def test_contrast2(self):
dummy = formula.Term('zero')
self.namespace['zero'] = np.zeros((40, ), np.float64)
term = dummy + self.terms[2]
c = contrast.Contrast(term, self.formula)
test = [0] * 2 + [1] + [0] * 7
assert_almost_equal(c.matrix, test)
def test_contrast1(self):
term = self.terms[0] + self.terms[2]
c = contrast.Contrast(term, self.formula)
col1 = self.formula.termcolumns(self.terms[0], dict=False)
col2 = self.formula.termcolumns(self.terms[1], dict=False)
test = [[1] + [0] * 9, [0] * 2 + [1] + [0] * 7]
assert_almost_equal(c.matrix, test)
def test_contrast4(self):
f = self.formula + self.terms[5] + self.terms[5]
f.namespace = self.namespace
estimable = False
c = contrast.Contrast(self.terms[5], f)
self.assertEquals(estimable, False)
def test_contrast3(self):
X = self.formula.design()
P = np.dot(X, L.pinv(X))
dummy = formula.Term('noise')
resid = np.identity(40) - P
self.namespace['noise'] = np.transpose(
np.dot(resid, R.standard_normal((40, 5))))
terms = dummy + self.terms[2]
terms.namespace = self.formula.namespace
c = contrast.Contrast(terms, self.formula)
self.assertEquals(c.matrix.shape, (10, ))
'''
print(form.hasterm('C'))
print(form.termcolumns(formula.Term('C'))) #doesn't work with string argument
#Example: use two columns and get contrast
f2 = (form['A'] + form['B'])
print(f2)
print(repr(f2))
list(iterkeys(f2.namespace)) #namespace is still empty
f2.namespace = namespace #associate data
iterkeys(f2.namespace)
f2.design().shape
contrast.Contrast(formula.Term('A'), f2).matrix
'''
>>> f2 = (form['A']+form['B'])
>>> print f2
<formula: A + B>
>>> print repr(f2)
<statsmodels.sandbox.formula.Formula object at 0x036BAE70>
>>> f2.namespace.keys() #namespace is still empty
[]
>>> f2.namespace = namespace #associate data
>>> f2.namespace.keys()
['A', 'C', 'B', 'E', 'D', 'G', 'F', 'I', 'H', 'J']
>>> f2.design().shape
(40, 2)
>>> contrast.Contrast(formula.Term('A'), f2).matrix
array([ 1., 0.])
