Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.glm.compute_fixed_effects

nilearn.glm.compute_fixed_effects(contrast_imgs, variance_imgs, mask=None, precision_weighted=False, dofs=None, return_z_score=False)[source]

Compute the fixed effects, given images of effects and variance.

Parameters:
contrast_imgslist of Nifti1Images or str or SurfaceImage

The input contrast images.

variance_imgslist of Nifti1Images or str or SurfaceImage

The input variance images.

maskNifti1Image or NiftiMasker instance or SurfaceMasker instance

or None, default=None Mask image. If None, it is recomputed from contrast_imgs.

precision_weightedbool, default=False

Whether fixed effects estimates should be weighted by inverse variance or not.

dofsarray-like or None, default=None

the degrees of freedom of the models with len = len(variance_imgs) when None, it is assumed that the degrees of freedom are 100 per input.

return_z_scorebool, default=False

Whether fixed_fx_z_score_img should be output or not.

Returns:
fixed_fx_contrast_imgNifti1Image or SurfaceImage

The fixed effects contrast computed within the mask.

fixed_fx_variance_imgNifti1Image or SurfaceImage

The fixed effects variance computed within the mask.

fixed_fx_stat_imgNifti1Image or SurfaceImage

The fixed effects stat computed within the mask.

fixed_fx_z_score_imgNifti1Image, optional

The fixed effects corresponding z-transform

Warns:
DeprecationWarning

Starting in version 0.13, fixed_fx_z_score_img will always be returned

Examples using nilearn.glm.compute_fixed_effects

Simple example of two-runs fMRI model fitting

Simple example of two-runs fMRI model fitting