interfaces.nipy.model¶
EstimateContrast¶
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: (a pathlike object or string representing an existing file)
beta coefficients of the fitted model
nvbeta: (any value)
s2: (a pathlike object or string representing an existing file)
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 pathlike object or string representing an existing file)
Outputs:
stat_maps: (a list of items which are a pathlike object or string
representing an existing file)
z_maps: (a list of items which are a pathlike object or string
representing an existing file)
p_maps: (a list of items which are a pathlike object or string
representing an existing file)
FitGLM¶
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 pathlike object or string representing an existing file)
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: (a pathlike object or string representing an existing file)
nvbeta: (any value)
s2: (a pathlike object or string representing an existing file)
dof: (any value)
constants: (any value)
axis: (any value)
reg_names: (a list of items which are any value)
residuals: (a pathlike object or string representing a file)
a: (a pathlike object or string representing an existing file)