afni.nimh.nih.gov

Hello,

I'm struggling to get my 3dLME to run. I keep getting stuck at the model test, with the suggestion that there's inappropriate coding in -model, -qVars, or -gltCode. I've combed through the message board and tried various things other people have had problems with but I can't seem to come to any resolution. Any insights as to what I may be doing wrong?

*I know the 3dLME command looks a little crazy with all the posthoc tests, but we're interested in the specific results of the various contrasts as they pertain to the 3 covariates (PCL_2wk, ETV_2wk, and PEDQ_2WK_TOTAL) and not just "controlling for their effects".

Thanks in advance!!


AFNI version:

Precompiled binary linux_openmp_64: Jul  3 2019 (Version AFNI_19.2.01 'Claudius')

3dLME -prefix unc_clock_ETC_PEDQ -jobs 24 \
        -model "Valence*Predict+PCL_2wk+ETV_2wk+PEDQ_2WK_TOTAL+Age+Gender" \
        -qVars "Age,ETV_2wk,PCL_2wk,PEDQ_2WK_TOTAL" \
       -qVarsCenters "32.47, 9.01, 23.77, 28.74" \
        -ranEff "~1+Age" \
        -SS_type 3 \
        -num_glt 28 \
        -gltLabel 1 'neg-neu' -gltCode 1 'Valence : 1*neg -1*neu' \
        -gltLabel 2 'u-p' -gltCode 2 'Predict : 1*u -1*p' \
        -gltLabel 3 'Uneg-Uneu' -gltCode 3 'Valence : 1*neg -1*neu Predict : 1*u' \
        -gltLabel 4 'Pneg-Pneu' -gltCode 4 'Valence : 1*neg -1*neu Predict : 1*p' \
        -gltLabel 5 'Uneg-Pneg' -gltCode 5 'Predict : 1*u -1*p Valence : 1*neg' \
        -gltLabel 6 'Uneu-Pneu' -gltCode 6 'Predict : 1*u -1*p Valence : 1*neu' \
        -gltLabel 7 'Uneg-Pneu' -gltCode 7 'Predict : 1*u -1*p Valence : 1*neg -1*neu' \
        -gltLabel 8 'neg-neu_PCL_2wk' -gltCode 8 'Valence : 1*neg -1*neu PCL_2wk :' \
        -gltLabel 9 'u-p_PCL_2wk' -gltCode 9 'Predict : 1*u -1*p PCL_2wk :' \
        -gltLabel 10 'Uneg-Uneu_PCL_2wk' -gltCode 10 'Valence : 1*neg -1*neu Predict : 1*u PCL_2wk :' \
        -gltLabel 11 'Pneg-Pneu_PCL_2wk' -gltCode 11 'Valence : 1*neg -1*neu Predict : 1*p PCL_2wk :' \
        -gltLabel 12 'Uneg-Pneg_PCL_2wk' -gltCode 12 'Predict : 1*u -1*p Valence : 1*neg PCL_2wk :' \
        -gltLabel 13 'Uneu-Pneu_PCL_2wk' -gltCode 13 'Predict : 1*u -1*p Valence : 1*neu PCL_2wk :' \
        -gltLabel 14 'Uneg-Pneu_PCL_2wk' -gltCode 14 'Predict : 1*u -1*p Valence : 1*neg -1*neu PCL_2wk :' \
        -gltLabel 15 'neg-neu_ETV_2wk' -gltCode 15 'Valence : 1*neg -1*neu ETV_2wk :' \
        -gltLabel 16 'u-p_ETV_2wk' -gltCode 16 'Predict : 1*u -1*p ETV_2wk :' \
        -gltLabel 17 'Uneg-Uneu_ETV_2wk' -gltCode 17 'Valence : 1*neg -1*neu Predict : 1*u ETV_2wk :' \
        -gltLabel 18 'Pneg-Pneu_ETV_2wk' -gltCode 18 'Valence : 1*neg -1*neu Predict : 1*p ETV_2wk :' \
        -gltLabel 19 'Uneg-Pneg_ETV_2wk' -gltCode 19 'Predict : 1*u -1*p Valence : 1*neg ETV_2wk :' \
        -gltLabel 20 'Uneu-Pneu_ETV_2wk' -gltCode 20 'Predict : 1*u -1*p Valence : 1*neu ETV_2wk :' \
        -gltLabel 21 'Uneg-Pneu_ETV_2wk' -gltCode 21 'Predict : 1*u -1*p Valence : 1*neg -1*neu ETV_2wk :' \
        -gltLabel 22 'neg-neu_PEDQ_2WK_TOTAL' -gltCode 22 'Valence : 1*neg -1*neu PEDQ_2WK_TOTAL :' \
        -gltLabel 23 'u-p_PEDQ_2WK_TOTAL' -gltCode 23 'Predict : 1*u -1*p PEDQ_2WK_TOTAL :' \
        -gltLabel 24 'Uneg-Uneu_PEDQ_2WK_TOTAL' -gltCode 24 'Valence : 1*neg -1*neu Predict : 1*u PEDQ_2WK_TOTAL :' \
        -gltLabel 25 'Pneg-Pneu_PEDQ_2WK_TOTAL' -gltCode 25 'Valence : 1*neg -1*neu Predict : 1*p PEDQ_2WK_TOTAL :' \
        -gltLabel 26 'Uneg-Pneg_PEDQ_2WK_TOTAL' -gltCode 26 'Predict : 1*u -1*p Valence : 1*neg PEDQ_2WK_TOTAL :' \
        -gltLabel 27 'Uneu-Pneu_PEDQ_2WK_TOTAL' -gltCode 27 'Predict : 1*u -1*p Valence : 1*neu PEDQ_2WK_TOTAL :' \
        -gltLabel 28 'Uneg-Pneu_PEDQ_2WK_TOTAL' -gltCode 28 'Predict : 1*u -1*p Valence : 1*neg -1*neu PEDQ_2WK_TOTAL :' \
       -num_glf 2 \
       -glfLabel 1 'cond_valence' -glfCode 1 'Valence : 1*neg & 1*neu' \
       -glfLabel 2 'cond_pred' -glfCode 2 'Predict : 1*u & 1*p' \
        -dataTable @LME_table_unc_trl_ETV_PEDQ_PCL.txt

