def test_posthoc_conover_friedman(self):
results = sp.posthoc_conover_friedman(self.df_bn)
p_results = np.array([[
-1.000000, 0.935333, 0.268619, 0.339721, 0.339721, 0.060540,
0.628079
],
[
0.935333, -1.000000, 0.302605, 0.380025,
0.380025, 0.070050, 0.685981
],
[
0.268619, 0.302605, -1.000000, 0.871144,
0.871144, 0.380025, 0.519961
],
[
0.339721, 0.380025, 0.871144, -1.000000,
1.000000, 0.302605, 0.628079
],
[
0.339721, 0.380025, 0.871144, 1.000000,
-1.000000, 0.302605, 0.628079
],
[
0.060540, 0.070050, 0.380025, 0.302605,
0.302605, -1.000000, 0.141412
],
[
0.628079, 0.685981, 0.519961, 0.628079,
0.628079, 0.141412, -1.000000
]])
self.assertTrue(np.allclose(results, p_results))
def test_posthoc_conover_friedman_tukey(self):
results = sp.posthoc_conover_friedman(self.df_bn,
p_adjust='single-step')
p_results = np.array(
[[1.0000000, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
[1.0000000, 1.000000, np.nan, np.nan, np.nan, np.nan, np.nan],
[0.8908193, 0.9212619, 1.000000, np.nan, np.nan, np.nan, np.nan],
[
0.9455949, 0.9642182, 0.9999978, 1.000000, np.nan, np.nan,
np.nan
],
[
0.9455949, 0.9642182, 0.9999978, 1.0000000, 1.000000, np.nan,
np.nan
],
[
0.3177477, 0.3686514, 0.9642182, 0.9212619, 0.9212619,
1.000000, np.nan
],
[
0.9986057, 0.9995081, 0.9931275, 0.9986057, 0.9986057,
0.6553018, 1.000000
]])
p_results[p_results > 0.9] = 0.9
tri_upper = np.triu_indices(p_results.shape[0], 1)
p_results[tri_upper] = np.transpose(p_results)[tri_upper]
np.fill_diagonal(p_results, 1)
self.assertTrue(np.allclose(results, p_results, atol=3.e-2))
def test_posthoc_conover_friedman_non_melted(self):
df = DataFrame(self.df_bn)
results = sp.posthoc_conover_friedman(df, melted=False)
p_results = np.array([[
1.000000, 0.9325836, 0.2497915, 0.3203414, 0.3203414, 0.0517417,
0.6136576
],
[
0.9325836, 1.000000, 0.28339156, 0.36072942,
0.36072942, 0.06033626, 0.67344846
],
[
0.2497915, 0.28339156, 1.000000, 0.8657092,
0.8657092, 0.3607294, 0.5026565
],
[
0.3203414, 0.36072942, 0.8657092, 1.000000,
1.000000, 0.2833916, 0.6136576
],
[
0.3203414, 0.36072942, 0.8657092, 1.000000,
1.000000, 0.2833916, 0.6136576
],
[
0.0517417, 0.06033626, 0.3607294, 0.2833916,
0.2833916, 1.000000, 0.1266542
],
[
0.6136576, 0.67344846, 0.5026565, 0.6136576,
0.6136576, 0.1266542, 1.000000
]])
self.assertTrue(np.allclose(results, p_results))
def test_posthoc_conover_friedman(self):
results = sp.posthoc_conover_friedman(self.df_bn)
p_results = np.array([[
-1.000000, 0.9325836, 0.2497915, 0.3203414, 0.3203414, 0.0517417,
0.6136576
],
[
0.9325836, -1.000000, 0.28339156, 0.36072942,
0.36072942, 0.06033626, 0.67344846
],
[
0.2497915, 0.28339156, -1.000000, 0.8657092,
0.8657092, 0.3607294, 0.5026565
],
[
0.3203414, 0.36072942, 0.8657092, -1.000000,
1.000000, 0.2833916, 0.6136576
],
[
0.3203414, 0.36072942, 0.8657092, 1.000000,
-1.000000, 0.2833916, 0.6136576
],
[
0.0517417, 0.06033626, 0.3607294, 0.2833916,
0.2833916, -1.000000, 0.1266542
],
[
0.6136576, 0.67344846, 0.5026565, 0.6136576,
0.6136576, 0.1266542, -1.000000
]])
self.assertTrue(np.allclose(results, p_results))