def test05(self):
R = """Marginals([('factorials', OrderedDict([('AGE', [u'old', u'old', u'old', u'old', u'old']), ('CONDITION', [u'adjective', u'counting', u'imagery', u'intention', u'rhyming'])])), ('dmu', [11.0, 7.0, 13.4, 12.0, 6.9000000000000004]), ('dN', [10, 10, 10, 10, 10]), ('dsem', [0.78881063774661542, 0.57735026918962573, 1.4236104336041748, 1.1832159566199232, 0.67412494720522276]), ('dlower', [9.4539311500166345, 5.868393472388334, 10.609723550135818, 9.6808967250249509, 5.578715103477764]), ('dupper', [12.546068849983365, 8.131606527611666, 16.190276449864182, 14.319103274975049, 8.2212848965222367])], val='WORDS', factors=['AGE', 'CONDITION'], where='AGE == "old"')"""
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = df.marginals('WORDS',
factors=['AGE','CONDITION'],
where='AGE == "old"')
def test05(self):
R = """Marginals([('factorials', OrderedDict([('AGE', [u'old', u'old', u'old', u'old', u'old']), ('CONDITION', [u'adjective', u'counting', u'imagery', u'intention', u'rhyming'])])), ('dmu', [11.0, 7.0, 13.4, 12.0, 6.9000000000000004]), ('dN', [10, 10, 10, 10, 10]), ('dsem', [0.78881063774661542, 0.57735026918962573, 1.4236104336041748, 1.1832159566199232, 0.67412494720522276]), ('dlower', [9.4539311500166345, 5.868393472388334, 10.609723550135818, 9.6808967250249509, 5.578715103477764]), ('dupper', [12.546068849983365, 8.131606527611666, 16.190276449864182, 14.319103274975049, 8.2212848965222367])], val='WORDS', factors=['AGE', 'CONDITION'], where='AGE == "old"')"""
df = DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = df.marginals('WORDS',
factors=['AGE', 'CONDITION'],
where='AGE == "old"')
def test04(self):
R = """\
AGE CONDITION Mean Count Std. 95% CI 95% CI
Error lower upper
==========================================================
old adjective 11.000 10 0.789 9.454 12.546
old counting 7.000 10 0.577 5.868 8.132
old imagery 13.400 10 1.424 10.610 16.190
old intention 12.000 10 1.183 9.681 14.319
old rhyming 6.900 10 0.674 5.579 8.221 """
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = str(df.marginals('WORDS',
factors=['AGE','CONDITION'],
where='AGE == "old"'))
self.assertEqual(D, R)
def test04(self):
R = """\
AGE CONDITION Mean Count Std. 95% CI 95% CI
Error lower upper
==========================================================
old adjective 11.000 10 0.789 9.454 12.546
old counting 7.000 10 0.577 5.868 8.132
old imagery 13.400 10 1.424 10.610 16.190
old intention 12.000 10 1.183 9.681 14.319
old rhyming 6.900 10 0.674 5.579 8.221 """
df = DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = str(
df.marginals('WORDS',
factors=['AGE', 'CONDITION'],
where='AGE == "old"'))
self.assertEqual(D, R)
def test0(self):
df = DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
x = df.marginals('WORDS', factors=['AGE', 'CONDITION'])
for d, r in zip(x['dmu'],
[11, 7, 13.4, 12, 6.9, 14.8, 6.5, 17.6, 19.3, 7.6]):
self.failUnlessAlmostEqual(d, r)
for d, r in zip(x['dN'], [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]):
self.failUnlessAlmostEqual(d, r)
for d, r in zip(x['dsem'], [
0.788810638, 0.577350269, 1.423610434, 1.183215957,
0.674124947, 1.103529690, 0.453382350, 0.819213715,
0.843932593, 0.618241233
]):
self.failUnlessAlmostEqual(d, r)
def test02(self):
R = """\
AGE CONDITION Mean Count Std. 95% CI 95% CI
Error lower upper
============================================================
old adjective 11.000 10 0.789 9.454 12.546
old counting 7.000 10 0.577 5.868 8.132
old imagery 13.400 10 1.424 10.610 16.190
old intention 12.000 10 1.183 9.681 14.319
old rhyming 6.900 10 0.674 5.579 8.221
young adjective 14.800 10 1.104 12.637 16.963
young counting 6.500 10 0.453 5.611 7.389
young imagery 17.600 10 0.819 15.994 19.206
young intention 19.300 10 0.844 17.646 20.954
young rhyming 7.600 10 0.618 6.388 8.812 """
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = df.marginals('WORDS',factors=['AGE','CONDITION'])
