Inheritance diagram for nipy.modalities.fmri.fmristat.delay:
This module defines a class to output estimates of delays and contrasts of delays.
Liao, C.H., Worsley, K.J., Poline, J-B., Aston, J.A.D., Duncan, G.H., Evans, A.C. (2002). ‘Estimating the delay of the response in fMRI data.’ NeuroImage, 16:593-606.
Bases: nipy.fixes.scipy.stats.models.contrast.Contrast
Specify a delay contrast.
Delay contrasts are specified by a sequence of functions and weights, the functions should NOT already be convolved with any HRF. They will be convolved with self.IRF which is expected to be a filter with a canonical HRF and its derivative – defaults to the Glover model.
Weights should have the same number of columns as len(fns), with each row specifying a different contrast.
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Returns: | ContrastResults |
To estimate the delay, it is assumed that the response contains
(f ** HRF)(t + delta)
for each delay model time series ‘f’. More specifically, it is assumed that
f(t + delta) = c1 * (f ** HRF)(t) + delta * c2 * (f ** dHRF)(t)
where HRF and dHRF are the HRFs for this delay contrast.
This function checks to ensure that the columns
[(f ** HRF)(t), (f ** dHRF(t))]
are in the column space of the fMRI regression model.
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Returns: | None |
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if any of the columns are not in the column space of the model |
Bases: nipy.algorithms.statistics.regression.TOutput
TODO
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Bases: nipy.modalities.fmri.hrf.SpectralHRF
Delay filter with spectral or Taylor series decomposition for estimating delays.
Liao et al. (2002).
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Perform an expansion of fn, shifted over the values in delta. Effectively, a Taylor series approximation to fn(t+delta), in delta, with basis given by the filter elements. If fn is None, it assumes fn=IRF[0], that is the first filter.
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Returns: | None |