Hi,

I am running Deconvolve on several subjects, and have run into multicollinearity problems with a few of them. In this design, stimuli were presented and the participant had the choice to take or pass on them; certain ones rendered only good or bad outcomes, while others produced both good and bad outcomes or a neutral (none) outcome. There were 7 runs with 149 scans after dropping the first 5, for a total of 1043 runs. The TR was 2s.

The stim_times were further split according to whether they corresponded to the display of the stimulus (dPass, dTake) - which were subdivided according to jitter - and the outcome of the stimulus. A TENT function was used in both cases, and was modeled with a larger time interval in the display conditions in order to accommodate for jitters of different lengths. Each stim_time was amplitude modulated with the magnitude and probability of the good and bad outcomes.

However, halfway through the Deconvolve output, I get the following collinearity error for several columns in one of the .1D files:

*+ WARNING: !! * Columns 418 [out_pass_good#7] and 432 [out_pass_good#21] are nearly collinear!

The content of that .1D file is only five rows, which is relatively short compared to the other .1D files:

400*100,66,60,33
1172*60,33,20,33
1578*60,83,20,83
1940*100,17,60,83
2018*60,33,20,66

I am unsure of what, exactly, is causing this collinearity problem. Is it a result of too many regressors splitting up all of the TRs, and if so, how can this be remedied without collapsing conditions? Would -GOFORIT be a viable option here, even with 9 warnings?


Thank you again for your time,

-Andrew

==================================

3dDeconvolve -input pb04.$subj.r??.scale+tlrc.HEAD \
-polort A -jobs 4 -float -global_times \
-mask full_mask.$subj+tlrc \
-num_stimts 14 \
-stim_times_AM2 1 stimuli/dPass4_n.1D 'TENT(-2,16,10)' \
-stim_label 1 dPass4_n \
-stim_times_AM2 2 stimuli/dPass6_n.1D 'TENT(-2,16,10)' \
-stim_label 2 dPass6_n \
-stim_times_AM2 3 stimuli/dPass8_n.1D 'TENT(-2,16,10)' \
-stim_label 3 dPass8_n \
-stim_times_AM2 4 stimuli/dTake4_n.1D 'TENT(-2,16,10)' \
-stim_label 4 dTake4_n \
-stim_times_AM2 5 stimuli/dTake6_n.1D 'TENT(-2,16,10)' \
-stim_label 5 dTake6_n \
-stim_times_AM2 6 stimuli/dTake8_n.1D 'TENT(-2,16,10)' \
-stim_label 6 dTake8_n \
-stim_times_AM2 7 stimuli/out_take_bad.1D 'TENT(0,12,7)' \
-stim_label 7 out_take_bad \
-stim_times_AM2 8 stimuli/out_take_both.1D 'TENT(0,12,7)' \
-stim_label 8 out_take_both \
-stim_times_AM2 9 stimuli/out_take_good.1D 'TENT(0,12,7)' \
-stim_label 9 out_take_good \
-stim_times_AM2 10 stimuli/out_take_none.1D 'TENT(0,12,7)' \
-stim_label 10 out_take_none \
-stim_times_AM2 11 stimuli/out_pass_bad.1D 'TENT(0,12,7)' \
-stim_label 11 out_pass_bad \
-stim_times_AM2 12 stimuli/out_pass_both.1D 'TENT(0,12,7)' \
-stim_label 12 out_pass_both \
-stim_times_AM2 13 stimuli/out_pass_good.1D 'TENT(0,12,7)' \
-stim_label 13 out_pass_good \
-stim_times_AM2 14 stimuli/out_pass_none.1D 'TENT(0,12,7)' \
-stim_label 14 out_pass_none \
-iresp 1 iresp_dPass4_n.1D.$subj \
-iresp 2 iresp_dPass6_n.1D.$subj \
-iresp 3 iresp_dPass8_n.1D.$subj \
-iresp 4 iresp_dTake4_n.1D.$subj \
-iresp 5 iresp_dTake6_n.1D.$subj \
-iresp 6 iresp_dTake8_n.1D.$subj \
-iresp 7 iresp_out_take_bad.1D.$subj \
-iresp 8 iresp_out_take_both.$subj \
-iresp 9 iresp_out_take_good.$subj \
-iresp 10 iresp_out_take_none.$subj \
-iresp 11 iresp_out_pass_bad.$subj \
-iresp 12 iresp_out_pass_both.$subj \
-iresp 13 iresp_out_pass_good.$subj \
-iresp 14 iresp_out_pass_none.$subj \
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
-fitts fitts.$subj \
-bucket stats.$subj \
-cbucket coeffs.$subj

