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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]
    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)

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)

    [Optional]
    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')
            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)