Dear Jesper,
On Tue, 2005-04-19 at 14:22 +0200, Jesper Andersson wrote:
> But, as I said, for PET it is much easier and you DO assume
> heteroscedasticity (at least across voxels) when you use SPM.
Many thanks for clearing that up for me! I'm pleased to know, and yet I
wonder what this all means wrt sensitivity. Since variance is estimated
voxelwise, there are precious few df... Is there an assumption of
conditions having equal variances -> pooling across conditions?
I think I have to follow up on your parenthetical note. I'm not sure I
understand why I'm asked if I want to model non-sphericity (replications
across subjects). I understand it as a question, whether or not I want
to estimate variance components (of either the proportional scaling or
ANCOVA normalization model) in my simple model (10subs, 5cond/sub). The
ensuing ReML crashes with each iteration resulting in a NaN.
Temporal non-sphericity (over voxels) : ...REML estimation
...
Surely there are not enough data to model the variance structure of this
PET design? Could you give an example where non-sphericity correction
made more sense (or how I should make more sense of the question)?
Thanks again for your help,
-Chris
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