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[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -t des_2sample.con -f des_2sample.fts --fonly -D (output/multiple comparison correction options)
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Possible options include -x, --T2, -F <optional threshold><threshold*>, -S <threshold>
* At some point, this could also run without a threshold; however, it usually gives an error ("F missing non-optional argument" or similar).
Just as in SPM "Results" where we put in statistical tests and thresholds, we do the same with randomise (see here for initial details). A starting point might be
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[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -t des_2sample.con -f des_2sample.fts --fonly -D -F 3
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -t des_2sample.con -f des_2sample.fts --fonly -D -x
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -f t des_2sample.fts --fonly -D -x
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -con -f des_2sample.
ftsfts --fonly -D --T2
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -f t des_2sample.fts --fonly -D -S 3
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_mask.nii.gz -o L_Hipp_vertexMNI_2sample_rand -d des_2sample.mat -con -f des_2sample.fts --fonly -D -
FS 3
Each analysis will generate a number of files, one for each contrast. There are maps of f statistics, p values, and corrected p values, regions that are significantly different based on the threshold if one was included (F statistic > 3 in the last example):
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