Hi again Nikos,
in matlab or in excel, you just need to take your
first regressor, calculate the average value of this
regressor across the 28 subjects you have, and then
remove this mean from your original regressor. Then
repeat this operation for the two other regressors and
replace the non-demeaned values by the demeaned ones
in your design.mat.
For instance, if you've got 9 subjects with the first
regressor:
2
4
6
8
10
12
14
16
18
The mean across the 9 subjects is ten, so you will
need to replace it by:
2-10=-8
4-10=-6
6-10=-4
8-10=-2
10-10=0
12-10=2
14-10=4
16-10=6
18-10=8
Cheers,
Gwenaelle
--- Nikolaos Koutsouleris
<[log in to unmask]> a écrit :
> Hello Gwenaëlle,
>
> thank you very much for your reply!
> Yes, I used the -D option, but how should I demean
> the three covariates ?
>
> Best Regards,
>
> Nikos
>
> Gwenaëlle DOUAUD wrote:
> > Hi Nikolaos,
> >
> > when you mention "demean the data first", does it
> mean
> > that you simply run randomise with the -D option?
> If
> > yes, this is not enough: you also need to demean
> the 3
> > covariates in your model as it appears you haven't
> > done so.
> >
> > Hope this helps,
> > Gwenaelle
> >
> >
> > --- Nikolaos Koutsouleris
> > <[log in to unmask]> a
> écrit :
> >
> >
> >> Der FSL'ers,
> >>
> >> I tried to estimate a VBM multiple regression
> design
> >> with randomise, which
> >> gave me unexpectedly high p values. As I am
> >> skeptical about my results, I am
> >> wondering if I made a mistake at some point:
> >>
> >> The following design with 1 covariate of interest
> >> (1st regressor) and two
> >> nuisance variables (2nd column: age & 3d column:
> >> global GM volume) was
> >> created with the GLM tool:
> >>
> >> %! VEST-Waveform File
> >> /NumWaves 3
> >> /NumPoints 28
> >> /PPheights 9.800000e-01 3.810000e+01
> 8.607700e+02
> >> /Matrix
> >> 7.900000e-01 2.824000e+01 6.572900e+02
> >> 8.800000e-01 2.600000e+01 6.204000e+02
> >> 7.400000e-01 2.942000e+01 7.207500e+02
> >> 8.100000e-01 2.494000e+01 7.028600e+02
> >> 9.800000e-01 2.460000e+01 6.524600e+02
> >> 9.300000e-01 2.275000e+01 5.986000e+02
> >> ...
> >>
> >> I was interested in positive and negative
> >> correlation between focal GM
> >> volume and regressor 1. Therefore, I created the
> >> following contrasts in Glm:
> >>
> >> %! VEST-Waveform File
> >> /ContrastName1 pos_pA
> >> /ContrastName2 neg_pA
> >> /NumWaves 3
> >> /NumContrasts 2
> >> /PPheights 8.410671e-01 8.410671e-01
> >> /RequiredEffect 4.138 4.138
> >> /Matrix
> >> 1.000000e+00 0.000000e+00 0.000000e+00
> >> -1.000000e+00 0.000000e+00 0.000000e+00
> >>
> >> I started randomise with the following
> parameters:
> >> * -c 2 and -C 2
> >> * 5000 permutations
> >> * Demean the data first
> >>
> >> I checked the order of my covariate values and
> the
> >> smoothed (10 mm FWHM),
> >> modulated gray matter partitions. They are ok.
> The
> >> cluster-level analyses
> >> result in a cluster that covers the entire
> cortex.
> >> Did I something wrong?
> >>
> >> Any help would be greatly appreciated...
> >> Cheers,
> >>
> >> Nikos
> >>
> >>
> >
> >
> >
> >
>
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