This is very helpful, thanks. In a design such as mine that is close to being
rank deficient, do the PEs deviate in a predictable way (e.g. all become smaller
in magnitude)?
In other words, if I want to find out whether there is something interesting in
the patterns that I saw with my PEs, is re-doing the design the only way?
Thanks,
Dost
-----Original Message-----
This is a little confusing - in a block design like this you should not
generally model _all_ conditions - there will normally be one condition
which you can consider "baseline" (I'm guessing CH in this case) which
you
should not model. This is because (at first level) the data gets
demeaned.
So you should probably only have EVs for A,B,C,D and use appropriate
contrasts to ask whatever questions you want.
> - All 5 PEs we specified in our design matrix (block design) are
negative
> in our ROI. Thinking that our ROI may have a lower blood flow level
than
> the brain overall as Joe Devlin mentioned, we ran avwstats on a couple
> filtered_func_img and get a mean of about 9900. But the raw data in
our
> ROIs are typically around 11000-13000!
I'm guessing that getting -ve PEs is a result of the design being close
to
rank deficient for the reasons above. Contrasts would still be well
conditioned, but PEs on their own would not be. I suspect this is what's
going on.
|