++ '-stim_times_AM2 1 stimuli/dPass4_n.1D' has 2 auxiliary values per time point
++ '-stim_times_AM2 1 stimuli/dPass4_n.1D' will have 30 regressors
++ '-stim_times_AM2 2 stimuli/dPass6_n.1D' has 2 auxiliary values per time point
++ '-stim_times_AM2 2 stimuli/dPass6_n.1D' will have 30 regressors
++ '-stim_times_AM2 3 stimuli/dPass8_n.1D' has 2 auxiliary values per time point
++ '-stim_times_AM2 3 stimuli/dPass8_n.1D' will have 30 regressors
++ '-stim_times_AM2 4 stimuli/dTake4_n.1D' has 2 auxiliary values per time point
++ '-stim_times_AM2 4 stimuli/dTake4_n.1D' will have 30 regressors
++ '-stim_times_AM2 5 stimuli/dTake6_n.1D' has 2 auxiliary values per time point
++ '-stim_times_AM2 5 stimuli/dTake6_n.1D' will have 30 regressors
++ '-stim_times_AM2 6 stimuli/dTake8_n.1D' has 2 auxiliary values per time point
++ '-stim_times_AM2 6 stimuli/dTake8_n.1D' will have 30 regressors
++ '-stim_times_AM2 7 stimuli/out_take_bad.1D' has 4 auxiliary values per time point
++ '-stim_times_AM2 7 stimuli/out_take_bad.1D' will have 35 regressors
++ '-stim_times_AM2 8 stimuli/out_take_both.1D' has 4 auxiliary values per time point
++ '-stim_times_AM2 8 stimuli/out_take_both.1D' will have 35 regressors
++ '-stim_times_AM2 9 stimuli/out_take_good.1D' has 4 auxiliary values per time point
++ '-stim_times_AM2 9 stimuli/out_take_good.1D' will have 35 regressors
++ '-stim_times_AM2 10 stimuli/out_take_none.1D' has 4 auxiliary values per time point
++ '-stim_times_AM2 10 stimuli/out_take_none.1D' will have 35 regressors
++ '-stim_times_AM2 11 stimuli/out_pass_bad.1D' has 4 auxiliary values per time point
++ '-stim_times_AM2 11 stimuli/out_pass_bad.1D' will have 35 regressors
++ '-stim_times_AM2 12 stimuli/out_pass_both.1D' has 4 auxiliary values per time point
++ '-stim_times_AM2 12 stimuli/out_pass_both.1D' will have 35 regressors
++ '-stim_times_AM2 13 stimuli/out_pass_good.1D' has 4 auxiliary values per time point
++ '-stim_times_AM2 13 stimuli/out_pass_good.1D' will have 35 regressors
++ '-stim_times_AM2 14 stimuli/out_pass_none.1D' has 4 auxiliary values per time point
++ '-stim_times_AM2 14 stimuli/out_pass_none.1D' will have 35 regressors
++ 3dDeconvolve: AFNI version=AFNI_2008_07_18_1710 (Jan 5 2009) [64-bit]
++ Authored by: B. Douglas Ward, et al.
++ current memory malloc-ated = 226136 bytes (about 226 thousand)
++ loading dataset pb04.JULY11_2007A.r01.scale+tlrc.HEAD pb04.JULY11_2007A.r02.scale+tlrc.HEAD pb04.JULY11_2007A.r03.scale+tlrc.HEAD pb04.JULY11_2007A.r04.scale+tlrc.HEAD pb04.JULY11_2007A.r05.scale+tlrc.HEAD pb04.JULY11_2007A.r06.scale+tlrc.HEAD pb04.JULY11_2007A.r07.scale+tlrc.HEAD
++ current memory malloc-ated = 1095474832 bytes (about 1.1 billion)
++ Auto-catenated datasets start at: 0 144 288 432 576 720 864
++ Imaging duration=288.0 s; Automatic polort=2
++ -stim_times using TR=2 s for stimulus timing conversion
++ -stim_times using TR=2 s for any -iresp output datasets
++ [you can alter the -iresp TR via the -TR_times option]
++ ** NOTE ** Will guess GLOBAL times if 1 time per line; LOCAL otherwise
++ ** GUESSED ** -stim_times_AM2 1 using GLOBAL times
*+ WARNING: '-stim_times_AM2 1' (GLOBAL) has 1 times outside range 0 .. 2014 [PSFB syndrome]
*+ WARNING: dataset TR being used is 2 s
++ '-stim_times_AM2 1' average amplitude#1=32.1619
++ '-stim_times_AM2 1' average amplitude#2=23.5809
++ ** GUESSED ** -stim_times_AM2 2 using GLOBAL times
*+ WARNING: '-stim_times_AM2 2' (GLOBAL) has 1 times outside range 0 .. 2014 [PSFB syndrome]
*+ WARNING: dataset TR being used is 2 s
++ '-stim_times_AM2 2' average amplitude#1=34.2083
++ '-stim_times_AM2 2' average amplitude#2=19.2667
++ ** GUESSED ** -stim_times_AM2 3 using GLOBAL times
