You can't covary for gender in your repeated measure model, for the
reason that I explained. Try including a gender covariate and you'll
find that your design matrix will be rank deficient. As for age, it
would only be an option to consider if you want to use a different value
for age for each repeated visit of a subject, and even then I think
you'll get a poorly conditioned design matrix unless your age at the
repeated visits for a subject differed in an appreciable manner (where
"appreciable" would be relative to the variation in ages across
subjects).
cheers,
-MH
On Thu, 2011-05-12 at 19:44 +0200, Alexander Olsen wrote:
> Hi Michael,
> Thank you so much for helping me. So that means that I should set up the
> design matrices without any covariates for age and gender?
>
> cheers,
> Alexander
>
>
> Michael Harms wrote:
> > Hi Alexander,
> > Given that you are modeling the mean of each subject using a repeated
> > measures design, that mean already incorporates that subject's age and
> > gender as part of its estimate. That is, if you added EVs for age
> > and/or gender, those EVs can be represented as a linear combination of
> > the columns modeling each subject's overall mean, and thus you would get
> > a degenerate design matrix. (This wouldn't strictly be true for age IF
> > the value you used for age differed across the repeated scans of a
> > subject, but unless your scans were spaced far apart temporally, you
> > still might get a poorly conditioned design matrix).
> >
> > cheers,
> > -MH
> >
> > On Thu, 2011-05-12 at 01:12 +0100, Alexander Olsen wrote:
> >
> >> Dear FLS experts,
> >>
> >> I'm struggling a bit with setting up my analysis. What I would like to do is a repeated measures design with four timepoints, as in this example:
> >>
> >> http://www.fmrib.ox.ac.uk/fsl/feat5/detail.html#ANOVA1factor4levelsRepeatedMeasures
> >>
> >> However, I would also like to add age and gender as covariates in order to "remove" these effects. I hope anyone can help me, or direct me to an example which describes this.
> >>
> >> Thank you so much in advance.
> >>
> >> Alexander
> >>
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