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_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)
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)
# 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)
r_res = r("Anova(lm(rating ~ instruction * expectation, Adler, type=2))")
assert_f_tests_equal(aov.f_tests, r_res, fs, 'Anova')
# single predictor
aov = test.anova('rating', 'instruction', ds=ds)
fs = run_on_lm_fitter('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], 'Anova')
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_r():
"Test interaction with R through rpy2"
from rpy2.robjects import r
r("data(sleep)")
ds = Dataset.from_r("sleep")
eq_(ds.name, 'sleep')
extra = (0.7, -1.6, -0.2, -1.2, -0.1, 3.4, 3.7, 0.8, 0.0, 2.0, 1.9, 0.8,
1.1, 0.1, -0.1, 4.4, 5.5, 1.6, 4.6, 3.4)
assert_array_equal(ds.eval('extra'), extra)
assert_array_equal(ds.eval('ID'), list(map(str, range(1, 11))) * 2)
assert_array_equal(ds.eval('group'), ['1'] * 10 + ['2'] * 10)
# test putting
ds.to_r('sleep_copy')
ds_copy = Dataset.from_r('sleep_copy')
assert_dataset_equal(ds_copy, ds)
def test_r():
"Test interaction with R thorugh rpy2"
from rpy2.robjects import r
r("data(sleep)")
ds = Dataset.from_r("sleep")
assert_equal(ds.name, 'sleep')
extra = (0.7, -1.6, -0.2, -1.2, -0.1, 3.4, 3.7, 0.8, 0.0, 2.0, 1.9, 0.8,
1.1, 0.1, -0.1, 4.4, 5.5, 1.6, 4.6, 3.4)
assert_array_equal(ds.eval('extra'), extra)
assert_array_equal(ds.eval('ID'), map(str, xrange(1, 11)) * 2)
assert_array_equal(ds.eval('group'), ['1'] * 10 + ['2'] * 10)
# test putting
ds.to_r('sleep_copy')
ds_copy = Dataset.from_r('sleep_copy')
assert_dataset_equal(ds_copy, ds)
