On 1/15/2020 3:08 PM, Graduate Imaging wrote:
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Hello,
I recently ran a vertex wise analysis on two different projects the first had
three groups with three co-variates that looked at if group membership was
associated with brain volume. The second project had two groups with three
co-variates looking at psychiatric x BMI interaction effect on brain volume.
I'm interested in running ROI analyses in matlab to ensure the same matrices
were used.
To run the ROI analyses I'm going to important the design matrix from the
vertex wise analysis and use the fast_glmfit and fast_fratio commands as shown
below:
X = load('Xg.dat');
C = load('C.dat');
y = load('ROI.dat'); text file containing participant ROI values from the DKT
atlas.
[beta rvar] = fast_glmfit(y,X);
[F pvalues] = fast_fratio(beta,X,rvar,C
My first question is the beta values that are calculated from fast_glmfit are
unstandardized is there anyway way to have it compute standardized values?
I think this is how you would compute that
betastddev = sqrt(rvar*diag(inv(X'*X)));
betastandard = beta./betastddev;
My second question is when I ran the ROI analyses looking at the diagnosis x
BMI interaction effect it outputs an array of beta values for each regressor.
In addition to each groups beta value (for BMI) I'm interested in the beta
value of the interaction (ie diagnosis x BMI). I was wondering how do i go
about obtaining this? Would I have to create a new matrix with the interaction
term included in it?
If so would the analysis essentially be one group with the diagnosis x BMI
interaction term plus the three co-variates?
If you are using an FSGD file and have used DODS, then you can create an
interaction contrast. Send me your fsgd file if you want further help
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