def contrast_from_cols_or_rows(L, D, pseudo=None):
""" Construct a contrast matrix from a design matrix D
(possibly with its pseudo inverse already computed)
and a matrix L that either specifies something in
the column space of D or the row space of D.
Parameters
----------
L : ndarray
Matrix used to try and construct a contrast.
D : ndarray
Design matrix used to create the contrast.
pseudo : None or array-like, optional
If not None, gives pseudo-inverse of `D`. Allows you to pass
this if it is already calculated.
Returns
-------
C : ndarray
Matrix with C.shape[1] == D.shape[1] representing an estimable
contrast.
Notes
-----
From an n x p design matrix D and a matrix L, 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.
L must satisfy either L.shape[0] == n or L.shape[1] == p.
If L.shape[0] == n, then L is thought of as representing
columns in the column space of D.
If L.shape[1] == p, then L is thought of as what is known
as a contrast matrix. In this case, this function returns an estimable
contrast corresponding to the dot(D, L.T)
This always produces a meaningful contrast, not always
with the intended properties because q is always non-zero unless
L is identically 0. That is, it produces a contrast that spans
the column space of L (after projection onto the column space of D).
"""
L = np.asarray(L)
D = np.asarray(D)
n, p = D.shape
if L.shape[0] != n and L.shape[1] != p:
raise ValueError('shape of L and D mismatched')
if pseudo is None:
pseudo = pinv(D)
if L.shape[0] == n:
C = np.dot(pseudo, L).T
else:
C = np.dot(pseudo, np.dot(D, L.T)).T
Lp = np.dot(D, C.T)
if len(Lp.shape) == 1:
Lp.shape = (n, 1)
Lp_rank = matrix_rank(Lp)
if Lp_rank != Lp.shape[1]:
Lp = full_rank(Lp, Lp_rank)
C = np.dot(pseudo, Lp).T
return np.squeeze(C)
def contrast_from_cols_or_rows(L, D, pseudo=None):
""" Construct a contrast matrix from a design matrix D
(possibly with its pseudo inverse already computed)
and a matrix L that either specifies something in
the column space of D or the row space of D.
Parameters
----------
L : ndarray
Matrix used to try and construct a contrast.
D : ndarray
Design matrix used to create the contrast.
pseudo : None or array-like, optional
If not None, gives pseudo-inverse of `D`. Allows you to pass
this if it is already calculated.
Returns
-------
C : ndarray
Matrix with C.shape[1] == D.shape[1] representing an estimable
contrast.
Notes
-----
From an n x p design matrix D and a matrix L, 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.
L must satisfy either L.shape[0] == n or L.shape[1] == p.
If L.shape[0] == n, then L is thought of as representing
columns in the column space of D.
If L.shape[1] == p, then L is thought of as what is known
as a contrast matrix. In this case, this function returns an estimable
contrast corresponding to the dot(D, L.T)
This always produces a meaningful contrast, not always
with the intended properties because q is always non-zero unless
L is identically 0. That is, it produces a contrast that spans
the column space of L (after projection onto the column space of D).
"""
L = np.asarray(L)
D = np.asarray(D)
n, p = D.shape
if L.shape[0] != n and L.shape[1] != p:
raise ValueError('shape of L and D mismatched')
if pseudo is None:
pseudo = pinv(D)
if L.shape[0] == n:
C = np.dot(pseudo, L).T
else:
C = np.dot(pseudo, np.dot(D, L.T)).T
Lp = np.dot(D, C.T)
if len(Lp.shape) == 1:
Lp.shape = (n, 1)
Lp_rank = matrix_rank(Lp)
if Lp_rank != Lp.shape[1]:
Lp = full_rank(Lp, Lp_rank)
C = np.dot(pseudo, Lp).T
return np.squeeze(C)