Dear Torsten,
The GLM outputs, even when used with normalised inputs or divided by the standard error, are not the same as correlation values. They are similar, and statistical tests are identical, but if you need an r value to convert to Z with the Fisher transform, then you need to calculate it using the full formula for correlation which is subtly different. Or you can just use Z values output by the GLM directly.
All the best,
Mark
On 6 Sep 2012, at 06:32, Torsten Ruest <[log in to unmask]> wrote:
> Dear Steve,
>
> thanks for your comment - I thought maybe it's just some rounding errors . I've tried to find the error that may explain the r value higher than 1, but...
>
> I used the method described in the mailing list as well as elsewhere:
>
> 1. regress nuisance variables
> 2. take the residual (1.), add back the mean, normalise the latter by dividing by it's standard deviation, then extract the seed mean time timeseries.
> 3. add the normalised "meaned" residual and the normalised "meaned" timeseries for that seed to the feat design and go.
>
> I also tried the other way I read here:
> 1. add normalised "meaned" timeseries to feat
> 2. add unnormalised "meaned" residual to feat
> 3. divide resulting cope / varcope by standard deviation
>
> the results are virtually identical.
>
> I take it that after normalization of the residuals and timeseries, the glm output should be in the range -1:1, so for isolating the error, whatever happened before, eg during nuisance regression etc, is kind of irrelevant ?
>
> Thanks in advance.
>
> Best wishes,
>
> Torsten
>
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