Hi Mara,
Hi - you can, but you need to be very careful about the details. To
be as accurate as possible, you would need to do a separate analysis
for each of these regressors, orthogonalising the regressor of
interest wrt the "confounds" (ie the others in the model) and putting
them into the -x matrix. E.g. orth R1 wrt R2 and the interaction,
then put R1 and R2 into the -x matrix.
Or - if you wait a few weeks for the new release, the new version of
randomise will do all this for you!
Hope this helps? Cheers, Steve.
On 4 Jul 2007, at 12:32, Mara Cercignani wrote:
> Hi,
>
> a quick question about randomise. I've noticed on teh web page it
> says:
> "if you have "confound regressors", randomise needs those to be
> removed
> before continuing. Therefore, unlike with FEAT, you need to specify
> these
> as a separate design matrix and use the -x option when calling
> randomise;
> randomise then regresses these out of the data before continuing. Note
> that for this to make sense, your confounds and design of interest
> need to
> be orthogonal. In fact, in general in randomise, your regressors
> should be
> orthogonal to each other."
>
> I do not have confound regressors, but I want to perform a multi-
> regressor
> analysis, to look at the effects of regressor1, regressor2 and
> regressor1-by-regressor2 interaction. Does the above mean that I
> CANT do
> it in randomise?
>
> Cheers
>
> Mara
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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