Dear all,
A colleague of mine is doing a longitudinal analysis of visual fMRI data
trying to see correlation of changes in the fMRI activation with
clinical measures. She has specified a flexible factorial model that
before estimation looks like the picture in the attached file
design1.pdf, and after estimation looks like the picture in the attached
file design1_post-estimate.pdf. After estimation all the grey colours in
the design matrix change. We were wondering, is it a normal feature of
the analysis (since a lot of the columns are covariates) or does it mean
that there has been some mistake in the definition of the design model?
More details about the analysis:
For each patient there are: fMRI acquired at 5 time points, 2 clinical
measures (logman and humphrey) at each time point, and included in the
model as factors are also the age, gender, and which eye was affected.
The model specified was a flexible factorial with two factors (1:
timepoint, 2: subject), 1 main effect (factor 1) and 5 covariates. Two
of these covariates are the clinical variables, and for them in spm5 she
specified that they have "interaction" with Factor 1 and for the
"centering" she selected "user specified value", therefore in the model
each of these covariate is automatically split in 5 columns. The other 3
covariates are age, eye side and gender. Given this definition of the
model would you expect the grey levels in the design matrix to change
after estimation?
Many thanks.
Kind regards,
Laura
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