self.assertEqual(str(D), R)
def test02(self):
R = """\
AGE CONDITION Mean Count Std. 95% CI 95% CI
Error lower upper
============================================================
old adjective 11.000 10 0.789 9.454 12.546
old counting 7.000 10 0.577 5.868 8.132
old imagery 13.400 10 1.424 10.610 16.190
old intention 12.000 10 1.183 9.681 14.319
old rhyming 6.900 10 0.674 5.579 8.221
young adjective 14.800 10 1.104 12.637 16.963
young counting 6.500 10 0.453 5.611 7.389
young imagery 17.600 10 0.819 15.994 19.206
young intention 19.300 10 0.844 17.646 20.954
young rhyming 7.600 10 0.618 6.388 8.812 """
df = DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = df.marginals('WORDS', factors=['AGE', 'CONDITION'])
self.assertEqual(str(D), R)
def test0(self):
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
x=df.marginals('WORDS',factors=['AGE','CONDITION'])
for d,r in zip(x['dmu'],[11,7,13.4,12,6.9,14.8,6.5,17.6,19.3,7.6]):
self.failUnlessAlmostEqual(d,r)
for d,r in zip(x['dN'],[10,10,10,10,10,10,10,10,10,10]):
self.failUnlessAlmostEqual(d,r)
for d,r in zip(x['dsem'],[0.788810638,
0.577350269,
1.423610434,
1.183215957,
0.674124947,
1.103529690,
0.453382350,
0.819213715,
0.843932593,
0.618241233]):
self.failUnlessAlmostEqual(d,r)
def test03(self):
R = """Marginals([('factorials', OrderedDict([('AGE', [u'old', u'old', u'old', u'old', u'old', u'young', u'young', u'young', u'young', u'young']), ('CONDITION', [u'adjective', u'counting', u'imagery', u'intention', u'rhyming', u'adjective', u'counting', u'imagery', u'intention', u'rhyming'])])), ('dmu', [11.0, 7.0, 13.4, 12.0, 6.9000000000000004, 14.800000000000001, 6.5, 17.600000000000001, 19.300000000000001, 7.5999999999999996]), ('dN', [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]), ('dsem', [0.78881063774661542, 0.57735026918962573, 1.4236104336041748, 1.1832159566199232, 0.67412494720522276, 1.1035296904831231, 0.4533823502911814, 0.81921371516296715, 0.84393259341147731, 0.61824123303304679]), ('dlower', [9.4539311500166345, 5.868393472388334, 10.609723550135818, 9.6808967250249509, 5.578715103477764, 12.637081806653079, 5.6113705934292843, 15.994341118280586, 17.645892116913505, 6.3882471832552277]), ('dupper', [12.546068849983365, 8.131606527611666, 16.190276449864182, 14.319103274975049, 8.2212848965222367, 16.962918193346923, 7.3886294065707157, 19.205658881719415, 20.954107883086497, 8.8117528167447716])], val='WORDS', factors=['AGE', 'CONDITION'])"""
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = repr(df.marginals('WORDS',factors=['AGE','CONDITION']))
def test03(self):
R = """Marginals([('factorials', OrderedDict([('AGE', [u'old', u'old', u'old', u'old', u'old', u'young', u'young', u'young', u'young', u'young']), ('CONDITION', [u'adjective', u'counting', u'imagery', u'intention', u'rhyming', u'adjective', u'counting', u'imagery', u'intention', u'rhyming'])])), ('dmu', [11.0, 7.0, 13.4, 12.0, 6.9000000000000004, 14.800000000000001, 6.5, 17.600000000000001, 19.300000000000001, 7.5999999999999996]), ('dN', [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]), ('dsem', [0.78881063774661542, 0.57735026918962573, 1.4236104336041748, 1.1832159566199232, 0.67412494720522276, 1.1035296904831231, 0.4533823502911814, 0.81921371516296715, 0.84393259341147731, 0.61824123303304679]), ('dlower', [9.4539311500166345, 5.868393472388334, 10.609723550135818, 9.6808967250249509, 5.578715103477764, 12.637081806653079, 5.6113705934292843, 15.994341118280586, 17.645892116913505, 6.3882471832552277]), ('dupper', [12.546068849983365, 8.131606527611666, 16.190276449864182, 14.319103274975049, 8.2212848965222367, 16.962918193346923, 7.3886294065707157, 19.205658881719415, 20.954107883086497, 8.8117528167447716])], val='WORDS', factors=['AGE', 'CONDITION'])"""
df = DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = repr(df.marginals('WORDS', factors=['AGE', 'CONDITION']))
