def test_fullrank(self):
X = R.standard_normal((40,10))
X[:,0] = X[:,1] + X[:,2]
Y = utils.fullrank(X)
self.assertEquals(Y.shape, (40,9))
self.assertEquals(utils.rank(Y), 9)
X[:,5] = X[:,3] + X[:,4]
Y = utils.fullrank(X)
self.assertEquals(Y.shape, (40,8))
self.assertEquals(utils.rank(Y), 8)
def isestimable(C, D):
"""
From an q x p contrast matrix C and an n x p design matrix D, checks
if the contrast C is estimable by looking at the rank of vstack([C,D]) and
verifying it is the same as the rank of D.
"""
if C.ndim == 1:
C.shape = (C.shape[0], 1)
new = N.vstack([C, D])
if utils.rank(new) != utils.rank(D):
return False
return True
def initialize(self, design):
"""
Set design for model, prewhitening design matrix and precomputing
covariance of coefficients (up to scale factor in front).
:Parameters:
design : TODO
TODO
"""
self.design = design
self.wdesign = self.whiten(design)
self.calc_beta = L.pinv(self.wdesign)
self.normalized_cov_beta = N.dot(self.calc_beta,
N.transpose(self.calc_beta))
self.df_resid = self.wdesign.shape[0] - utils.rank(self.design)
def contrastfromcols(T, D, pseudo=None):
"""
From an n x p design matrix D and a matrix T, tries
to determine a p x q contrast matrix C which
determines a contrast of full rank, i.e. the
n x q matrix
dot(transpose(C), pinv(D))
is full rank.
T must satisfy either T.shape[0] == n or T.shape[1] == p.
Note that this always produces a meaningful contrast, not always
with the intended properties because q is always non-zero unless
T is identically 0. That is, it produces a contrast that spans
the column space of T (after projection onto the column space of D).
"""
n, p = D.shape
if T.shape[0] != n and T.shape[1] != p:
raise ValueError, "shape of T and D mismatched"
if pseudo is None:
pseudo = pinv(D)
if T.shape[0] == n:
C = N.transpose(N.dot(pseudo, T))
else:
C = T
Tp = N.dot(D, N.transpose(C))
if utils.rank(Tp) != Tp.shape[1]:
Tp = utils.fullrank(Tp)
C = N.transpose(N.dot(pseudo, Tp))
return N.squeeze(C)
def test_rank(self):
X = R.standard_normal((40,10))
self.assertEquals(utils.rank(X), 10)
X[:,0] = X[:,1] + X[:,2]
self.assertEquals(utils.rank(X), 9)