++ '-stim_times_AM2 3' average amplitude#1=32.4857
++ '-stim_times_AM2 3' average amplitude#2=31.5
++ ** GUESSED ** -stim_times_AM2 4 using GLOBAL times
++ '-stim_times_AM2 4' average amplitude#1=22.13
++ '-stim_times_AM2 4' average amplitude#2=47.58
++ ** GUESSED ** -stim_times_AM2 5 using GLOBAL times
++ '-stim_times_AM2 5' average amplitude#1=29.6824
++ '-stim_times_AM2 5' average amplitude#2=34.7647
++ ** GUESSED ** -stim_times_AM2 6 using GLOBAL times
*+ WARNING: '-stim_times_AM2 6' (GLOBAL) has 1 times outside range 0 .. 2014 [PSFB syndrome]
*+ WARNING: dataset TR being used is 2 s
++ '-stim_times_AM2 6' average amplitude#1=20.3704
++ '-stim_times_AM2 6' average amplitude#2=36.0815
++ ** GUESSED ** -stim_times_AM2 7 using GLOBAL times
*+ WARNING: '-stim_times_AM2 7' (GLOBAL) has 1 times outside range 0 .. 2014 [PSFB syndrome]
*+ WARNING: dataset TR being used is 2 s
++ '-stim_times_AM2 7' average amplitude#1=49.0909
++ '-stim_times_AM2 7' average amplitude#2=67.9091
++ '-stim_times_AM2 7' average amplitude#3=89.0909
++ '-stim_times_AM2 7' average amplitude#4=43.7273
++ ** GUESSED ** -stim_times_AM2 8 using GLOBAL times
++ '-stim_times_AM2 8' average amplitude#1=57.8947
++ '-stim_times_AM2 8' average amplitude#2=55.8947
++ '-stim_times_AM2 8' average amplitude#3=60
++ '-stim_times_AM2 8' average amplitude#4=63.6316
++ ** GUESSED ** -stim_times_AM2 9 using GLOBAL times
++ '-stim_times_AM2 9' average amplitude#1=42.2222
++ '-stim_times_AM2 9' average amplitude#2=35.1667
++ '-stim_times_AM2 9' average amplitude#3=66.6667
++ '-stim_times_AM2 9' average amplitude#4=64.4444
++ ** GUESSED ** -stim_times_AM2 10 using GLOBAL times
++ '-stim_times_AM2 10' average amplitude#1=58.75
++ '-stim_times_AM2 10' average amplitude#2=30.375
++ '-stim_times_AM2 10' average amplitude#3=82.5
++ '-stim_times_AM2 10' average amplitude#4=45.625
++ ** GUESSED ** -stim_times_AM2 11 using GLOBAL times
*+ WARNING: '-stim_times_AM2 11' (GLOBAL) has 2 times outside range 0 .. 2014 [PSFB syndrome]
*+ WARNING: dataset TR being used is 2 s
++ '-stim_times_AM2 11' average amplitude#1=65.1852
++ '-stim_times_AM2 11' average amplitude#2=61.5185
++ '-stim_times_AM2 11' average amplitude#3=55.5556
++ '-stim_times_AM2 11' average amplitude#4=35.7037
++ ** GUESSED ** -stim_times_AM2 12 using GLOBAL times
++ '-stim_times_AM2 12' average amplitude#1=55.3846
++ '-stim_times_AM2 12' average amplitude#2=62.6154
++ '-stim_times_AM2 12' average amplitude#3=61.5385
++ '-stim_times_AM2 12' average amplitude#4=53.6154
++ ** GUESSED ** -stim_times_AM2 13 using GLOBAL times
*+ WARNING: '-stim_times_AM2 13' (GLOBAL) has 1 times outside range 0 .. 2014 [PSFB syndrome]
*+ WARNING: dataset TR being used is 2 s
++ '-stim_times_AM2 13' average amplitude#1=80
++ '-stim_times_AM2 13' average amplitude#2=49.75
++ '-stim_times_AM2 13' average amplitude#3=40
++ '-stim_times_AM2 13' average amplitude#4=58
++ ** GUESSED ** -stim_times_AM2 14 using GLOBAL times
++ '-stim_times_AM2 14' average amplitude#1=64.2857
++ '-stim_times_AM2 14' average amplitude#2=35.7143
++ '-stim_times_AM2 14' average amplitude#3=67.1429
++ '-stim_times_AM2 14' average amplitude#4=33.3571
++ total shared memory needed = 2156766020 bytes (about 2.2 billion)
++ current memory malloc-ated = 1097701643 bytes (about 1.1 billion)
++ mmap() memory allocated: 2156766020 bytes (about 2.2 billion)
++ Memory required for output bricks = 2156766020 bytes (about 2.2 billion)
++ Wrote matrix image to file X.jpg
++ Wrote matrix values to file X.xmat.1D
++ ========= Things you can do with the matrix file =========
++ (a) Linear regression with ARMA(1,1) modeling of serial correlation:

