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Dear AFNI users-
We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:
https://discuss.afni.nimh.nih.gov
Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.
The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.
Sincerely,
AFNI HQ
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Hi Gang,
I am a bit confused with the group contrasts. Let's say I have 3 groups (Control, Treatment1 and Treatment2).
Let's assume my model is Y ~ group + (group | ROI) + (1 |Subj1:Subj2) + (1 | mm(Subj1, Subj2, weights = cbind(1, 1), scale = FALSE))
Group is coded as Control = 0, Treatment1 = 1, and Treatment2 = -1.
Now if I am understanding correctly, in my output I will ha
by
vdiogo
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AFNI Message Board
Hi,
Thanks for confirming.
I am still wondering if it would be incorrect to use all 3 values as input in the first example. I ask because my intuition is that 3 values would be better than the mean of those 3 values. Is there any advantage to using the mean besides shorter computation time?
Vasco
by
vdiogo
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AFNI Message Board
Thank you for the clarification! I am, as you suggested, running the within-subject effects as separate models. However, I still have a question regarding this. Let assume for simplicity thay i have 3 r values per subject (rA, rB, and rC).
Firstly, if i wanted the overall effect regardless of video, should i input all 3 values or take the mean after r-to-z transformation and use that as input
by
vdiogo
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AFNI Message Board
Hi Gang,
Thank you so much. I coded the model as you specified with the 3 groups and was able to a pillot test on a subset of my sample.
Now I'm working on extracting effect estimates, summary info, plots, etc.
As I was going through the MBA code, I noticed that when summarizing the information for the posterior samples of Subject pairs, you apply some sort of standardization to all v
by
vdiogo
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AFNI Message Board
Hello Gang,
Thank you so much it all make sense. I still have two small questions.
1 - If I am including age, is the age the sum of ages of the 2 participants a given r corresponds to? Am I also correct in assuming that this summed age needs to be mean-centered across all pairs?
2- In my case I have 3 groups, and not interested in cross-group ISC, am I correct in assuming I can model thi
by
vdiogo
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AFNI Message Board
Hello Gang,
Thanks for the clarification regarding the three models.
Yes I can easily obtain the ISC between the two levels, so I would just do that. I'm still curious what a model with such a factor incorporated would look like.
And yes, the paper you attached is precisely what I am trying to accomplish, so I was wondering if the adaptation i mentioned to MBA would the correct one (i
by
vdiogo
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AFNI Message Board
Hello again,
I have some additional questions after going through the MBA script from AFNI. I suppose this question might be more adequate for the stan forums, but if you could help that would be much appreciated. I came up with the following model for my analysis:
Y ~ 1 + group_dummy1 + group_dummy2 + (1 | ROI) + (1 + group_dummy1 + group_dummy2 | mm(Subj1, Subj2, weights = cbind(1, 1),
by
vdiogo
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AFNI Message Board
Hi Gang,
Thank you for the clarifications. I understand that you similarly recommend that if using BML for this type of analysis, each condition contrast should be run separately for a computational time. Is there a recommended way to correct for the fact that I would be running 3 models (e.g. by adjusting quantiles when plotting and interpreting results)?
Also, I wanted to check if to adap
by
vdiogo
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AFNI Message Board
Hello,
I am using 3dISC to model data from a mixed-effects design. I have 3 between-subject levels (patients with drug; patients with placebo; controls with placebo) and 4 within-subject levels (4 types of videos all subjects watched).
I am not interested in the between-group ISC and started by building a model disregarding the within-subject effects (averaging the pairwise correlation matr
by
vdiogo
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AFNI Message Board