nipype.interfaces.nipy.model module¶
EstimateContrast¶
Bases: NipyBaseInterface
Estimate contrast of a fitted model.
- Mandatory Inputs
axis (any value)
beta (a pathlike object or string representing an existing file) – Beta coefficients of the fitted model.
constants (any value)
contrasts (a list of items which are a tuple of the form: (a string, ‘T’, a list of items which are a string, a list of items which are a float) or a tuple of the form: (a string, ‘T’, a list of items which are a string, a list of items which are a float, a list of items which are a float) or a tuple of the form: (a string, ‘F’, a list of items which are a tuple of the form: (a string, ‘T’, a list of items which are a string, a list of items which are a float) or a tuple of the form: (a string, ‘T’, a list of items which are a 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.
dof (any value) – Degrees of freedom.
nvbeta (any value)
reg_names (a list of items which are any value)
s2 (a pathlike object or string representing an existing file) – Squared variance of the residuals.
- Optional Inputs
mask (a pathlike object or string representing an existing file)
- Outputs
p_maps (a list of items which are a pathlike object or string representing an existing file)
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)
FitGLM¶
Bases: NipyBaseInterface
Fit GLM model based on the specified design. Supports only single or concatenated runs.
- Mandatory Inputs
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 Inputs
drift_model (‘Cosine’ or ‘Polynomial’ or ‘Blank’) – String that specifies the desired drift model, to be chosen among ‘Polynomial’, ‘Cosine’, ‘Blank’. (Nipype default value:
Cosine
)hrf_model (‘Canonical’ or ‘Canonical With Derivative’ or ‘FIR’) – That specifies the hemodynamic reponse function it can be ‘Canonical’, ‘Canonical With Derivative’ or ‘FIR’. (Nipype default value:
Canonical
)mask (a pathlike object or string representing an existing file) – Restrict the fitting only to the region defined by this mask.
method (‘kalman’ or ‘ols’) – Method to fit the model, ols or kalma; kalman is more time consuming but it supports autoregressive model. (Nipype default value:
kalman
)model (‘ar1’ or ‘spherical’) – Autoregressive mode is available only for the kalman method. (Nipype default value:
ar1
)normalize_design_matrix (a boolean) – Normalize (zscore) the regressors before fitting. (Nipype default value:
False
)plot_design_matrix (a boolean) – (Nipype default value:
False
)save_residuals (a boolean) – (Nipype default value:
False
)- Outputs
a (a pathlike object or string representing an existing file)
axis (any value)
beta (a pathlike object or string representing an existing file)
constants (any value)
dof (any value)
nvbeta (any value)
reg_names (a list of items which are any value)
residuals (a pathlike object or string representing a file)
s2 (a pathlike object or string representing an existing file)