def test_anova_r_adler():
"""Test ANOVA accuracy by comparing with R (Adler dataset of car package)
An unbalanced 3 by 2 independent measures design.
"""
from rpy2.robjects import r
# "Adler" dataset
r_require('car')
ds = Dataset.from_r('Adler')
# with balanced data
dsb = ds.equalize_counts('expectation % instruction')
dsb.to_r('AdlerB')
aov = test.ANOVA('rating', 'instruction * expectation', ds=dsb)
fs = run_on_lm_fitter('rating', 'instruction * expectation', dsb)
fnds = run_as_ndanova('rating', 'instruction * expectation', dsb)
print(r('a.aov <- aov(rating ~ instruction * expectation, AdlerB)'))
print(r('a.summary <- summary(a.aov)'))
r_res = r['a.summary'][0]
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds)
# with unbalanced data; for Type II SS use car package
aov = test.ANOVA('rating', 'instruction * expectation', ds=ds)
fs = run_on_lm_fitter('rating', 'instruction * expectation', ds)
fnds = run_as_ndanova('rating', 'instruction * expectation', ds)
r_res = r("Anova(lm(rating ~ instruction * expectation, Adler, type=2))")
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'Anova')
# single predictor
aov = test.ANOVA('rating', 'instruction', ds=ds)
fs = run_on_lm_fitter('rating', 'instruction', ds)
fnds = run_as_ndanova('rating', 'instruction', ds)
r_res = r("Anova(lm(rating ~ instruction, Adler, type=2))")
assert_f_test_equal(aov.f_tests[0], r_res, 0, fs[0], fnds[0], 'Anova')
def test_anova_r_sleep():
"Test ANOVA accuracy by comparing with R (sleep dataset)"
from rpy2.robjects import r
# "sleep" dataset
print r('data(sleep)')
ds = Dataset.from_r('sleep')
ds['ID'].random = True
# independent measures
aov = test.ANOVA('extra', 'group', ds=ds)
fs = run_on_lm_fitter('extra', 'group', ds)
print r('sleep.aov <- aov(extra ~ group, sleep)')
print r('sleep.summary <- summary(sleep.aov)')
r_res = r['sleep.summary'][0]
assert_f_test_equal(aov.f_tests[0], r_res, 0, fs[0])
# repeated measures
aov = test.ANOVA('extra', 'group * ID', ds=ds)
fs = run_on_lm_fitter('extra', 'group * ID', ds)
print r('sleep.aov <- aov(extra ~ group + Error(ID / group), sleep)')
print r('sleep.summary <- summary(sleep.aov)')
r_res = r['sleep.summary'][1][0]
assert_f_test_equal(aov.f_tests[0], r_res, 0, fs[0])
# unbalanced (independent measures)
ds2 = ds[1:]
print r('sleep2 <- subset(sleep, (group == 2) | (ID != 1))')
aov = test.ANOVA('extra', 'group', ds=ds2)
fs = run_on_lm_fitter('extra', 'group', ds2)
print r('sleep2.aov <- aov(extra ~ group, sleep2)')
print r('sleep2.summary <- summary(sleep2.aov)')
r_res = r['sleep2.summary'][0]
assert_f_test_equal(aov.f_tests[0], r_res, 0, fs[0])
def test_anova():
"Test ANOVA"
from rpy2.robjects import r
r_require('car')
r_require('ez')
ds = datasets.get_uv(nrm=True)
ds.to_r('ds')
# fixed effects
aov = test.ANOVA('fltvar', 'A*B', ds=ds)
print(aov)
fs = run_on_lm_fitter('fltvar', 'A*B', ds)
fnds = run_as_ndanova('fltvar', 'A*B', ds)
r_res = r("Anova(lm(fltvar ~ A * B, ds, type=2))")