3dREMLfit -matrix X.xmat.1D \
-input "pb04.JULY11_2007A.r01.scale+tlrc.HEAD pb04.JULY11_2007A.r02.scale+tlrc.HEAD pb04.JULY11_2007A.r03.scale+tlrc.HEAD pb04.JULY11_2007A.r04.scale+tlrc.HEAD pb04.JULY11_2007A.r05.scale+tlrc.HEAD pb04.JULY11_2007A.r06.scale+tlrc.HEAD pb04.JULY11_2007A.r07.scale+tlrc.HEAD" \
-mask full_mask.JULY11_2007A+tlrc -Rbeta coeffs.JULY11_2007A_REML \
-fout -tout -Rbuck stats.JULY11_2007A_REML -Rvar stats.JULY11_2007A_REMLvar \
-Rfitts fitts.JULY11_2007A_REML -verb

++ N.B.: 3dREMLfit command above written to file stats.REML_cmd
++ (b) Visualization/analysis of the matrix via ExamineXmat.R
++ (c) Synthesis of sub-model datasets using 3dSynthesize
++ ==========================================================
*+ WARNING: -------------------------------------------------
*+ WARNING: Problems with the X matrix columns, listed below:
*+ WARNING: !! * Columns 418 [out_pass_good#7] and 432 [out_pass_good#21] are nearly collinear!
*+ WARNING: !! * Columns 419 [out_pass_good#8] and 433 [out_pass_good#22] are nearly collinear!
*+ WARNING: !! * Columns 420 [out_pass_good#9] and 434 [out_pass_good#23] are nearly collinear!
*+ WARNING: !! * Columns 421 [out_pass_good#10] and 435 [out_pass_good#24] are nearly collinear!
*+ WARNING: !! * Columns 422 [out_pass_good#11] and 436 [out_pass_good#25] are nearly collinear!
*+ WARNING: !! * Columns 423 [out_pass_good#12] and 437 [out_pass_good#26] are nearly collinear!
*+ WARNING: !! * Columns 424 [out_pass_good#13] and 438 [out_pass_good#27] are nearly collinear!
*+ WARNING: -------------------------------------------------
++ ----- Signal+Baseline matrix condition [X] (1008x481): 31.1747 ++ GOOD ++
*+ WARNING: !! in Signal+Baseline matrix:
* Largest singular value=3.33597
* 7 singular values are less than cutoff=3.33597e-07
* Implies strong collinearity in the matrix columns!
++ Signal+Baseline matrix singular values:
-1.30965e-15 -6.0593e-16 -5.22383e-16 -3.20069e-16 -2.73e-16
3.82242e-16 1.16682e-15 0.00343256 0.0040513 0.00442149
0.00484106 0.0050637 0.00518096 0.00540671 0.00546658
0.0130069 0.0135433 0.0147499 0.0154943 0.0160587
0.0164869 0.0173691 0.0182338 0.0188835 0.0220071
0.0258857 0.0267655 0.0273647 0.0283364 0.0300164
0.031956 0.0331497 0.0341278 0.0624032 0.0678185
0.0680967 0.0771172 0.0831177 0.0890425 0.094119
0.095158 0.0969989 0.100028 0.101116 0.104891
0.108271 0.109697 0.115279 0.115659 0.119893
0.126743 0.129506 0.133111 0.135212 0.141185
0.143193 0.149813 0.151992 0.153829 0.155906
0.158381 0.160161 0.164167 0.16613 0.168686
0.172137 0.173639 0.178487 0.181386 0.185257
0.189574 0.196161 0.202316 0.20341 0.209972
0.213132 0.218062 0.222043 0.224132 0.228968
0.232923 0.237452 0.242189 0.245939 0.250476
