effsize="ng2").round(3)
array_equal(aov.loc[:, 'ng2'], [0.254, 0.359, 0.084])
# 2 factors with missing values. Cannot compare with JASP directly
# because Pingouin applies an automatic removal of missing values
# (on the last factor). JASP uses a regression-based approach which
# can handle missing values.
df2 = read_dataset('rm_missing')
df2.rm_anova(dv='BOLD', within=['Session', 'Time'], subject='Subj')
# Error: more than two factors
with pytest.raises(ValueError):
df2.rm_anova(dv='BOLD', within=['Session', 'Time', 'Wrong'],
subject='Subj')
def test_rm_anova(self):
"""Test function rm_anova.
Compare with JASP"""
rm_anova(dv='Scores', within='Time', subject='Subject', data=df,
correction=False, detailed=False)
rm_anova(dv='Scores', within='Time', subject='Subject', data=df,
correction=True, detailed=False)
aov = rm_anova(dv='Scores', within='Time', subject='Subject', data=df,
correction='auto', detailed=True)
# Compare with JASP
assert np.allclose(aov.loc[0, 'F'], 3.913)
assert np.allclose(np.round(aov.loc[0, 'p-unc'], 3), .023)
assert np.allclose(aov.loc[0, 'np2'], .062)
rm_anova(dv='Scores', within='Time', subject='Subject', data=df,
correction=True, detailed=True)
rm_anova(dv='Scores', within=['Time'], subject='Subject', data=df_nan,
export_filename='test_export.csv')
# Using a wide dataframe with NaN and compare with JASP
data = read_dataset('rm_anova_wide')
aov = data.rm_anova(detailed=True, correction=True)
assert aov.loc[0, 'F'] == 5.201
assert round(aov.loc[0, 'p-unc'], 3) == .007
assert aov.loc[0, 'np2'] == .394
assert aov.loc[0, 'eps'] == .694
assert aov.loc[0, 'W-spher'] == .307
assert round(aov.loc[0, 'p-GG-corr'], 3) == .017
def _rm_anova(self, dv=None, within=None, subject=None, detailed=False,
correction='auto', export_filename=None):
"""One-way and two-way repeated measures ANOVA."""
aov = rm_anova(data=self, dv=dv, within=within, subject=subject,
correction=correction, detailed=detailed,
export_filename=export_filename)
return aov
def test_rm_anova2(self):
"""Test function rm_anova2.
Compare with JASP."""
data = read_dataset('rm_anova2')
aov = rm_anova(data=data,
subject='Subject',
within=['Time', 'Metric'],
dv='Performance',
export_filename='test_export.csv').round(3)
assert aov.loc[0, "MS"] == 828.817
assert aov.loc[1, "MS"] == 682.617
assert aov.loc[2, "MS"] == 112.217
assert aov.loc[0, "F"] == 33.852
assert aov.loc[1, "F"] == 26.959
assert aov.loc[2, "F"] == 12.632
assert aov.loc[0, "np2"] == 0.790
assert aov.loc[1, "np2"] == 0.750
assert aov.loc[2, "np2"] == 0.584
assert aov.loc[0, "eps"] == 1.000
assert aov.loc[1, "eps"] == 0.969
assert aov.loc[2, "eps"] >= 0.500 # 0.5 is the lower bound
# With missing values
df2 = read_dataset('rm_missing')
df2.rm_anova(dv='BOLD', within=['Session', 'Time'], subject='Subj')
def test_rm_anova2(self):
"""Test function rm_anova2.
Compare with JASP."""
data = read_dataset('rm_anova2')
aov = rm_anova(data=data,
subject='Subject',
within=['Time', 'Metric'],
dv='Performance',
export_filename='test_export.csv').round(3)
array_equal(aov.loc[:, 'MS'].values, [828.817, 682.617, 112.217])
array_equal(aov.loc[:, 'F'].values, [33.852, 26.959, 12.632])
array_equal(aov.loc[:, 'np2'].values, [0.790, 0.750, 0.584])
assert aov.loc[0, "eps"] == 1.000
assert aov.loc[1, "eps"] == 0.969
assert aov.loc[2, "eps"] >= 0.500 # 0.5 is the lower bound
# With missing values
df2 = read_dataset('rm_missing')
df2.rm_anova(dv='BOLD', within=['Session', 'Time'], subject='Subj')
# Error: more than two factors
with pytest.raises(ValueError):
df2.rm_anova(dv='BOLD',
within=['Session', 'Time', 'Wrong'],
subject='Subj')
def test_rm_anova2(self):
"""Test function rm_anova2."""
data = pd.DataFrame({
'Subject': [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
'Time': [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1],
'Drug':
['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C'],
'Scores': [3, 4, 5, 7, 2, 4, 5, 8, 5, 7, 3, 8]
})
rm_anova2(dv='Scores',
within=['Time', 'Drug'],
subject='Subject',
data=data,
export_filename='test_export.csv')
rm_anova(dv='Scores',
within=['Time', 'Drug'],
subject='Subject',
data=data)
def test_rm_anova(self):
"""Test function rm_anova."""
rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction=False,
detailed=False)
rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction=True,
detailed=False)
aov = rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction='auto',
detailed=True)
# Compare with JASP
assert np.allclose(aov.loc[0, 'F'], 3.913)
assert np.allclose(np.round(aov.loc[0, 'p-unc'], 3), .023)
assert np.allclose(aov.loc[0, 'np2'], .062)
rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction=True,
detailed=True)
rm_anova(dv='Scores',
within=['Time'],
subject='Subject',
data=df_nan,
export_filename='test_export.csv')
def test_rm_anova(self):
"""Test function rm_anova.
Compare with JASP
"""
rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction=False,
detailed=False)
rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction=True,
detailed=False)
# Compare with JASP
aov = rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction='auto',
detailed=True).round(3)
assert aov.at[0, 'F'] == 3.913
assert aov.at[0, 'p-unc'] == .023
assert aov.at[0, 'np2'] == .062
# Same but with categorical columns
aov = rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df_cat,
correction='auto',
detailed=True).round(3)
assert aov.at[0, 'F'] == 3.913
assert aov.at[0, 'p-unc'] == .023
assert aov.at[0, 'np2'] == .062
# With different effect sizes
aov = rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction='auto',
effsize="n2").round(3)
assert aov.at[0, 'n2'] == .062
aov = rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction='auto',
detailed=True,
effsize="ng2").round(3)
assert aov.at[0, 'ng2'] == .040
rm_anova(dv='Scores',
within='Time',
subject='Subject',
data=df,
correction=True,
detailed=True)
rm_anova(dv='Scores', within=['Time'], subject='Subject', data=df_nan)
# Using a wide dataframe with NaN and compare with JASP
data = read_dataset('rm_anova_wide')
aov = data.rm_anova(detailed=True, correction=True).round(3)
assert aov.at[0, 'F'] == 5.201
assert aov.at[0, 'p-unc'] == .007
assert aov.at[0, 'np2'] == .394
assert aov.at[0, 'eps'] == .694
assert aov.at[0, 'W-spher'] == .307
assert aov.at[0, 'p-GG-corr'] == .017
