AFNI program: 3dLocalSVD
Output of -help
Usage: 3dLocalSVD [options] inputdataset
* You may want to use 3dDetrend before running this program,
or at least use the '-polort' option.
* This program is highly experimental. And slowish.
* Computes the SVD of the time series from a neighborhood of each
voxel. An inricate way of 'smoothing' 3D+time datasets,
in some sense, maybe.
* For most purposes, program 3dLocalPV does the same thing, but faster.
The only reason to use 3dLocalSVD is if you are using -vproj
with the projection dimension ndim > 2.
Options:
-mask mset = restrict operations to this mask
-automask = create a mask from time series dataset
-prefix ppp = save SVD vector result into this new dataset
-input inputdataset = input time series dataset
-nbhd nnn = e.g., 'SPHERE(5)' 'TOHD(7)' etc.
-polort p [+] = detrending ['+' means to add trend back]
-vnorm = normalize data vectors
[strongly recommended]
-vproj [ndim] = project central data time series onto local SVD subspace
of dimension 'ndim'
[default: just output principal singular vector]
[for 'smoothing' purposes, '-vnorm -vproj 2' is a good idea]
++ Compile date = Oct 10 2024 {AFNI_24.3.02:linux_ubuntu_24_64}
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