Exemple #1
0
 def __init__(self,
              Y=None,
              Cg=None,
              Cn=None,
              R=None,
              S_R=None,
              U_R=None,
              G=None,
              rank=None):
     """
     Args:
         Y:      [N, P] phenotype matrix
         Cg:     Limix covariance matrix for Cg (dimension P)
         Cn:     Limix covariance matrix for Cn (dimension P)
         G:      [N, rank_r] numpy covariance matrix for G
         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
         rank:   rank of column low-rank covariance (default = 1)
     """
     covar = Cov3KronSumLR(Cg=Cg,
                           Cn=Cn,
                           R=R,
                           G=G,
                           rank=rank,
                           S_R=S_R,
                           U_R=U_R)
     mean = MeanKronSum(Y=Y)
     GP.__init__(self, covar=covar, mean=mean)
Exemple #2
0
 def __init__(self, Y=None, Cg=None, Cn=None, R=None, S_R=None, U_R=None, G=None, rank=None):
     """
     Args:
         Y:      [N, P] phenotype matrix
         Cg:     Limix covariance matrix for Cg (dimension P)
         Cn:     Limix covariance matrix for Cn (dimension P)
         G:      [N, rank_r] numpy covariance matrix for G
         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
         rank:   rank of column low-rank covariance (default = 1)
     """
     covar = Cov3KronSumLR(Cg=Cg, Cn=Cn, R=R, G=G, rank=rank, S_R=S_R, U_R=U_R)
     mean = MeanKronSum(Y=Y)
     GP.__init__(self, covar=covar, mean=mean)
Exemple #3
0
    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)