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'Anova')
# random effect
aov = test.ANOVA('fltvar', 'A*B*rm', ds=ds)
print(aov)
fs = run_on_lm_fitter('fltvar', 'A*B*rm', ds)
fnds = run_as_ndanova('fltvar', 'A*B*rm', ds)
r('test.aov <- aov(fltvar ~ A * B + Error(rm / (A * B)), ds)')
print(r('test.summary <- summary(test.aov)'))
r_res = r['test.summary'][1:]
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'rmaov')
# nested random effect
aov = test.ANOVA('fltvar', 'B + A%B + A * nrm(B)', ds=ds)
print(aov)
fs = run_on_lm_fitter('fltvar', 'B + A%B + A * nrm(B)', ds)
fnds = run_as_ndanova('fltvar', 'B + A%B + A * nrm(B)', ds)
r_res = r('ezANOVA(ds, fltvar, nrm, A, between=B)')
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'ez')
# not fully specified model with random effects
assert_raises(NotImplementedError, test.anova, 'fltvar', 'A*rm', ds=ds)
# empty cells
dss = ds.sub("A%B != ('a2', 'b2')")
assert_raises(NotImplementedError, test.anova, 'fltvar', 'A*B', ds=dss)
assert_raises(NotImplementedError,
run_on_lm_fitter,
'fltvar',
'A*B',
ds=dss)
dss = ds.sub("A%B != ('a1', 'b1')")
assert_raises(NotImplementedError, test.anova, 'fltvar', 'A*B', ds=dss)
assert_raises(NotImplementedError,
run_on_lm_fitter,
'fltvar',
'A*B',
ds=dss)
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 loc_ress(self):
# McCarthy & Wood tests
loc_ress = {}
for t1, t2 in combinations(self.terms.values(), 2):
for i, hemi in enumerate(['lh', 'rh']):
y1 = self.trf_dss[t1][self.metric].sub(source=self.masks[i])
y2 = self.trf_dss[t2][self.metric].sub(source=self.masks[i])
dy = normalize_in_cells(y1, 'source') - normalize_in_cells(y2, 'source')
ds_dy = table.melt_ndvar(dy, 'source', ds=self.trf_dss[t1])
loc_ress[t1, t2, hemi] = loc_ress[t2, t1, hemi] = test.ANOVA(self.metric, 'source * subject', ds=ds_dy)
return loc_ress
def test_anova():
"Test ANOVA"
r_require('car')
ds = datasets.get_uv(nrm=True)
ds.to_r('ds')
# fixed effects
aov = test.ANOVA('fltvar', 'A*B', ds=ds)
assert f'\n{aov}\n' == """
SS df MS F p
---------------------------------------------------
A 28.69 1 28.69 25.69*** < .001
B 0.04 1 0.04 0.03 .855
A x B 1.16 1 1.16 1.04 .310
Residuals 84.85 76 1.12
---------------------------------------------------
Total 114.74 79
"""
fs = run_on_lm_fitter('fltvar', 'A*B', ds)
fnds = run_as_ndanova('fltvar', 'A*B', ds)
r_res = r("Anova(lm(fltvar ~ A * B, ds, type=2))")
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'Anova')
# random effect
aov = test.ANOVA('fltvar', 'A*B*rm', ds=ds)
assert f'\n{aov}\n' == """
SS df MS MS(denom) df(denom) F p
-----------------------------------------------------------------------
A 28.69 1 28.69 1.21 19 23.67*** < .001
B 0.04 1 0.04 1.15 19 0.03 .859
A x B 1.16 1 1.16 1.01 19 1.15 .297
-----------------------------------------------------------------------
Total 114.74 79
"""
fs = run_on_lm_fitter('fltvar', 'A*B*rm', ds)
fnds = run_as_ndanova('fltvar', 'A*B*rm', ds)
r('test.aov <- aov(fltvar ~ A * B + Error(rm / (A * B)), ds)')
