modalities.fmri.spm.reml¶
Module: modalities.fmri.spm.reml
¶
Functions¶

nipy.modalities.fmri.spm.reml.
orth
(X, tol=1e07)¶ 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 – nbyp matrix
 OUTPUTS:
 B – nbyrank(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=1.2664165549094176e14, penalty_mean=0)¶ Adapted from spm_reml.m
ReML estimation of covariance components from sigma using design matrix.
 INPUTS:
sigma – mbym covariance matrix components – qbymbym array of variance components
mean of sigma is modeled as a some over components[i] design – mbyp 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 componentscov_h – estimated covariance matrix of h