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)[source]#

Compute the fixed effects, given images of effects and variance

Parameters:
contrast_imgslist of Nifti1Images or strings

The input contrast images.

variance_imgslist of Nifti1Images or strings

The input variance images.

maskNifti1Image or NiftiMasker instance or None, optional

Mask image. If None, it is recomputed from contrast_imgs.

precision_weightedBool, optional

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

Returns:
fixed_fx_contrast_imgNifti1Image

The fixed effects contrast computed within the mask.

fixed_fx_variance_imgNifti1Image

The fixed effects variance computed within the mask.

fixed_fx_t_imgNifti1Image

The fixed effects t-test computed within the mask.

Notes

This function is experimental. It may change in any future release of Nilearn.

Examples using nilearn.glm.compute_fixed_effects#

Example of explicit fixed effects fMRI model fitting

Example of explicit fixed effects fMRI model fitting

Example of explicit fixed effects fMRI model fitting