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)