3dLME Model Error / Script Crashing

Hi there,

I’m currently trying to run a simple 3dLME with the following:

3dLME -prefix final_PCLTotal_LME -jobs 24
-model “Time*PCLTotal+age+Gender”
-qVars “age”
-ranEff “~1+age”
-SS_type 3
-dataTable @CONN_LME_DataTable.txt

The script successfully reads the input files, but then gets stuck on the model test before killing it after about 5-10 minutes (it does this on its own, I do not end it). I have run file_tool on my script and run the FIXED version, so I am guessing there is a problem with the model since I don’t have any GLT coding in the script. I’m not sure what to change about the model, but any advice would be helpful.

Here’s the output I get:

Reading input files now…
Reading input files: Done!

If the program hangs here for more than, for example, half an hour,
kill the process because the model specification or the GLT coding
is likely inappropriate.


-model “Time*PCLTotal+age+Gender”
-qVars “age” \

Is “age” the only quantitative predictor? Make sure that all the quantitative predictors are declared.

You’re right, PCLTotal should also be included in the qVars line. Time and Gender are both dichotomous variables, so they would not go in the qVars, correct? Here is my model now:

3dLME -prefix final_PCLTotal_LME -jobs 24
-model “Time*PCLTotal+age+Gender”
-qVars “PCLTotal,age”
-ranEff “~1+age”
-SS_type 3
-dataTable @CONN_LME_DataTable.txt

It doesn’t kill the script now, but the model is still failing the model test. Is there anything else I should be changing?

Does ‘PCLTotal’ vary between the two ‘Time’ points? If not, the model

-model “Time*PCLTotal+age+Gender” \

does not make sense, and you can try

-model “Time+PCLTotal+age+Gender” \

it does, PCLTotal should be a different value for each time point. Here is a clip of my data table.
Note: I had changed the variable names in the original post for simplicity’s sake, so that is not the issue. Also there are 121 subjects, but this is just a portion:

Subj Time Gender Racial_Category age2wk PCLTotal_Both Input.File
2 1 1 4 24.8 35 REST/results/firstlevel/allsubs_ICC/BETA_Subject002_Condition001_Measure002_Component001.nii
2 2 1 4 24.8 24 REST/results/firstlevel/allsubs_ICC/BETA_Subject002_Condition003_Measure002_Component001.nii
3 1 1 5 20.9 51 REST/results/firstlevel/allsubs_ICC/BETA_Subject003_Condition001_Measure002_Component001.nii
3 2 1 5 20.9 46 REST/results/firstlevel/allsubs_ICC/BETA_Subject003_Condition003_Measure002_Component001.nii
5 1 0 4 25.6 49 REST/results/firstlevel/allsubs_ICC/BETA_Subject005_Condition001_Measure002_Component001.nii
5 2 0 4 25.6 37 REST/results/firstlevel/allsubs_ICC/BETA_Subject005_Condition003_Measure002_Component001.nii \

I am curious, is the issue that I am trying to use the beta maps from CONN as the input files? I had originally ran this ICC (intrinsic connectivity contrast) in CONN with the intention of completing the LME in AFNI; however, maybe this is messing with the model.

is the issue that I am trying to use the beta maps from CONN as the input files?

No, almost all AFNI programs accept NIfFI files as input.

Try changing the column name “Input.File” to “InputFile”

Ah, sorry I copied this from my excel spreadsheet. In the text file I had changed it to InputFile already, but it still fails the model.

I don’t have the full information about your data structure, so I’d have to keep guessing:

Does ‘age’ vary within subject? If not, specification

-ranEff “~1+age” \

does not make sense. Change it to

-ranEff “~1” \


-ranEff “~1+PCLTotal” \

Sorry for all the back and forth, let me know if there’s anything I can send to make things clearer.
Age does not vary within subjects, so I tried “~1” but it failed. I also tried the “~PCLTotal” which also failed, although PCLTotal is the primary outcome measure I am interested in. Essentially, I have two sessions, one at 2 weeks and one at 6 months, and I want to assess if changes in connectivity between the 2 week and 6 month sessions relates to changes in the PCLTotal score (a self-administered symptom scale). Age and Gender are included as covariates. Does any of that help?