def test_lmfitter():
"Test the _nd_anova class"
ds = datasets.get_uts()
# independent, residuals vs. Hopkins
y = ds['uts'].x
x = ds.eval("A * B")
lm = glm._nd_anova(x)
f_maps = lm.map(y)
p_maps = lm.p_maps(f_maps)
x_full = ds.eval("A * B + ind(A%B)")
lm_full = glm._nd_anova(x_full)
assert isinstance(lm_full, glm._BalancedMixedNDANOVA)
f_maps_full = lm_full.map(y)
p_maps_full = lm_full.p_maps(f_maps)
for f, f_full in zip(f_maps, f_maps_full):
assert_allclose(f, f_full)
for p, p_full in zip(p_maps, p_maps_full):
assert_allclose(p, p_full)
# repeated measures
x = ds.eval("A * B * rm")
lm = glm._nd_anova(x)
f_maps = lm.map(y)
p_maps = lm.p_maps(f_maps)
aov = test.ANOVA(y[:, 0], x)
for f_test, f_map, p_map in zip(aov.f_tests, f_maps, p_maps):
assert f_map[0] == pytest.approx(f_test.F)
assert p_map[0] == pytest.approx(f_test.p)
def test_lmfitter():
"Test the _nd_anova class"
ds = datasets.get_rand()
# independent, residuals vs. Hopkins
y = ds['uts'].x
x = ds.eval("A * B")
lm = _nd_anova(x)
f_maps = lm.map(y)
p_maps = lm.p_maps(f_maps)
x_full = ds.eval("A * B + ind(A%B)")
lm_full = _nd_anova(x_full)
f_maps_full = lm_full.map(y)
p_maps_full = lm_full.p_maps(f_maps)
for f, f_full in izip(f_maps, f_maps_full):
assert_allclose(f, f_full)
for p, p_full in izip(p_maps, p_maps_full):
assert_allclose(p, p_full)
# repeated measures
x = ds.eval("A * B * rm")
lm = _nd_anova(x)
f_maps = lm.map(y)
p_maps = lm.p_maps(f_maps)
aov = test.anova(y[:, 0], x)
for f_test, f_map, p_map in izip(aov.f_tests, f_maps, p_maps):
assert_almost_equal(f_map[0], f_test.F)
assert_almost_equal(p_map[0], f_test.p)
def test_lmfitter():
"Test the _nd_anova class"
ds = datasets.get_rand()
# independent, residuals vs. Hopkins
y = ds['uts'].x
x = ds.eval("A * B")
lm = _nd_anova(x)
f_maps = lm.map(y)
p_maps = lm.p_maps(f_maps)
x_full = ds.eval("A * B + ind(A%B)")
lm_full = _nd_anova(x_full)
f_maps_full = lm_full.map(y)
p_maps_full = lm_full.p_maps(f_maps)
for f, f_full in izip(f_maps, f_maps_full):
assert_allclose(f, f_full)
for p, p_full in izip(p_maps, p_maps_full):
assert_allclose(p, p_full)
# repeated measures
x = ds.eval("A * B * rm")
lm = _nd_anova(x)
f_maps = lm.map(y)
p_maps = lm.p_maps(f_maps)
aov = test.anova(y[:, 0], x)
for f_test, f_map, p_map in izip(aov.f_tests, f_maps, p_maps):
assert_almost_equal(f_map[0], f_test.F)
assert_almost_equal(p_map[0], f_test.p)
def test_lmfitter():
"Test the _nd_anova class"
ds = datasets.get_uts()
# independent, residuals vs. Hopkins
y = ds['uts'].x
x = ds.eval("A * B")
lm = glm._nd_anova(x)
f_maps = lm.map(y)
p_maps = lm.p_maps(f_maps)
x_full = ds.eval("A * B + ind(A%B)")
lm_full = glm._nd_anova(x_full)
assert isinstance(lm_full, glm._BalancedMixedNDANOVA)
f_maps_full = lm_full.map(y)
p_maps_full = lm_full.p_maps(f_maps)
for f, f_full in zip(f_maps, f_maps_full):
assert_allclose(f, f_full)
for p, p_full in zip(p_maps, p_maps_full):
assert_allclose(p, p_full)
# repeated measures
x = ds.eval("A * B * rm")
lm = glm._nd_anova(x)
f_maps = lm.map(y)
p_maps = lm.p_maps(f_maps)
aov = test.ANOVA(y[:, 0], x)
for f_test, f_map, p_map in zip(aov.f_tests, f_maps, p_maps):
assert f_map[0] == pytest.approx(f_test.F)
assert p_map[0] == pytest.approx(f_test.p)
def run_on_lm_fitter(y, x, ds):
y = ds.eval(y)
y = y.x[:, newaxis]
y = np.hstack((y, y))
x = ds.eval(x)
fitter = glm._nd_anova(x)
fmaps = fitter.map(y)
fs = fmaps[:, 0]
return fs
def run_on_lm_fitter(y, x, ds):
y = ds.eval(y)
y = y.x[:, newaxis]
y = np.hstack((y, y))
x = ds.eval(x)
fitter = _nd_anova(x)
fmaps = fitter.map(y)
fs = fmaps[:, 0]
return fs
