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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?
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?
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