7.1.110. 3dNormalityTest

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Program: 3dNormalityTest

  • This program tests the input values at each voxel for normality, using the Anderson-Darling method:

  • 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 = Dec 16 2015

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