Here's a preview of my dataTable "LME_table_unc_trl_ETV_PEDQ_PCL.txt", in total there are 65 subjects with complete data. I've included 3 subjects below.

Subj    Age     Gender  ETV_2wk PCL_2wk PEDQ_2WK_TOTAL  Valence Predict InputFile \
s270    25      0       4       41      51      neg     p       /raid-06/LS/Data/istar/270_day1/unc_fullN_SSWarp_trl_results/stats.270+tlrc[61] \
s270    25      0       4       42      52      neu     p       /raid-06/LS/Data/istar/270_day1/unc_fullN_SSWarp_trl_results/stats.270+tlrc[64] \
s270    25      0       4       43      53      neg     u       /raid-06/LS/Data/istar/270_day1/unc_fullN_SSWarp_trl_results/stats.270+tlrc[67] \
s270    25      0       4       44      54      neu     u       /raid-06/LS/Data/istar/270_day1/unc_fullN_SSWarp_trl_results/stats.270+tlrc[70] \
s283    24.6    1       0       36      40      neg     p       /raid-06/LS/Data/istar/283_day1/unc_fullN_SSWarp_trl_results/stats.283+tlrc[61] \
s283    24.6    1       0       37      41      neu     p       /raid-06/LS/Data/istar/283_day1/unc_fullN_SSWarp_trl_results/stats.283+tlrc[64] \
s283    24.6    1       0       38      42      neg     u       /raid-06/LS/Data/istar/283_day1/unc_fullN_SSWarp_trl_results/stats.283+tlrc[67] \
s283    24.6    1       0       39      43      neu     u       /raid-06/LS/Data/istar/283_day1/unc_fullN_SSWarp_trl_results/stats.283+tlrc[70] \
s294    39.4    0       10      40      45      neg     p       /raid-06/LS/Data/istar/294_day1/unc_fullN_SSWarp_trl_results/stats.294+tlrc[61] \
s294    39.4    0       10      40      45      neu     p       /raid-06/LS/Data/istar/294_day1/unc_fullN_SSWarp_trl_results/stats.294+tlrc[64] \
s294    39.4    0       10      40      45      neg     u       /raid-06/LS/Data/istar/294_day1/unc_fullN_SSWarp_trl_results/stats.294+tlrc[67] \
s294    39.4    0       10      40      45      neu     u       /raid-06/LS/Data/istar/294_day1/unc_fullN_SSWarp_trl_results/stats.294+tlrc[70] \
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