0.259063 0.260695 0.266362 0.26793 0.271012
0.272517 0.277512 0.27921 0.283365 0.289148
0.290873 0.293651 0.298147 0.301748 0.307696
0.311988 0.315932 0.322857 0.328977 0.332852
0.334521 0.338594 0.344232 0.348499 0.352837
0.355346 0.362623 0.366437 0.367871 0.371534
0.373986 0.376578 0.383145 0.385367 0.387155
0.392868 0.393781 0.396168 0.400658 0.403185
0.403928 0.405586 0.410433 0.410786 0.414585
0.419521 0.423692 0.424653 0.426941 0.428536
0.430449 0.4333 0.438274 0.4434 0.447201
0.450992 0.452484 0.456194 0.460126 0.465671
0.469408 0.475321 0.476578 0.482429 0.483187
0.487696 0.489998 0.494808 0.498753 0.502843
0.506418 0.510254 0.516687 0.517913 0.522093
0.529497 0.535321 0.536623 0.542164 0.54539
0.554495 0.556561 0.558866 0.562933 0.566763
0.568772 0.570525 0.573439 0.577305 0.583653
0.585357 0.588116 0.59248 0.595802 0.59873
0.602393 0.606514 0.612054 0.616612 0.623177
0.629891 0.635235 0.645094 0.646247 0.649305
0.650716 0.656358 0.661852 0.668166 0.670418
0.677511 0.68203 0.685815 0.688473 0.695433
0.701894 0.705772 0.711213 0.71619 0.719572
0.722295 0.725881 0.728394 0.73105 0.734018
0.748432 0.750778 0.753519 0.756072 0.76235
0.766526 0.767857 0.774605 0.776237 0.783358
0.785683 0.794409 0.798513 0.800713 0.801736
0.806906 0.814124 0.814955 0.818773 0.82779
0.829313 0.830415 0.833459 0.845926 0.851913
0.853373 0.856503 0.863409 0.868627 0.87433
0.878095 0.880699 0.882908 0.88973 0.895984
0.898634 0.903598 0.905314 0.907181 0.914411
0.918592 0.922513 0.925573 0.931685 0.937071
0.938835 0.941597 0.948658 0.949641 0.953578
0.958532 0.960999 0.966259 0.971194 0.974625
0.983155 0.986469 0.987726 0.994431 0.998368
0.999842 1.00118 1.0046 1.0094 1.01143
1.01595 1.02129 1.02482 1.02704 1.0324
1.03527 1.04087 1.04269 1.04694 1.05112
1.05598 1.05683 1.06359 1.06462 1.07498
1.07542 1.08033 1.08413 1.08518 1.08879
1.09965 1.10292 1.10788 1.1108 1.11657
1.12126 1.13415 1.14188 1.14793 1.15246
1.15837 1.16329 1.16433 1.17211 1.17333
1.17637 1.17935 1.1905 1.19268 1.1965
1.20144 1.20835 1.21343 1.21599 1.22132
1.22192 1.22646 1.2336 1.24331 1.2447
1.24958 1.25423 1.26128 1.26607 1.27553
1.2796 1.28545 1.28972 1.29704 1.29952
1.30701 1.31152 1.32141 1.32442 1.33015
1.34124 1.34288 1.35082 1.36168 1.37033
1.37561 1.37992 1.38508 1.3937 1.40187
1.40647 1.41721 1.42736 1.43557 1.44419
1.44766 1.45067 1.45655 1.46906 1.4721
1.47701 1.48005 1.493 1.50245 1.50571
1.5127 1.52476 1.52766 1.53009 1.5342
1.54086 1.55523 1.55986 1.5653 1.58077
1.58571 1.59082 1.60741 1.61514 1.62263
1.64238 1.64425 1.65539 1.65799 1.66336
1.66918 1.68006 1.6917 1.69673 1.7017
1.72214 1.72602 1.73453 1.74271 1.75377
1.75458 1.78322 1.78601 1.79832 1.80905
1.82457 1.83581 1.85226 1.86266 1.877
