def test_cochran(self):
"""Test function cochran"""
from pingouin.datasets import read_dataset
df = read_dataset('cochran')
st = cochran(dv='Energetic', within='Time', subject='Subject', data=df)
assert st.loc['cochran', 'Q'] == 6.706
cochran(dv='Energetic', within='Time', subject='Subject', data=df,
export_filename='test_export.csv')
# With a NaN value
df.loc[2, 'Energetic'] = np.nan
cochran(dv='Energetic', within='Time', subject='Subject', data=df)
def test_cochran(self):
"""Test function cochran
http://www.real-statistics.com/anova-repeated-measures/cochrans-q-test/
"""
from pingouin import read_dataset
df = read_dataset('cochran')
st = cochran(dv='Energetic', within='Time', subject='Subject', data=df)
assert st.loc['cochran', 'Q'] == 6.706
assert np.allclose(st.loc['cochran', 'p-unc'], 0.034981)
cochran(dv='Energetic', within='Time', subject='Subject', data=df)
# With a NaN value
df.loc[2, 'Energetic'] = np.nan
cochran(dv='Energetic', within='Time', subject='Subject', data=df)
def test_cochran(self):
"""Test function cochran
http://www.real-statistics.com/anova-repeated-measures/cochrans-q-test/
"""
from pingouin import read_dataset
df = read_dataset('cochran')
st = cochran(dv='Energetic', within='Time', subject='Subject', data=df)
assert round(st.at['cochran', 'Q'], 3) == 6.706
assert np.isclose(st.at['cochran', 'p-unc'], 0.034981)
# With Categorical
df['Time'] = df['Time'].astype('category')
df['Subject'] = df['Subject'].astype('category')
df['Time'] = df['Time'].cat.add_categories("Unused")
st = cochran(dv='Energetic', within='Time', subject='Subject', data=df)
assert round(st.at['cochran', 'Q'], 3) == 6.706
assert np.isclose(st.at['cochran', 'p-unc'], 0.034981)
# With a NaN value
df.loc[2, 'Energetic'] = np.nan
cochran(dv='Energetic', within='Time', subject='Subject', data=df)