Program: 3dNormalityTest
* This program tests the input values at each voxel for normality,
using the Anderson-Darling method:
http://en.wikipedia.org/wiki/Anderson-Darling_test
* Each voxel must have at least 5 values (sub-bricks).
* The resulting dataset has the Anderson-Darling statistic converted
to an exponentially distributed variable, so it can be thresholded
with the AFNI slider and display a nominal p-value below. If you
want the A-D statistic un-converted, use the '-noexp' option.
* Conversion of the A-D statistic to a p-value is done via simulation
of the null distribution.
OPTIONS:
--------
-input dset = Specifies the input dataset.
Alternatively, the input dataset can be given as the
last argument on the command line, after all other
options.
-prefix ppp = Specifies the name for the output dataset.
-noexp = Do not convert the A-D statistic to an exponentially
distributed value -- just leave the raw A-D score in
the output dataset.
-pval = Output the results as a pure (estimated) p-value.
EXAMPLES:
---------
(1) Simulate a 2D square dataset with the values being normal on one
edge and exponentially distributed on the other, and mixed in-between.
3dUndump -dimen 101 101 1 -prefix UUU
3dcalc -datum float -a UUU+orig -b '1D: 0 0 0 0 0 0 0 0 0 0' -prefix NNN \
-expr 'i*gran(0,1.4)+(100-i)*eran(4)'
rm -f UUU+orig.*
3dNormalityTest -prefix Ntest -input NNN+orig
afni -com 'OPEN_WINDOW axialimage' Ntest+orig
In the above script, the UUU+orig dataset is created just to provide a spatial
template for 3dcalc. The '1D: 0 ... 0' input to 3dcalc is a time template
to create a dataset with 10 time points. The values are random deviates,
ranging from pure Gaussian where i=100 to pure exponential at i=0.
(2) Simulate a single logistic random variable into a 1D file and compute
the A-D nominal p-value:
1deval -num 200 -expr 'lran(2)' > logg.1D
3dNormalityTest -input logg.1D\' -prefix stdout: -pval
Note the necessity to transpose the logg.1D file (with the \' operator),
since 3D programs interpret each 1D file row as a voxel time series.
++ March 2012 -- by The Ghost of Carl Friedrich Gauss
++ Compile date = Oct 13 2022 {AFNI_22.3.03:linux_ubuntu_16_64}
