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Dear AFNI users-
We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:
Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.
The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.
Sincerely, AFNI HQ
We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:
https://discuss.afni.nimh.nih.gov
Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.
The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.
Sincerely, AFNI HQ
| May 20, 2022 01:11PM | |
Registered: 2 years ago Posts: 19 |
Hi,
I am attempting to conduct a group-level analysis using 3dMVM. The design has 1 between-subject factor (group) and 1 within-subject factor (session). I would like to use 3dMVM because I have an unequal number of subjects in each group. I have followed the example set-up from the 3dMVM help documentation. When I attempt to run 3dMVM, it looks like the data structure and design is being read properly, but I then get the "Model test failed!" message. I can't figure out what the issue is. I think my script looks good based on my reading of the help docs, and I have the required R packages installed. I'm getting the same error when attempting to run this on Mac and on Ubuntu machines. I also tried running the 3dMVM validator but it returned various program dependency errors I could not resolve.
Any assistance would be much appreciated. Please see below for the 3dMVM script and output log.
Thanks!
Michael
3dMVM -prefix GroupAnalysis -jobs 9 \
-mask mask_union_group+tlrc \
-bsVars Group \
-wsVars Session \
-num_glt 7 \
-gltLabel 1 TrainingVsPlacebo -gltCode 1 'Group : 1*Training -1*Placebo' \
-gltLabel 2 PreVsPost -gltCode 2 'Session : 1*Pre -1*Post' \
-gltLabel 3 GroupXSession -gltCode 3 'Group : 1*Training -1*Placebo Session : 1*Pre -1*Post' \
-gltLabel 4 Training_PreVsPost -gltCode 4 'Session : 1*Pre -1*Post Group : 1*Training' \
-gltLabel 5 Placebo_PreVsPost -gltCode 5 'Session : 1*Pre -1*Post Group : 1*Placebo' \
-gltLabel 6 Pre_TrainingVsPlacebo -gltCode 6 'Group : 1*Training -1*Placebo Session : 1*Pre' \
-gltLabel 7 Post_TrainingVsPlacebo -gltCode 7 'Group : 1*Training -1*Placebo Session : 1*Post' \
-dataTable \
Subj Session Group InputFile \
000156_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000156_1.results/stats.000156_1_REML+tlrc'[19]' \
000156_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000156_3.results/stats.000156_3_REML+tlrc'[19]' \
000178_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000178_1.results/stats.000178_1_REML+tlrc'[19]' \
000178_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000178_3.results/stats.000178_3_REML+tlrc'[19]' \
000573_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000573_1.results/stats.000573_1_REML+tlrc'[19]' \
000573_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000573_3.results/stats.000573_3_REML+tlrc'[19]' \
000955_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000955_1.results/stats.000955_1_REML+tlrc'[19]' \
000955_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000955_3.results/stats.000955_3_REML+tlrc'[19]' \
001156_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001156_1.results/stats.001156_1_REML+tlrc'[19]' \
001156_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001156_3.results/stats.001156_3_REML+tlrc'[19]' \
001271_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001271_1.results/stats.001271_1_REML+tlrc'[19]' \
001271_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001271_3.results/stats.001271_3_REML+tlrc'[19]' \
001372_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001372_1.results/stats.001372_1_REML+tlrc'[19]' \
001372_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001372_3.results/stats.001372_3_REML+tlrc'[19]' \
002665_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/002665_1.results/stats.002665_1_REML+tlrc'[19]' \
002665_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/002665_3.results/stats.002665_3_REML+tlrc'[19]' \
004386_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004386_1.results/stats.004386_1_REML+tlrc'[19]' \
004386_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004386_3.results/stats.004386_3_REML+tlrc'[19]' \
004397_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004397_1.results/stats.004397_1_REML+tlrc'[19]' \
004397_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004397_3.results/stats.004397_3_REML+tlrc'[19]' \
005703_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005703_1.results/stats.005703_1_REML+tlrc'[19]' \
005703_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005703_3.results/stats.005703_3_REML+tlrc'[19]' \
005760_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005760_1.results/stats.005760_1_REML+tlrc'[19]' \
005760_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005760_3.results/stats.005760_3_REML+tlrc'[19]' \
005782_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005782_1.results/stats.005782_1_REML+tlrc'[19]' \
005782_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005782_3.results/stats.005782_3_REML+tlrc'[19]' \
005894_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005894_1.results/stats.005894_1_REML+tlrc'[19]' \
005894_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005894_3.results/stats.005894_3_REML+tlrc'[19]' \
005927_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005927_1.results/stats.005927_1_REML+tlrc'[19]' \
005927_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005927_3.results/stats.005927_3_REML+tlrc'[19]' \
005973_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005973_1.results/stats.005973_1_REML+tlrc'[19]' \
005973_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005973_3.results/stats.005973_3_REML+tlrc'[19]'
Output log:
