interfaces.nipy.model

EstimateContrast

Link to code

Estimate contrast of a fitted model.

Inputs:

[Mandatory]
contrasts: (a list of items which are a tuple of the form: (a unicode
          string, '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, '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, 'F', a list of items which
          are a tuple of the form: (a unicode string, '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, '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.
beta: (an existing file name)
        beta coefficients of the fitted model
nvbeta: (any value)
s2: (an existing file name)
        squared variance of the residuals
dof: (any value)
        degrees of freedom
constants: (any value)
axis: (any value)
reg_names: (a list of items which are any value)

[Optional]
mask: (a file name)

Outputs:

stat_maps: (a list of items which are an existing file name)
z_maps: (a list of items which are an existing file name)
p_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]
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)
TR: (a float)

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

Outputs:

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