Usage: 3dLocalBistat [options] dataset1 dataset2
This program computes statistics between 2 datasets,
at each voxel, based on a local neighborhood of that voxel.
- The neighborhood is defined by the '-nbhd' option.
- Statistics to be calculated are defined by the '-stat' option(s).
- The 2 input datasets should have the same number of sub-bricks.
- OR dataset1 should have 1 sub-brick and dataset2 can have more than 1:
- In which case, the statistics of dataset2 against dataset1 are
calculated for the #0 sub-brick of dataset1 against each sub-brick
of dataset2.
OPTIONS
-------
-nbhd 'nnn' = The string 'nnn' defines the region around each
voxel that will be extracted for the statistics
calculation. The format of the 'nnn' string are:
* 'SPHERE(r)' where 'r' is the radius in mm;
the neighborhood is all voxels whose center-to-
center distance is less than or equal to 'r'.
** A negative value for 'r' means that the region
is calculated using voxel indexes rather than
voxel dimensions; that is, the neighborhood
region is a "sphere" in voxel indexes of
"radius" abs(r).
* 'RECT(a,b,c)' is a rectangular block which
proceeds plus-or-minus 'a' mm in the x-direction,
'b' mm in the y-direction, and 'c' mm in the
z-direction. The correspondence between the
dataset xyz axes and the actual spatial orientation
can be determined by using program 3dinfo.
** A negative value for 'a' means that the region
extends plus-and-minus abs(a) voxels in the
x-direction, rather than plus-and-minus a mm.
Mutatis mutandum for negative 'b' and/or 'c'.
* 'RHDD(r)' is a rhombic dodecahedron of 'radius' r.
* 'TOHD(r)' is a truncated octahedron of 'radius' r.
-stat sss = Compute the statistic named 'sss' on the values
extracted from the region around each voxel:
* pearson = Pearson correlation coefficient
* spearman = Spearman correlation coefficient
* quadrant = Quadrant correlation coefficient
* mutinfo = Mutual Information
* normuti = Normalized Mutual Information
* jointent = Joint entropy
* hellinger= Hellinger metric
* crU = Correlation ratio (Unsymmetric)
* crM = Correlation ratio (symmetrized by Multiplication)
* crA = Correlation ratio (symmetrized by Addition)
* L2slope = slope of least-squares (L2) linear regression of
the data from dataset1 vs. the dataset2
(i.e., d2 = a + b*d1 ==> this is 'b')
* L1slope = slope of least-absolute-sum (L1) linear regression
of the data from dataset1 vs. the dataset2
* num = number of the values in the region:
with the use of -mask or -automask,
the size of the region around any given
voxel will vary; this option lets you
map that size.
* ALL = all of the above, in that order
More than one '-stat' option can be used.
-mask mset = Read in dataset 'mset' and use the nonzero voxels
therein as a mask. Voxels NOT in the mask will
not be used in the neighborhood of any voxel. Also,
a voxel NOT in the mask will have its statistic(s)
computed as zero (0).
-automask = Compute the mask as in program 3dAutomask.
-mask and -automask are mutually exclusive: that is,
you can only specify one mask.
-weight ws = Use dataset 'ws' as a weight. Only applies to 'pearson'.
-prefix ppp = Use string 'ppp' as the prefix for the output dataset.
The output dataset is always stored as floats.
ADVANCED OPTIONS
----------------
-histpow pp = By default, the number of bins in the histogram used
for calculating the Hellinger, Mutual Information,
and Correlation Ratio statistics is n^(1/3), where n
is the number of data points in the -nbhd mask. You
can change that exponent to 'pp' with this option.
-histbin nn = Or you can just set the number of bins directly to 'nn'.
-hclip1 a b = Clip dataset1 to lie between values 'a' and 'b'. If 'a'
and 'b' end in '%', then these values are percentage
points on the cumulative histogram.
-hclip2 a b = Similar to '-hclip1' for dataset2.
-----------------------------
Author: RWCox - October 2006.
++ Compile date = Oct 13 2022 {AFNI_22.3.03:linux_ubuntu_16_64}
