modalities.fmri.spm.model

Module: modalities.fmri.spm.model

Inheritance diagram for nipy.modalities.fmri.spm.model:

Class

SecondStage

class nipy.modalities.fmri.spm.model.SecondStage(fmri_image, formula, sigma, outputs=[], volume_start_times=None)

Bases: object

Parameters:

fmri_image : FmriImageList

object returning 4D array from np.asarray, having attribute volume_start_times (if volume_start_times is None), and such that object[0] returns something with attributes shape

formula : nipy.algorithms.statistics.formula.Formula

sigma :

outputs :

volume_start_times :

__init__(fmri_image, formula, sigma, outputs=[], volume_start_times=None)

Initialize self. See help(type(self)) for accurate signature.

execute()

Functions

nipy.modalities.fmri.spm.model.Fmask(Fimg, dfnum, dfdenom, pvalue=0.0001)

Create mask for use in estimating pooled covariance based on an F contrast.

nipy.modalities.fmri.spm.model.estimate_pooled_covariance(resid, ARtarget=[0.3], mask=None)

Use SPM’s REML implementation to estimate a pooled covariance matrix.

Thresholds an F statistic at a marginal pvalue to estimate covariance matrix.