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# interfaces.nipy.model

## EstimateContrast

Link to code

Estimate contrast of a fitted model.

Inputs:

    [Mandatory]
    axis
    beta: (an existing file name)
            beta coefficients of the fitted model
    constants
    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
            degrees of freedom
    nvbeta
    reg_names: (a list of items which are any value)
    s2: (an existing file name)
            squared variance of the residuals

    [Optional]
    axis
    beta: (an existing file name)
            beta coefficients of the fitted model
    constants
    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
            degrees of freedom
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mask: (a file name)
    nvbeta
    reg_names: (a list of items which are any value)
    s2: (an existing file name)
            squared variance of the residuals

Outputs:

    p_maps: (an existing file name)
    stat_maps: (an existing file name)
    z_maps: (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]
    TR: (a float)
    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'
    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'
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mask: (a file name)
            restrict the fitting only to the region defined by this mask
    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
    model: ('ar1' or 'spherical', nipype default value: ar1)
            autoregressive mode is available only for the kalman method
    normalize_design_matrix: (a boolean, nipype default value: False)
            normalize (zscore) the regressors before fitting
    plot_design_matrix: (a boolean, nipype default value: False)
    save_residuals: (a boolean, nipype default value: False)
    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)

Outputs:

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