def __init__(self, Y, Cn, G, F=None, A=None, rank=1, Cr=None):
"""
Args:
Y: [N, P] phenotype matrix
F: Sample fixed effect design (first dimension must be N)
A: Trait fixed effect design (second dimension must be P)
Cn: Limix covariance matrix for Cn (dimension P)
G: [N, rank_r] numpy covariance matrix for G
rank: rank of column low-rank covariance (default = 1)
"""
assert_type(Y, NP.ndarray, 'Y')
assert_subtype(Cn, Covariance, 'Cn')
assert_type(G, NP.ndarray, 'G')
covar = Cov2KronSumLR(Cn=Cn, G=G, rank=rank, Cr=Cr)
mean = MeanKronSum(Y=Y, F=F, A=A)
assert mean.n_terms <= 1, ('GP2KronSum supports MeanKronSum'
' means with maximum 1 term!')
GP.__init__(self, covar=covar, mean=mean)
def __init__(self, Y, Cn, G, F=None, A=None, rank=1, Cr=None):
"""
Args:
Y: [N, P] phenotype matrix
F: Sample fixed effect design (first dimension must be N)
A: Trait fixed effect design (second dimension must be P)
Cn: Limix covariance matrix for Cn (dimension P)
G: [N, rank_r] numpy covariance matrix for G
rank: rank of column low-rank covariance (default = 1)
"""
assert_type(Y, NP.ndarray, 'Y')
assert_subtype(Cn, Covariance, 'Cn')
assert_type(G, NP.ndarray, 'G')
covar = Cov2KronSumLR(Cn=Cn, G=G, rank=rank, Cr=Cr)
mean = MeanKronSum(Y=Y, F=F, A=A)
assert mean.n_terms <= 1, ('GP2KronSum supports MeanKronSum'
' means with maximum 1 term!')
GP.__init__(self, covar=covar, mean=mean)
def __init__(self, Y, Cg, Cn, F=None, A=None, R=None, S_R=None, U_R=None):
"""
Args:
Y: [N, P] phenotype matrix
F: list of sample fixed effect designs.
Each term must have first dimension N
A: list of trait fixed effect design.
Each term must have second dimension P
Cg: Limix covariance matrix for Cg (dimension P)
Cn: Limix covariance matrix for Cn (dimension P)
R: [N, N] numpy semidemidefinite covariance matrix for R.
In alternative to R, S_R and U_R can be specified.
S_R: N vector of eigenvalues of R
U_R: [N, N] eigenvector matrix of R
"""
assert_type(Y, NP.ndarray, 'Y')
assert_subtype(Cg, Covariance, 'Cg')
assert_subtype(Cn, Covariance, 'Cn')
covar = Cov2KronSum(Cg=Cg, Cn=Cn, R=R, S_R=S_R, U_R=U_R)
mean = MeanKronSum(Y=Y, F=F, A=A)
GP.__init__(self, covar=covar, mean=mean)
def __init__(self, Y, Cg, Cn, F=None, A=None, R=None, S_R=None, U_R=None):
"""
Args:
Y: [N, P] phenotype matrix
F: list of sample fixed effect designs.
Each term must have first dimension N
A: list of trait fixed effect design.
Each term must have second dimension P
Cg: Limix covariance matrix for Cg (dimension P)
Cn: Limix covariance matrix for Cn (dimension P)
R: [N, N] numpy semidemidefinite covariance matrix for R.
In alternative to R, S_R and U_R can be specified.
S_R: N vector of eigenvalues of R
U_R: [N, N] eigenvector matrix of R
"""
assert_type(Y, NP.ndarray, 'Y')
assert_subtype(Cg, Covariance, 'Cg')
assert_subtype(Cn, Covariance, 'Cn')
covar = Cov2KronSum(Cg=Cg, Cn=Cn, R=R, S_R=S_R, U_R=U_R)
mean = MeanKronSum(Y=Y, F=F, A=A)
GP.__init__(self, covar=covar, mean=mean)