print(r('test.summary <- summary(test.aov)'))
r_res = r['test.summary'][1:]
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'rmaov')
def test_anova_eq():
"Test ANOVA against r-ez"
r_require('ez')
ds = datasets.get_uv(nrm=True)
ds.to_r('ds')
# nested random effect
aov_explicit = test.ANOVA('fltvar', 'A + B + A%B + nrm(B) + A%nrm(B)', ds=ds)
aov = test.ANOVA('fltvar', 'A * B * nrm(B)', ds=ds)
assert str(aov_explicit) == str(aov)
print(aov)
fs = run_on_lm_fitter('fltvar', 'A * B * nrm(B)', ds)
fnds = run_as_ndanova('fltvar', 'A * B * nrm(B)', ds)
r_res = r('ezANOVA(ds, fltvar, nrm, A, between=B)')
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'ez')
# sub parameter
r1 = test.ANOVA('fltvar', 'B * rm', ds=ds.sub('A == "a1"'))
r2 = test.ANOVA('fltvar', 'B * rm', sub='A == "a1"', ds=ds)
assert str(r2) == str(r1)
# not fully specified model with random effects
with pytest.raises(IncompleteModel):
test.anova('fltvar', 'A*rm', ds=ds)
# unequal group size, 1-way
sds = ds.sub("A == 'a1'").sub("nrm.isnotin(('s037', 's038', 's039'))")
sds.to_r('sds')
aov = test.ANOVA('fltvar', 'B * nrm(B)', ds=sds)
print(aov)
fs = run_on_lm_fitter('fltvar', 'B * nrm(B)', sds)
fnds = run_as_ndanova('fltvar', 'B * nrm(B)', sds)
with r_warning_filter: # type argument to ezANOVA
r_res = r('ezANOVA(sds, fltvar, nrm, between=B)')
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'ez')
# unequal group size, 2-way
sds = ds.sub("nrm.isnotin(('s037', 's038', 's039'))")
sds.to_r('sds')
aov = test.ANOVA('fltvar', 'A * B * nrm(B)', ds=sds)
print(aov)
fs = run_on_lm_fitter('fltvar', 'A * B * nrm(B)', sds)
fnds = run_as_ndanova('fltvar', 'A * B * nrm(B)', sds)
with r_warning_filter: # type argument to ezANOVA
r_res = r('ezANOVA(sds, fltvar, nrm, A, between=B)')
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'ez')
# empty cells
dss = ds.sub("A%B != ('a2', 'b2')")
with pytest.raises(NotImplementedError):
test.anova('fltvar', 'A*B', ds=dss)
with pytest.raises(NotImplementedError):
run_on_lm_fitter('fltvar', 'A*B', ds=dss)
dss = ds.sub("A%B != ('a1', 'b1')")
with pytest.raises(NotImplementedError):
test.anova('fltvar', 'A*B', ds=dss)
with pytest.raises(NotImplementedError):
run_on_lm_fitter('fltvar', 'A*B', ds=dss)
def test_anova():
"Test ANOVA"
r_require('car')
ds = datasets.get_uv(nrm=True)
ds.to_r('ds')
# fixed effects
aov = test.ANOVA('fltvar', 'A*B', ds=ds)
print(aov)
fs = run_on_lm_fitter('fltvar', 'A*B', ds)
fnds = run_as_ndanova('fltvar', 'A*B', ds)
r_res = r("Anova(lm(fltvar ~ A * B, ds, type=2))")
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'Anova')
# random effect
aov = test.ANOVA('fltvar', 'A*B*rm', ds=ds)
print(aov)
fs = run_on_lm_fitter('fltvar', 'A*B*rm', ds)
fnds = run_as_ndanova('fltvar', 'A*B*rm', ds)
r('test.aov <- aov(fltvar ~ A * B + Error(rm / (A * B)), ds)')
print(r('test.summary <- summary(test.aov)'))
r_res = r['test.summary'][1:]
assert_f_tests_equal(aov.f_tests, r_res, fs, fnds, 'rmaov')