That is how you would compute a partial model fit, though I'm not sure
which part of the model you are trying to fit. In what you have, it will
remove the main effect of group (but not interactions).
On 5/14/2020 1:30 PM, Hua, Jessica wrote:
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Hi FreeSurfer Experts,
I am trying to extract or calculate the adjusted values for each participant in
each cluster after accounting for covariates in my permutation analysis.
This is a 3 group analysis with 3 covariates (age, sex, and ICV).
After navigating to the glmdir folder of interest, I ran the following code:
X = load('Xg.dat');
The X matrix was 240x12 (see attached file)
* Column 1: 1s for Group 1 and 0s for Group 2 and 0s for Group 3
* Column 2: 0s for Group 1 and 1s for Group 2 and 0s for Group 3
* Column 3: 0s for Group 1 and 0s for Group 2 and 1s for Group 3
* Column 4: Age for Group 1 and 0s for Group 2 and 0s for Group 3
* Column 5: 0s for Group 1 and Age for Group 2 and 0s for Group 3
* Column 6: 0s for Group 1 and 0s for Group 2 and Age for Group 3
* Column 7: Sex for Group 1 and 0s for Group 2 and 0s for Group 3
* Column 8: 0s for Group 1 and Sex for Group 2 and 0s for Group 3
* Column 9: 0s for Group 1 and 0s for Group 2 and Sex for Group 3
* Column 10: zICV for Group 1 and 0s for Group 2 and 0s for Group 3
* Column 11: 0s for Group 1 and zICV for Group 2 and 0s for Group 3
* Column 12: 0s for Group 1 and 0s for Group 2 and zICV for Group 3
beta = MRIread('beta.mgh');
betamat = fast_vol2mat(beta);
And I got stuck at this step: yhatmat = X(:,[i j k m n])*betamat([i j k m n],:);
I wasn't sure how to modify the code. Is this correct: yhatmat = X(:,[4 5 6 7 8
9 10 11 12])*betamat([4 6 7 8 9 10 11 12],:);
Once I figure out the above step, I will finish with this code:
yhat = beta;
yhat.vol = fast_mat2vol(yhat,yhat.volsize);
---
Jessica Hua, M.A.
Cognitive and Emotional Control Lab
Doctoral Candidate Clinical Psychology
University of Missouri
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