Dear SPM users
How does the GLM "decorrelate" dependent regressors?
"Thus, if two covariates are correlated, testing for the significance of the
parameter associated with one will only test for the part that is not
present in the second covariate". SPM manual spm_conman.m, Contrasts:
Non-orthogonal designs, page 7. See also Andrade et al., 1999, Neuroimage.
If two regressor share common variance, testing for one shows only the part
that is unique to this regressor. Is this true for both of them and how is
this achieved? A decorrelation like in spm_orth.m is not used (for
decorrelating prameters within one condition, parametric modulations),
because the order of regressors in a model does not matter, while the vector
order in a decorrelation procedure (i.e. spm_orth.m) does matter. Maybe
correlated regressors won't be changed but the shared variance will be taken
into account when testing i.e spm{T}, spm{F}? But how?
Thanks for your help
Best regard Rafael Lüchinger
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