Principal Component Analysis of 3D Datasets
Usage: 3dpc [options] dataset dataset ...
Each input dataset may have a sub-brick selector list.
Otherwise, all sub-bricks from a dataset will be used.
OPTIONS:
-dmean = remove the mean from each input brick (across space)
-vmean = remove the mean from each input voxel (across bricks)
[N.B.: -dmean and -vmean are mutually exclusive]
[default: don't remove either mean]
-vnorm = L2 normalize each input voxel time series
[occurs after the de-mean operations above,]
[and before the brick normalization below. ]
-normalize = L2 normalize each input brick (after mean subtraction)
[default: don't normalize]
-nscale = Scale the covariance matrix by the number of samples
This is not done by default for backward compatibility.
You probably want this option on.
-pcsave sss = 'sss' is the number of components to save in the output;
it can't be more than the number of input bricks
[default = none of them]
* To get all components, set 'sss' to a very large
number (more than the time series length), like 99999
You can also use the key word ALL, as in -pcsave ALL
to save all the components.
-reduce r pp = Compute a 'dimensionally reduced' dataset with the top
'r' eigenvalues and write to disk in dataset 'pp'
[default = don't compute this at all]
* If '-vmean' is given, then each voxel's mean will
be added back into the reduced time series. If you
don't want this behaviour, you could remove the mean
with 3dDetrend before running 3dpc.
* On the other hand, the effects of '-vnorm' and '-dmean'
and '-normalize' are not reversed in this output
(at least at present -- send some cookies and we'll talk).
-prefix pname = Name for output dataset (will be a bucket type);
* Also, the eigen-timeseries will be in 'pname'_vec.1D
(all of them) and in 'pnameNN.1D' for eigenvalue
#NN individually (NN=00 .. 'sss'-1, corresponding
to the brick index in the output dataset)
* The eigenvalues will be printed to file 'pname'_eig.1D
All eigenvalues are printed, regardless of '-pcsave'.
[default value of pname = 'pc']
-1ddum ddd = Add 'ddd' dummy lines to the top of each *.1D file.
These lines will have the value 999999, and can
be used to align the files appropriately.
[default value of ddd = 0]
-verbose = Print progress reports during the computations
-quiet = Don't print progress reports [the default]
-eigonly = Only compute eigenvalues, then
write them to 'pname'_eig.1D, and stop.
-float = Save eigen-bricks as floats
[default = shorts, scaled so that |max|=10000]
-mask mset = Use the 0 sub-brick of dataset 'mset' as a mask
to indicate which voxels to analyze (a sub-brick
selector is allowed) [default = use all voxels]
Example using 1D data a input, with each column being the equivalent
of a sub-brick:
3dpc -prefix mmm -dmean -nscale -pcsave ALL datafile.1D
++ Compile date = Oct 17 2024 {AFNI_24.3.03:linux_ubuntu_24_64}
