Hi Mark,
Thanks for your reply. My data is not from fMRI but from a different modality.
The dataset that I am talking about consists of multichannel HbO2 time series from functional near infra-red spectroscopy (fNIRS).
I would be very glad if I could use FLAME for this data, but I guess I can't.
> Sounds like what you are describing is a decent stab at incorporating
> lower-level variance information. However, what you are doing will
> only be appropriate when the within subject variance is large with
> respect to the between subject variance (normally it is the other way
> round). For example, if the between subject variance is much larger
> than the within subject variance then you should do no weighting.
In my current dataset, within-subject variance is larger than between subject variance.
Having said this, I wonder if I should worry about negative variance issue here.
> What you are describing has been done in the past but only when using
> permutation testing to help with the statistical validity.
Do you mean the statistical vality of of permutation test?
> What is the reason that you are not using FLAME? FLAME estimates the
> between-subject variance and and implicitly weights at the same time,
> all while taking into account the lower-level variance. Even though
> FLAME is derived from a Bayesian framework it is designed to do
> frequentist inference --- if that was your concern.
That is not really my concern. As I explained above, I cannot use FLAME
for my fNIRS dataset. Since I have to try using my own scripts, I prefer starting
with something that is not too complicated, but eventually I am interested in finding out how
FLAME type of analysis would influence the result.
Cheers,
Archana
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