def test_termcolumns(self):
t1 = formula.Term("A")
t2 = formula.Term("B")
f = t1 + t2 + t1 * t2
def other(val):
return np.array([3.2 * val, 4.342 * val**2, 5.234 * val**3])
q = formula.Quantitative(['other%d' % i for i in range(1, 4)],
termname='other',
func=t1,
transform=other)
f += q
q.namespace = f.namespace = self.formula.namespace
assert_almost_equal(q(), f()[f.termcolumns(q)])
if __name__ == '__main__':
import numpy.random as R
n = 100
X = np.array([0]*n + [1]*n)
b = 0.4
lin = 1 + b*X
Y = R.standard_exponential((2*n,)) / lin
delta = R.binomial(1, 0.9, size=(2*n,))
subjects = [Observation(Y[i], delta[i]) for i in range(2*n)]
for i in range(2*n):
subjects[i].X = X[i]
import statsmodels.sandbox.formula as F
x = F.Quantitative('X')
f = F.Formula(x)
c = CoxPH(subjects, f)
# c.cache()
# temp file cleanup doesn't work on windows
c = CoxPH(subjects, f, time_dependent=True)
c.cache() #this creates tempfile cache,
# no tempfile cache is created in normal use of CoxPH
#res = c.newton([0.4]) #doesn't work anymore
res=c.fit([0.4],method="bfgs")
print res.params
print dir(c)
def test_quantitative(self):
t = self.terms[2]
sint = formula.Quantitative('t', func=t, transform=np.sin)
t.namespace = sint.namespace = self.formula.namespace
assert_almost_equal(np.sin(t()), sint())
