def test_approx(self):
ramsay = np.array((111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
101, 96, 97, 102, 107, 113, 116, 113, 110, 98))
parekh = np.array((107, 108, 106, 98, 105, 103, 110, 105, 104,
100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99))
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
message="Ties preclude use of exact statistic.")
W, pval = stats.ansari(ramsay, parekh)
assert_almost_equal(W,185.5,11)
assert_almost_equal(pval,0.18145819972867083,11)
def test_approx(self):
ramsay = np.array((111, 107, 100, 99, 102, 106, 109, 108, 104, 99, 101,
96, 97, 102, 107, 113, 116, 113, 110, 98))
parekh = np.array((107, 108, 106, 98, 105, 103, 110, 105, 104, 100, 96,
108, 103, 104, 114, 114, 113, 108, 106, 99))
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore', message="Ties preclude use of exact statistic.")
W, pval = stats.ansari(ramsay, parekh)
assert_almost_equal(W, 185.5, 11)
assert_almost_equal(pval, 0.18145819972867083, 11)
def test_exact(self):
W,pval = stats.ansari([1,2,3,4],[15,5,20,8,10,12])
assert_almost_equal(W,10.0,11)
assert_almost_equal(pval,0.533333333333333333,7)
def test_small(self):
x = [1,2,3,3,4]
y = [3,2,6,1,6,1,4,1]
W, pval = stats.ansari(x,y)
assert_almost_equal(W,23.5,11)
assert_almost_equal(pval,0.13499256881897437,11)
def test_exact(self):
W, pval = stats.ansari([1, 2, 3, 4], [15, 5, 20, 8, 10, 12])
assert_almost_equal(W, 10.0, 11)
assert_almost_equal(pval, 0.533333333333333333, 7)
def test_small(self):
x = [1, 2, 3, 3, 4]
y = [3, 2, 6, 1, 6, 1, 4, 1]
W, pval = stats.ansari(x, y)
assert_almost_equal(W, 23.5, 11)
assert_almost_equal(pval, 0.13499256881897437, 11)