It would be great to add a few options to run with Brain_Data.regress() that users don't often do themselves. Not defaults, just kwargs:
- VIF check on passed Design_Matrix
- Scaling to interpret betas as % signal change
- Adjusting SE epsilon factor to prevent divide-by-zeros or using
np.lstsqrs
It would be great to add a few options to run with
Brain_Data.regress()that users don't often do themselves. Not defaults, just kwargs:np.lstsqrs