Inheritance diagram for nipy.neurospin.group.spatial_relaxation_onesample:
Multivariate modeling of fMRI group data accounting for spatial uncertainty In: data (n,p) estimated effects
vardata (n,p) variances of estimated effects XYZ (3,p) voxel coordinates std <float> Initial guess for standard deviate of spatial displacements sigma <float> regularity of displacement field labels (p,) labels defining regions of interest network (N,) binary region labels (1 for active, 0 for inactive) v_shape <float> intensity variance prior shape v_scale <float> intensity variance prior scale std_shape <float> spatial standard error prior shape std_scale <float> spatial standard error prior scale m_mean_rate <float> mean effect prior rate m_var_shape <float> effect variance prior shape m_var_scale <float> effect variance prior scale disp_mask (q,) mask of the brain, to limit displacements labels_prior (M,r) prior on voxelwise region membership labels_prior_values (M,r) voxelwise label values where prior is defined labels_prior_mask (r,) Mask of voxels where a label prior is defined
Sample posterior distribution of model parameters, or compute their MAP estimator In: nsimu <int> Number of samples drawn from posterior mean distribution
burnin <int> Number of discarded burn-in samples J (N,) voxel indices where successive mean values are stored verbose <bool> Print some infos during the sampling process proposal <str> ‘prior’, ‘rand_walk’ or ‘fixed’ proposal_mean <float> Used for fixed proposal only proposal_std <float> Used for random walk or fixed proposal mode <str> if mode=’saem’, compute MAP estimates of model parameters.
if mode=’mcmc’, sample their posterior distributionupdate_spatial <bool> when False, enables sampling conditional on spatial parameters