interfaces.nipy.model

EstimateContrast

Link to code

Estimate contrast of a fitted model.

Inputs:

[Mandatory]
nvbeta: (any value)
s2: (an existing file name)
        squared variance of the residuals
dof: (any value)
        degrees of freedom
beta: (an existing file name)
        beta coefficients of the fitted model
reg_names: (a list of items which are any value)
contrasts: (a list of items which are a tuple of the form: (a unicode
          string, u'T', a list of items which are a unicode string, a list
          of items which are a float) or a tuple of the form: (a unicode
          string, u'T', a list of items which are a unicode string, a list
          of items which are a float, a list of items which are a float) or
          a tuple of the form: (a unicode string, u'F', a list of items
          which are a tuple of the form: (a unicode string, u'T', a list of
          items which are a unicode string, a list of items which are a
          float) or a tuple of the form: (a unicode string, u'T', a list of
          items which are a unicode string, a list of items which are a
          float, a list of items which are a float)))
        List of contrasts with each contrast being a list of the form:
         [('name', 'stat', [condition list], [weight list], [session
        list])]. if
         session list is None or not provided, all sessions are used. For F
         contrasts, the condition list should contain previously defined
         T-contrasts.
constants: (any value)
axis: (any value)

[Optional]
mask: (a file name)

Outputs:

p_maps: (a list of items which are an existing file name)
stat_maps: (a list of items which are an existing file name)
z_maps: (a list of items which are an existing file name)

FitGLM

Link to code

Fit GLM model based on the specified design. Supports only single or concatenated runs.

Inputs:

[Mandatory]
TR: (a float)
session_info: (a list of from 1 to 1 items which are any value)
        Session specific information generated by ``modelgen.SpecifyModel``,
        FitGLM does not support multiple runs uless they are concatenated
        (see SpecifyModel options)

[Optional]
drift_model: (u'Cosine' or u'Polynomial' or u'Blank', nipype default
          value: Cosine)
        string that specifies the desired drift model, to be chosen among
        'Polynomial', 'Cosine', 'Blank'
normalize_design_matrix: (a boolean, nipype default value: False)
        normalize (zscore) the regressors before fitting
mask: (a file name)
        restrict the fitting only to the region defined by this mask
save_residuals: (a boolean, nipype default value: False)
plot_design_matrix: (a boolean, nipype default value: False)
hrf_model: (u'Canonical' or u'Canonical With Derivative' or u'FIR',
          nipype default value: Canonical)
        that specifies the hemodynamic reponse function it can be
        'Canonical', 'Canonical With Derivative' or 'FIR'
model: (u'ar1' or u'spherical', nipype default value: ar1)
        autoregressive mode is available only for the kalman method
method: (u'kalman' or u'ols', nipype default value: kalman)
        method to fit the model, ols or kalma; kalman is more time consuming
        but it supports autoregressive model

Outputs:

a: (an existing file name)
nvbeta: (any value)
s2: (an existing file name)
dof: (any value)
beta: (an existing file name)
residuals: (a file name)
reg_names: (a list of items which are any value)
constants: (any value)
axis: (any value)