Hello AFNI users,

I had a question about the output for the LOGIT implementation of 3dLME.

No matter how I specify the model, it always produces a final unnamed sub-brick that seems to have no beta or z parameter estimates (i.e. all 0).

For example, when running the following command on a dataset:

3dLME \
-prefix LAPTOP.REHO.LOG.2RCI.V1.gb6.1x1x1 -jobs 20 \
-mask ${mask_path} \
-model "REHO_MAP*Age+meanFD" \
-vVars "REHO_MAP" \
-qVars "Age,meanFD" \
-SS_type 3 \
-LOGIT \
-dataTable \
`cat ${results_path}/dataTable_REHO | sed '$ s/.$//'`

The program will produce the acc, cutoff, and parameter estimate outputs, and the parameter estimate map will give all appropriate sub-bricks for the model, but will always add the last unnamed sub-brick (see below output from 3dinfo; sub-brick #10):

Dataset File: LAPTOP.REHO.LOG.2RCI.V1.gb6.1x1x1+tlrc
Identifier Code: XYZ_b17x7IP5wAar7O5t36PX6C Creation Date:
Template Space: TT_N27
Dataset Type: Func-Bucket (-fbuc)
Byte Order: LSB_FIRST [this CPU native = LSB_FIRST]
Storage Mode: BRIK
Storage Space: 102,154,822 (102 million [mega]) bytes
Geometry String: "MATRIX(1,0,0,-80,0,1,0,-80,0,0,1,-65):161,191,151"
Data Axes Tilt: Plumb
Data Axes Orientation:
first (x) = Right-to-Left
second (y) = Anterior-to-Posterior
third (z) = Inferior-to-Superior [-orient RAI]
R-to-L extent: -80.000 [R] -to- 80.000 [L] -step- 1.000 mm [161 voxels]
A-to-P extent: -80.000 [A] -to- 110.000 [P] -step- 1.000 mm [191 voxels]
I-to-S extent: -65.000 -to- 85.000 [S] -step- 1.000 mm [151 voxels]
Number of values stored at each pixel = 11
-- At sub-brick #0 '(Intercept)' datum type is short: -32767 to 0 [internal]
[* 8.14932e-05] -2.67029 to 0 [scaled]
-- At sub-brick #1 '(Intercept) z' datum type is short: -32767 to 0 [internal]
[* 0.000184701] -6.0521 to 0 [scaled]
statcode = fizt
-- At sub-brick #2 'REHO_MAP' datum type is short: -18294 to 32767 [internal]
[* 0.000296949] -5.43238 to 9.73013 [scaled]
-- At sub-brick #3 'REHO_MAP z' datum type is short: -31459 to 32767 [internal]
[* 0.000109312] -3.43884 to 3.58183 [scaled]
statcode = fizt
-- At sub-brick #4 'Age' datum type is short: -9522 to 32767 [internal]
[* 1.01399e-05] -0.0965524 to 0.332255 [scaled]
-- At sub-brick #5 'Age z' datum type is short: -15432 to 32767 [internal]
[* 5.28039e-05] -0.81487 to 1.73023 [scaled]
statcode = fizt
-- At sub-brick #6 'meanFD' datum type is short: -8865 to 32767 [internal]
[* 0.000525982] -4.66283 to 17.2348 [scaled]
-- At sub-brick #7 'meanFD z' datum type is short: -11118 to 32767 [internal]
[* 8.61567e-05] -0.95789 to 2.8231 [scaled]
statcode = fizt
-- At sub-brick #8 'REHO_MAP:Age' datum type is short: -12696 to 32767 [internal]
[* 0.000229167] -2.9095 to 7.5091 [scaled]
-- At sub-brick #9 'REHO_MAP:Age z' datum type is short: 0 to 32767 [internal]
[* 5.59973e-06] 0 to 0.183486 [scaled]
statcode = fizt
-- At sub-brick #10 '#10' datum type is short: 0 to 0


I was curious what this last sub-brick was, if it is a result of me not implementing the model correctly or something else?

If additional information is needed, please let me know.

Thank you,

David