# modalities.fmri.spm.reml

## Module: `modalities.fmri.spm.reml`

## Functions

nipy.modalities.fmri.spm.reml.orth(X, tol=1e-07)

  Compute orthonormal basis for the column span of X.

  Rank is determined by zeroing all singular values, u, less than or equal to tol*u.max().

  INPUTS:

    X – n-by-p matrix

  OUTPUTS:
    B – n-by-rank(X) matrix with orthonormal columns spanning

      the column rank of X

nipy.modalities.fmri.spm.reml.reml(sigma, components, design=None, n=1, niter=128, penalty_cov=np.float64(1.2664165549094176e-14), penalty_mean=0)

  Adapted from spm_reml.m

  ReML estimation of covariance components from sigma using design matrix.

  INPUTS:

    sigma – m-by-m covariance matrix components – q-by-m-by-m array of variance components

    > mean of sigma is modeled as a some over components[i]

    design – m-by-p design matrix whose effect is to be removed for

      ReML. If None, no effect removed (???)

    n – degrees of freedom of sigma penalty_cov – quadratic penalty to be applied in Fisher algorithm.

    > If the value is a float, f, the penalty is f * identity(m). If the value is a 1d array, this is the diagonal of the penalty.

    penalty_mean – mean of quadratic penalty to be applied in Fisher

      algorithm. If the value is a float, f, the location is f * np.ones(m).

  OUTPUTS:

    C – estimated mean of sigma h – array of length q representing coefficients

    > of variance components

    cov_h – estimated covariance matrix of h