If you don't model a mean (intercept) in your design matrix, and you
don't include the -D option, then your results are not interpretable --
you have nothing in your model to account for a constant term in your
data.
As for which design matrix you should use, it shouldn't matter in this
particular case because the slope of a regressor (and its significance)
is not affected by whether or not you demean that regressor. See
Jeanette Mumford's web page on centering for illustrations.
cheers,
-MH
On Mon, 2011-11-28 at 13:52 -0600, Rodrigo Perea wrote:
> Michael,
> Thanks for your response....so what you are suggesting
> me to do is just to run the analysis with my first matrix and the -D option? If so,
> why didnt I get any significant regions and why did I get some when I ran it without the -D option?
> Thanks in advance,
> Rodrigo
>
>
>
> Hi Rodrigo,
> The -D option in randomise has to do with whether the input data is to
> be demeaned -- it has nothing to do with whether the regressors in the
> design matrix are demeaned.
>
> Quoting the web help page for randomise:
> The -D option tells randomise to demean the data before continuing -
> this is necessary if you are not modelling the mean in the design matrix
>
> In your case, your design matrices are not modeling a mean, so the -D
> option must be included.
>
> cheers,
> -MH
>
> On Mon, 2011-11-28 at 19:32 +0000, Rodrigo Perea wrote:
> > Could someone explain me this. So I have a column that I want to use as a regressor in my TBSS analysis. I created a matrix without demeaning this value and I got a significant value at p<0.05 of what I expected. Then I demeaned the regressor and also I used the -D option and after 5000 permutations no significance was found in any direction. Why? If someone could help guide me in the right direction, I would greatly appreciate it.
> >
> >
> > Thanks in advance.
> > Rodrigo
> >
> > My non-demeanded design and contrast matrix are (I used this same matrix when using the -D option):
> >
> > design.mat (sex and age as covariates):
> > /Matrix
> >
> > Sex Age Regressor Variable
> > 1.000000e+00 7.500000e+01 1.634750e+03
> > 0.000000e+00 7.500000e+01 1.153250e+03
> > 0.000000e+00 8.300000e+01 3.151000e+03
> > 0.000000e+00 7.300000e+01 1.651000e+03
> > 0.000000e+00 8.100000e+01 1.503250e+03
> > 1.000000e+00 7.100000e+01 1.325500e+03
> > .
> > .
> > .
> >
> > design.con:
> > /Matrix
> > 0.000000e+00 0.000000e+00 1.000000e+00
> > 0.000000e+00 0.000000e+00 -1.000000e+00
> >
> >
> >
> > My demeaned design matrix and contrast are:
> >
> >
> > design.mat (sex and age as covariates):
> > /Matrix
> >
> > Age Sex Regressor
> > 1.000000e+00 7.500000e+01 -6.570900e+02
> > 0.000000e+00 7.500000e+01 -1.138590e+03
> > 0.000000e+00 8.300000e+01 8.591600e+02
> > 0.000000e+00 7.300000e+01 -6.408400e+02
> > 0.000000e+00 8.100000e+01 -7.885900e+02
> > 1.000000e+00 7.100000e+01 -9.663400e+02
> > .
> > .
> > .
> > '
> >
> > design.con:
> > /Matrix
> > 0.000000e+00 0.000000e+00 1.000000e+00
> > 0.000000e+00 0.000000e+00 -1.000000e+00
>
> Rodrigo Dennis Perea
> Graduate Research Assistant
> [log in to unmask]
> Bioengineering Program
> The University of Kansas
>
|