from statsmodels.compat.pandas import assert_frame_equal from numpy.testing import ( assert_array_almost_equal, assert_equal, assert_raises, ) import pandas as pd from statsmodels.stats.anova import AnovaRM DV = [7, 3, 6, 6, 5, 8, 6, 7, 7, 11, 9, 11, 10, 10, 11, 11, 8, 14, 10, 11, 12, 10, 11, 12, 16, 7, 11, 9, 10, 11, 8, 8, 16, 10, 13, 10, 10, 14, 11, 12, 24, 29, 10, 22, 25, 28, 22, 24, 1, 3, 5, 8, 3, 5, 6, 8, 9, 18, 19, 1, 12, 15, 2, 3, 3, 4, 13, 21, 2, 11, 18, 2, 12, 7, 12, 3, 19, 1, 4, 13, 13, 14, 3, 4, 8, 19, 21, 2, 4, 9, 12, 2, 5, 8, 2, 4] id = [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8] id = ['%d' % i for i in id] A = ['a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'] B = ['a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'c'] D = ['a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'] data = pd.DataFrame([id, A, B, D, DV], index=['id', 'A', 'B', 'D', 'DV']).T data['DV'] = data['DV'].astype('int') def test_single_factor_repeated_measures_anova(): """ Testing single factor repeated measures anova Results reproduces R `ezANOVA` function from library ez """ df = AnovaRM(data.iloc[:16, :], 'DV', 'id', within=['B']).fit() a = [[1, 7, 22.4, 0.002125452]] assert_array_almost_equal(df.anova_table.iloc[:, [1, 2, 0, 3]].values, a, decimal=5) def test_two_factors_repeated_measures_anova(): """ Testing two factors repeated measures anova Results reproduces R `ezANOVA` function from library ez """ df = AnovaRM(data.iloc[:48, :], 'DV', 'id', within=['A', 'B']).fit() a = [[1, 7, 40.14159, 3.905263e-04], [2, 14, 29.21739, 1.007549e-05], [2, 14, 17.10545, 1.741322e-04]] assert_array_almost_equal(df.anova_table.iloc[:, [1, 2, 0, 3]].values, a, decimal=5) def test_three_factors_repeated_measures_anova(): """ Testing three factors repeated measures anova Results reproduces R `ezANOVA` function from library ez """ df = AnovaRM(data, 'DV', 'id', within=['A', 'B', 'D']).fit() a = [[1, 7, 8.7650709, 0.021087505], [2, 14, 8.4985785, 0.003833921], [1, 7, 20.5076546, 0.002704428], [2, 14, 0.8457797, 0.450021759], [1, 7, 21.7593382, 0.002301792], [2, 14, 6.2416695, 0.011536846], [2, 14, 5.4253359, 0.018010647]] assert_array_almost_equal(df.anova_table.iloc[:, [1, 2, 0, 3]].values, a, decimal=5) def test_repeated_measures_invalid_factor_name(): """ Test with a factor name of 'C', which conflicts with patsy. """ assert_raises(ValueError, AnovaRM, data.iloc[:16, :], 'DV', 'id', within=['C']) def test_repeated_measures_collinearity(): data1 = data.iloc[:48, :].copy() data1['E'] = data1['A'] assert_raises(ValueError, AnovaRM, data1, 'DV', 'id', within=['A', 'E']) def test_repeated_measures_unbalanced_data(): assert_raises(ValueError, AnovaRM, data.iloc[1:48, :], 'DV', 'id', within=['A', 'B']) def test_repeated_measures_aggregation(): df1 = AnovaRM(data, 'DV', 'id', within=['A', 'B', 'D']).fit() double_data = pd.concat([data, data], axis=0) df2 = AnovaRM(double_data, 'DV', 'id', within=['A', 'B', 'D'], aggregate_func=pd.Series.mean).fit() assert_frame_equal(df1.anova_table, df2.anova_table) def test_repeated_measures_aggregation_one_subject_duplicated(): df1 = AnovaRM(data, 'DV', 'id', within=['A', 'B', 'D']).fit() data2 = pd.concat([data, data.loc[data['id'] == '1', :]], axis=0) data2 = data2.reset_index() df2 = AnovaRM(data2, 'DV', 'id', within=['A', 'B', 'D'], aggregate_func=pd.Series.mean).fit() assert_frame_equal(df1.anova_table, df2.anova_table) def test_repeated_measures_aggregate_func(): double_data = pd.concat([data, data], axis=0) assert_raises(ValueError, AnovaRM, double_data, 'DV', 'id', within=['A', 'B', 'D']) m1 = AnovaRM(double_data, 'DV', 'id', within=['A', 'B', 'D'], aggregate_func=pd.Series.mean) m2 = AnovaRM(double_data, 'DV', 'id', within=['A', 'B', 'D'], aggregate_func=pd.Series.median) assert_raises(AssertionError, assert_equal, m1.aggregate_func, m2.aggregate_func) assert_frame_equal(m1.fit().anova_table, m2.fit().anova_table) def test_repeated_measures_aggregate_func_mean(): double_data = pd.concat([data, data], axis=0) m1 = AnovaRM(double_data, 'DV', 'id', within=['A', 'B', 'D'], aggregate_func=pd.Series.mean) m2 = AnovaRM(double_data, 'DV', 'id', within=['A', 'B', 'D'], aggregate_func='mean') assert_equal(m1.aggregate_func, m2.aggregate_func) def test_repeated_measures_aggregate_compare_with_ezANOVA(): # Results should reproduces those from R's `ezANOVA` (library ez). ez = pd.DataFrame( {'F Value': [8.7650709, 8.4985785, 20.5076546, 0.8457797, 21.7593382, 6.2416695, 5.4253359], 'Num DF': [1, 2, 1, 2, 1, 2, 2], 'Den DF': [7, 14, 7, 14, 7, 14, 14], 'Pr > F': [0.021087505, 0.003833921, 0.002704428, 0.450021759, 0.002301792, 0.011536846, 0.018010647]}, index=pd.Index(['A', 'B', 'D', 'A:B', 'A:D', 'B:D', 'A:B:D'])) ez = ez[['F Value', 'Num DF', 'Den DF', 'Pr > F']] double_data = pd.concat([data, data], axis=0) df = (AnovaRM(double_data, 'DV', 'id', within=['A', 'B', 'D'], aggregate_func=pd.Series.mean) .fit() .anova_table) assert_frame_equal(ez, df, check_dtype=False)