7.1.153. 3dTcorrelateΒΆ

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++ 3dTcorrelate: AFNI version=AFNI_2011_12_21_1014 (Dec 16 2015) [64-bit] Usage: 3dTcorrelate [options] xset yset Computes the correlation coefficient between corresponding voxel time series in two input 3D+time datasets ‘xset’ and ‘yset’, and stores the output in a new 1 sub-brick dataset.

Options:

-pearson = Correlation is the normal Pearson (product moment)
correlation coefficient [this is the default method].
-spearman = Correlation is the Spearman (rank) correlation
coefficient.
-quadrant = Correlation is the quadrant correlation coefficient.
-ktaub = Correlation is Kendall’s tau_b coefficient.
++ For ‘continuous’ or finely-discretized data, tau_b
and rank correlation are nearly equivalent.
-covariance = Covariance instead of correlation. That would be
the pearson correlation without scaling by the product of the standard deviations.
-ycoef = Least squares coefficient that best fits y(t) to x(t),
after detrending. That is, if yd(t) is the detrended y(t) and xd(t) is the detrended x(t), then the ycoef value is from the OLSQ fit to xd(t) = ycoef * y(t) + error.
-polort m = Remove polynomical trend of order ‘m’, for m=-1..9.
[default is m=1; removal is by least squares]. Using m=-1 means no detrending; this is only useful for data/information that has been pre-processed.
-ort r.1D = Also detrend using the columns of the 1D file ‘r.1D’.
Only one -ort option can be given. If you want to use more than one, create a temporary file using 1dcat.
-autoclip = Clip off low-intensity regions in the two datasets,
-automask = so that the correlation is only computed between
high-intensity (presumably brain) voxels. The intensity level is determined the same way that 3dClipLevel works.
-prefix p = Save output into dataset with prefix ‘p’
[default prefix is ‘Tcorr’].

Notes: * The output dataset is functional bucket type, with just one

sub-brick, stored in floating point format.

– RWCox - Aug 2001

++ Compile date = Dec 16 2015

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