Indeed - FSL is so good that it can give you negative p-values.
Thanks for pointing this out Ged. We've tracked this down to a slight
rare buglette in the uncorrected p-value estimation in randomise.
This doesn't affect any of the corrected p-values.
Matthew will release a patch within the next day.
Cheers, Steve.
On 15 Mar 2007, at 00:14, Ged Ridgway wrote:
> Hi,
>
> I've been playing around with randomise on some (unrealistic)
> simulated data, and have got voxel-wise (1-p)-values of more than
> 1, which seems like it should be impossible...
>
> I've put a very small (10x11x12x10) 4D NIfTI here:
> http://www.cs.ucl.ac.uk/staff/gridgway/test.nii
> which appears to give the following results:
>
> $ randomise -i test -o test_randomise -1
> $ avwstats test_randomise_vox_tstat1 -R -x
> 0.000000 1.068411 5 4 5
> $ avwmeants -i test_randomise_tstat1 -c 5 4 5
> 3.851071 # fair enough
> $ avwmeants -i test_randomise_max_tstat1 -c 5 4 5
> 0.418945 # fair enough
> $ avwmeants -i test_randomise_vox_tstat1 -c 5 4 5
> 1.068411 # ???
> $ avwmeants -i test -c 5 4 5
> 0.899183
> 2.682735
> 1.605658
> 1.076425
> 2.913476
> -0.619041
> 0.911262
> 0.595414
> 1.170928
> 1.059577
>
> As far as I understand the vox_tstat image, each voxel's p-value is
> only dependent on the raw data for that voxel (as I have not
> selected variance smoothing). If I put just this voxel's data into
> MATLAB and resample (with replacement, sorry, 1024 or 10000 times)
> then I get a p-value of about 0.003, which seems more reasonable
> than a negative one...
>
> It's getting late though, so the chances of my having gone
> completely mad are quite high...
>
> Thanks,
> Ged.
>
> P.S. I'm using FSL 3.3.9 (randomise v0.11) on 32-bit RedHat Ent.
> Linux Rel. 3.
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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