1.88986 1.90439 1.91328 1.916 1.94504
1.95252 1.96244 1.96992 1.98211 1.98835
1.99348 2.00193 2.00896 2.01753 2.02703
2.03082 2.03484 2.0426 2.05795 2.06573
2.06731 2.07942 2.09243 2.09943 2.12436
2.12944 2.14342 2.14604 2.15628 2.17032
2.17989 2.18977 2.21557 2.2249 2.23135
2.24752 2.25826 2.27124 2.28062 2.29116
2.31723 2.32738 2.34661 2.34777 2.35324
2.36418 2.37914 2.39254 2.40721 2.4276
2.4358 2.44704 2.45914 2.47484 2.48205
2.49347 2.51333 2.52852 2.54385 2.55846
2.58357 2.6093 2.63078 2.63108 2.63655
2.65665 2.67647 2.70418 2.70719 2.71662
2.76433 2.78207 2.87227 2.88924 2.93124
3.33597
++ ----- Signal-only matrix condition [X] (1008x460): 25.9282 ++ GOOD ++
*+ WARNING: !! in Signal-only matrix:
* Largest singular value=2.8483
* 7 singular values are less than cutoff=2.8483e-07
* Implies strong collinearity in the matrix columns!
++ Signal-only matrix singular values:
1.74711e-16 7.14475e-16 1.02916e-15 1.84881e-15 1.85599e-15
2.7267e-15 4.87604e-15 0.00423682 0.00463857 0.00496207
0.00507582 0.00525107 0.00540207 0.0054191 0.00549718
0.0143387 0.0151349 0.0152998 0.0166256 0.0169043
0.0181925 0.0182758 0.0193279 0.0229514 0.0266906
0.0276899 0.0286314 0.0297801 0.0309832 0.0329021
0.0333539 0.0348602 0.0657842 0.0713094 0.0735291
0.0881629 0.0929134 0.0959237 0.0966785 0.0993413
0.100978 0.102295 0.105186 0.109311 0.110838
0.117983 0.122654 0.125299 0.131475 0.136065
0.140064 0.145005 0.151124 0.151687 0.153111
0.15862 0.15884 0.162686 0.165367 0.166371
0.168905 0.16908 0.174564 0.178066 0.18476
0.190685 0.196816 0.202376 0.205076 0.213168
0.217143 0.221421 0.226436 0.2277 0.232021
0.238761 0.244203 0.251993 0.254557 0.256383
0.264819 0.268236 0.270239 0.270451 0.274687
0.275636 0.2854 0.290543 0.293565 0.299832
0.303066 0.3071 0.314355 0.324971 0.325506
0.333589 0.334622 0.338162 0.340522 0.343839
0.353535 0.359672 0.36438 0.370206 0.370933
0.373678 0.374206 0.378352 0.381348 0.388887
0.392707 0.394323 0.397937 0.402021 0.402738
0.40366 0.406325 0.409585 0.412764 0.414007
0.414687 0.421353 0.42506 0.425693 0.428203
0.430619 0.43153 0.437823 0.442284 0.446444
0.450888 0.451584 0.456006 0.459872 0.462377
0.468225 0.471252 0.478626 0.481872 0.484251
0.488136 0.489751 0.495283 0.498878 0.505502
0.511537 0.516216 0.524607 0.526883 0.532191
0.533703 0.53924 0.539667 0.544709 0.549664
0.550577 0.55656 0.558045 0.567193 0.56746
0.569129 0.573572 0.57635 0.583491 0.587746
0.589681 0.592712 0.593339 0.60053 0.60962
0.610085 0.615438 0.622782 0.625941 0.629783
0.636225 0.645203 0.64799 0.649747 0.653086
0.656713 0.662438 0.675209 0.677017 0.680203
0.685449 0.687451 0.691943 0.699998 0.704563
0.713261 0.719518 0.722862 0.723765 0.725859
0.726438 0.733894 0.740713 0.742603 0.743248
