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The shape analysis subfodlers contain a text file with example scripts. These just need to be run from the design subfolders.
The following gives background information.
Running Analysis
Randomize is like a combination of "Estimate" and Results" in SPM in that it estimates a model and does a statistical test on a contrast. The inputs (analysis and mask) are the outputs from the previous vertex analysis step. Just as in SPM "Results" where we put in statistical tests and thresholds, we do the same with randomise (see here for initial details). The approach is to to look at bi-directional effects (--fonly), after de-meaning the data (-D). See guide (see Running vertex analysis) for details.
[user@localhost temp]$ randomise -i L_Hipp_vertexMNI_2sample.nii.gz -m L_Hipp_vertexMNI_2sample_The "-o" indicates the output file name, so best practice is to label this with the test parameters (e.g., -o L_Hipp_vertexMNI_2sample_rand_F3 ). However, the example scripts only use the region and vertex type in the name ("L_Hipp_vertexMNI_rand"), in order to allow the same commands to be used for multiple designs.
Step 1: create a correction-specific subfolder, i.e., one of vox, tfce, clustere, or clusterm.
Step 2: (in Linux from here on) open a terminal in that subfolder
Step 3: run the F-test; this will perform the correction for multiple comparisons.
[user@localhost vox]$ randomise -i ../R_Amyg_vertexMNI.nii.gz -m ../R_Amyg_vertexMNI_mask.nii.gz -o Lo R_HippAmyg_vertexMNI_2sample_rand -d des_2sample.mat -t des_2sample.con -f des_2sample.fts --fonly -D (output/multiple comparison correction options)vertexMNI_randN5000 -d ../design.mat -t ../design.con -f ../design.fts --fonly -n 5000 -D -x
Options: -i input data from all subjects, -m mask, -o output name; Note use "N5000" to distinguish this from the t test (next). Always need -d design matrix, t contrast, and for F test the f contrasts, --fonly does only F test, -D de-means, and -x does voxel correction.
[user@localhost vox]$ randomise -i ../R_Amyg_vertexMNI.nii.gz -m ../R_Amyg_vertexMNI_mask.nii.gz -o R_Amyg_vertexMNI_randN1 -d ../design.mat -t ../design.con -n 1 --glm_output -D -x
This can be repeated for the three other correction methods; note that the clustere and clusterm need F and t contrast thresholds specified.
Output Multiple comparison options: (see table in randomise User Guide):
Possible options include -x, --T2, -F <threshold<F threshold*>, -S <threshold><F threshold>, -c <t 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
-F 3
to look at bi-directional effects (--fonly), after de-meaning the data (-D), with an F threshold of 3, with cluster-based correction for multiple comparisons. The guide (see Running vertex analysis) suggests always using demean and F-test only).
The "-o" indicates the output file name, so you probably want to label this with the test parameters (e.g., -o L_Hipp_vertexMNI_2sample_rand_F3 for the suggested options).
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Older examples
Refer to table in randomise guide for files that are created (randomise User Guide).
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