This program performs a two-factor Analysis of Variance (ANOVA)
on 3D datasets



      -type k              : type of ANOVA model to be used:
                              k=1  fixed effects model  (A and B fixed)
                              k=2  random effects model (A and B random)
                              k=3  mixed effects model  (A fixed, B random)

      -alevels a           : a = number of levels of factor A

      -blevels b           : b = number of levels of factor B

      -dset 1 1 filename   : data set for level 1 of factor A
                                      and level 1 of factor B
            . . .                           . . .
      -dset i j filename   : data set for level i of factor A
                                      and level j of factor B
            . . .                           . . .
      -dset a b filename   : data set for level a of factor A
                                      and level b of factor B

     [-voxel num]          : screen output for voxel # num

     [-diskspace]          : print out disk space required for
                             program execution

     [-mask mset]          : use sub-brick #0 of dataset 'mset'
                             to define which voxels to process

   The following commands generate individual AFNI 2-sub-brick datasets:
  (In each case, output is written to the file with the specified
   prefix file name.)

     [-ftr prefix]         : F-statistic for treatment effect

     [-fa prefix]          : F-statistic for factor A effect

     [-fb prefix]          : F-statistic for factor B effect

     [-fab prefix]         : F-statistic for interaction

     [-amean i prefix]     : estimate mean of factor A level i

     [-bmean j prefix]     : estimate mean of factor B level j

     [-xmean i j prefix]   : estimate mean of cell at level i of factor A,
                                                      level j of factor B

     [-adiff i j prefix]   : difference between levels i and j of factor A

     [-bdiff i j prefix]   : difference between levels i and j of factor B

     [-xdiff i j k l prefix]     : difference between cell mean at A=i,B=j
                                                  and cell mean at A=k,B=l

     [-acontr c1 ... ca prefix]  : contrast in factor A levels

     [-bcontr c1 ... cb prefix]  : contrast in factor B levels

     [-xcontr c11 ... c1b c21 ... c2b  ...  ca1 ... cab  prefix]
                                 : contrast in cell means

The following command generates one AFNI 'bucket' type dataset:

     [-bucket prefix]      : create one AFNI 'bucket' dataset whose
                             sub-bricks are obtained by concatenating
                             the above output files; the output 'bucket'
                             is written to file with prefix file name

Modified ANOVA computation options:    (December, 2005)

     ** These options apply to model type 3, only.
        For details, see https://afni.nimh.nih.gov/sscc/gangc/ANOVA_Mod.html

     [-old_method]        : request to perform ANOVA using the previous
                            functionality (requires -OK, also)

     [-OK]                : confirm you understand that contrasts that
                            do not sum to zero have inflated t-stats, and
                            contrasts that do sum to zero assume sphericity
                            (to be used with -old_method)

     [-assume_sph]        : assume sphericity (zero-sum contrasts, only)

                            This allows use of the old_method for
                            computing contrasts which sum to zero (this
                            includes diffs, for instance).  Any contrast
                            that does not sum to zero is invalid, and
                            cannot be used with this option (such as


 Example of 3dANOVA2:

      Example is based on a study with a 3 x 4 mixed factorial design:

              Factor 1 - DONUTS has 3 levels:
                         (1) chocolate, (2) glazed, (3) sugar

              Factor 2 - SUBJECTS, of which there are 4 in this analysis:
                         (1) fred, (2) ethel, (3) lucy, (4) ricky

 3dANOVA2 -type 3 -alevels 3 -blevels 4   \
          -dset 1 1 fred_choc+tlrc        \
          -dset 2 1 fred_glaz+tlrc        \
          -dset 3 1 fred_sugr+tlrc        \
          -dset 1 2 ethel_choc+tlrc       \
          -dset 2 2 ethel_glaz+tlrc       \
          -dset 3 2 ethel_sugr+tlrc       \
          -dset 1 3 lucy_choc+tlrc        \
          -dset 2 3 lucy_glaz+tlrc        \
          -dset 3 3 lucy_sugr+tlrc        \
          -dset 1 3 ricky_choc+tlrc       \
          -dset 2 3 ricky_glaz+tlrc       \
          -dset 3 3 ricky_sugr+tlrc       \
          -amean 1 Chocolate              \
          -amean 2 Glazed                 \
          -amean 3 Sugar                  \
          -adiff 1 2 CvsG                 \
          -adiff 2 3 GvsS                 \
          -adiff 1 3 CvsS                 \
          -acontr 1 1 -2 CGvsS            \
          -acontr -2 1 1 CvsGS            \
          -acontr 1 -2 1 CSvsG            \
          -fa Donuts                      \
          -bucket ANOVA_results

 The -bucket option will place all of the 3dANOVA2 results (i.e., main
 effect of DONUTS, means for each of the 3 levels of DONUTS, and
 contrasts between the 3 levels of DONUTS) into one big dataset with
 multiple sub-bricks called ANOVA_results+tlrc.


N.B.: For this program, the user must specify 1 and only 1 sub-brick
      with each -dset command. That is, if an input dataset contains
      more than 1 sub-brick, a sub-brick selector must be used, e.g.:
      -dset 2 4 'fred+orig[3]'

This program accepts datasets that are modified on input according to the
following schemes:
  'r1+orig[3..5]'                                    {sub-brick selector}
  'r1+orig<100..200>'                                {sub-range selector}
  'r1+orig[3..5]<100..200>'                          {both selectors}
  '3dcalc( -a r1+orig -b r2+orig -expr 0.5*(a+b) )'  {calculation}
For the gruesome details, see the output of 'afni -help'.

Also see HowTo #5: Group Analysis on the AFNI website:

The default output format is to store the results as scaled short
(16-bit) integers.  This truncantion might cause significant errors.
If you receive warnings that look like this:
  *+ WARNING: TvsF[0] scale to shorts misfit = 8.09% -- *** Beware
then you can force the results to be saved in float format by
defining the environment variable AFNI_FLOATIZE to be YES
before running the program.  For convenience, you can do this
on the command line, as in
  3dANOVA -DAFNI_FLOATIZE=YES ... other options ...
Also see the following links:

++ Compile date = Aug 10 2020 {AFNI_20.2.11:linux_ubuntu_16_64}