0.750524 0.756143 0.759921 0.761791 0.76789
0.772413 0.774667 0.77864 0.783376 0.798061
0.804625 0.805085 0.807177 0.810057 0.814889
0.815954 0.824382 0.826479 0.831708 0.839447
0.843926 0.851735 0.852041 0.852638 0.859896
0.861299 0.86574 0.873227 0.879866 0.886694
0.88859 0.894575 0.896089 0.903345 0.908175
0.910934 0.914112 0.91521 0.917611 0.92457
0.928774 0.930578 0.938742 0.943087 0.944781
0.946302 0.948993 0.95341 0.955626 0.959958
0.963797 0.970942 0.978436 0.986572 0.987442
0.99167 0.994414 0.996231 0.997136 0.999244
1.00233 1.00733 1.01094 1.01136 1.01879
1.02073 1.02447 1.02916 1.03415 1.03725
1.03997 1.0414 1.04168 1.0458 1.05197
1.05599 1.06851 1.07169 1.07251 1.07895
1.0802 1.08438 1.08784 1.09128 1.10048
1.10279 1.10727 1.11272 1.12424 1.13446
1.14195 1.14281 1.15516 1.15678 1.15859
1.16332 1.16636 1.17535 1.18312 1.18664
1.18899 1.19161 1.19422 1.20121 1.20615
1.21058 1.21998 1.22529 1.22859 1.23602
1.24354 1.24395 1.2549 1.26026 1.26056
1.26388 1.27429 1.28129 1.28928 1.2908
1.29132 1.31029 1.31336 1.32091 1.32475
1.32934 1.33356 1.34118 1.35566 1.35859
1.37623 1.3799 1.38392 1.39093 1.39466
1.40828 1.414 1.42201 1.43416 1.43689
1.44255 1.44954 1.46145 1.46841 1.47523
1.47617 1.48762 1.49581 1.49868 1.50658
1.51703 1.51725 1.52318 1.53363 1.54193
1.54698 1.55135 1.56635 1.57171 1.5905
1.60203 1.61115 1.62258 1.62812 1.64009
1.65243 1.65444 1.6577 1.66911 1.68336
1.69376 1.69748 1.70833 1.71571 1.72351
1.72935 1.75515 1.7599 1.76468 1.76782
1.78897 1.82036 1.83027 1.83102 1.84596
1.85481 1.86773 1.8789 1.897 1.91717
1.92874 1.9397 1.94243 1.95896 1.96393
1.97805 1.98417 1.9949 1.99529 1.99795
1.99963 2.00726 2.01555 2.02882 2.0366
2.03899 2.05285 2.06018 2.07105 2.07786
2.09854 2.11438 2.12269 2.14195 2.14387
2.1547 2.16376 2.18024 2.1949 2.22146
2.2271 2.25141 2.25574 2.26496 2.28201
2.28927 2.30564 2.32265 2.34349 2.34404
2.34988 2.35607 2.37186 2.40164 2.41101
2.41719 2.42994 2.4343 2.43755 2.45434
2.47648 2.49254 2.49707 2.52013 2.53834
2.57946 2.59665 2.61712 2.6261 2.62961
2.63386 2.65516 2.65605 2.67071 2.70506
2.70737 2.72483 2.77882 2.79475 2.8483
++ ----- Baseline-only matrix condition [X] (1008x21): 1 ++ VERY GOOD ++
++ ----- polort-only matrix condition [X] (1008x21): 1 ++ VERY GOOD ++
++ +++++ Matrix inverse average error = 6.05115e-05 ++ GOOD ++
++ Matrix setup time = 606.12 s
** ERROR: !! 3dDeconvolve: Can't run past 9 matrix warnings without '-GOFORIT 9'
** ERROR: !! See file 3dDeconvolve.err for all WARNING and ERROR messages !!
** ERROR: !! Be sure you understand what you are doing before using -GOFORIT !!
** ERROR: !! If in doubt, consult with someone or with the AFNI message board !!
** FATAL ERROR: !! 3dDeconvolve (regretfully) shuts itself down !!