3dMVM -prefix GroupAnalysis -jobs 9 -mask mask_union_group+tlrc -bsVars Group -wsVars Session -num_glt 7 -gltLabel 1 TrainingVsPlacebo -gltCode 1 Group : 1*Training -1*Placebo -gltLabel 2 PreVsPost -gltCode 2 Session : 1*Pre -1*Post -gltLabel 3 GroupXSession -gltCode 3 Group : 1*Training -1*Placebo Session : 1*Pre -1*Post -gltLabel 4 Training_PreVsPost -gltCode 4 Session : 1*Pre -1*Post Group : 1*Training -gltLabel 5 Placebo_PreVsPost -gltCode 5 Session : 1*Pre -1*Post Group : 1*Placebo -gltLabel 6 Pre_TrainingVsPlacebo -gltCode 6 Group : 1*Training -1*Placebo Session : 1*Pre -gltLabel 7 Post_TrainingVsPlacebo -gltCode 7 Group : 1*Training -1*Placebo Session : 1*Post -dataTable Subj Session Group InputFile 000156_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000156_1.results/stats.000156_1_REML+tlrc[19] 000156_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000156_3.results/stats.000156_3_REML+tlrc[19] 000178_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000178_1.results/stats.000178_1_REML+tlrc[19] 000178_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000178_3.results/stats.000178_3_REML+tlrc[19] 000573_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000573_1.results/stats.000573_1_REML+tlrc[19] 000573_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000573_3.results/stats.000573_3_REML+tlrc[19] 000955_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000955_1.results/stats.000955_1_REML+tlrc[19] 000955_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000955_3.results/stats.000955_3_REML+tlrc[19] 001156_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001156_1.results/stats.001156_1_REML+tlrc[19] 001156_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001156_3.results/stats.001156_3_REML+tlrc[19] 001271_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001271_1.results/stats.001271_1_REML+tlrc[19] 001271_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001271_3.results/stats.001271_3_REML+tlrc[19] 001372_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001372_1.results/stats.001372_1_REML+tlrc[19] 001372_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001372_3.results/stats.001372_3_REML+tlrc[19] 002665_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/002665_1.results/stats.002665_1_REML+tlrc[19] 002665_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/002665_3.results/stats.002665_3_REML+tlrc[19] 004386_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004386_1.results/stats.004386_1_REML+tlrc[19] 004386_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004386_3.results/stats.004386_3_REML+tlrc[19] 004397_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004397_1.results/stats.004397_1_REML+tlrc[19] 004397_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004397_3.results/stats.004397_3_REML+tlrc[19] 005703_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005703_1.results/stats.005703_1_REML+tlrc[19] 005703_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005703_3.results/stats.005703_3_REML+tlrc[19] 005760_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005760_1.results/stats.005760_1_REML+tlrc[19] 005760_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005760_3.results/stats.005760_3_REML+tlrc[19] 005782_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005782_1.results/stats.005782_1_REML+tlrc[19] 005782_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005782_3.results/stats.005782_3_REML+tlrc[19] 005894_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005894_1.results/stats.005894_1_REML+tlrc[19] 005894_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005894_3.results/stats.005894_3_REML+tlrc[19] 005927_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005927_1.results/stats.005927_1_REML+tlrc[19] 005927_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005927_3.results/stats.005927_3_REML+tlrc[19] 005973_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005973_1.results/stats.005973_1_REML+tlrc[19] 005973_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005973_3.results/stats.005973_3_REML+tlrc[19]
Loading required package: lme4
Loading required package: Matrix
************
Welcome to afex. For support visit: [afex.singmann.science]
- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
- Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
- NEWS: emmeans() for ANOVA models now uses model = 'multivariate' as default.
- Get and set global package options with: afex_options()
- Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()
- For example analyses see: browseVignettes("afex")
************
Attaching package: ‘afex’
The following object is masked from ‘package:lme4’:
lmer
Loading required package: car
Loading required package: carData
++++++++++++++++++++++++++++++++++++++++++++++++++++
***** Summary information of data structure *****
32 subjects : 000156_1 000156_3 000178_1 000178_3 000573_1 000573_3 000955_1 000955_3 001156_1 001156_3 001271_1 001271_3 001372_1 001372_3 002665_1 002665_3 004386_1 004386_3 004397_1 004397_3 005703_1 005703_3 005760_1 005760_3 005782_1 005782_3 005894_1 005894_3 005927_1 005927_3 005973_1 005973_3
32 response values
2 levels for factor Session : Post Pre
2 levels for factor Group : Placebo Training
7 post hoc tests
Contingency tables of subject distributions among the categorical variables:
Session
Group Post Pre
Placebo 10 10
Training 6 6
Tabulation of subjects against each of the categorical variables:
~~~~~~~~~~~~~~
lop$nSubj vs Session:
Post Pre
000156_1 0 1
000156_3 1 0
000178_1 0 1
000178_3 1 0
000573_1 0 1
000573_3 1 0
000955_1 0 1
000955_3 1 0
001156_1 0 1
001156_3 1 0
001271_1 0 1
001271_3 1 0
001372_1 0 1
001372_3 1 0
002665_1 0 1
002665_3 1 0
004386_1 0 1
004386_3 1 0
004397_1 0 1
004397_3 1 0
005703_1 0 1
005703_3 1 0
005760_1 0 1
005760_3 1 0
005782_1 0 1
005782_3 1 0
005894_1 0 1
005894_3 1 0
005927_1 0 1
005927_3 1 0
005973_1 0 1
005973_3 1 0
~~~~~~~~~~~~~~
lop$nSubj vs Group:
Placebo Training
000156_1 1 0
000156_3 1 0
000178_1 0 1
000178_3 0 1
000573_1 1 0
000573_3 1 0
000955_1 1 0
000955_3 1 0
001156_1 0 1
001156_3 0 1
001271_1 0 1
001271_3 0 1
001372_1 0 1
001372_3 0 1
002665_1 1 0
002665_3 1 0
004386_1 0 1
004386_3 0 1
004397_1 0 1
004397_3 0 1
005703_1 1 0
005703_3 1 0
005760_1 1 0
005760_3 1 0
005782_1 1 0
005782_3 1 0
005894_1 1 0
005894_3 1 0
005927_1 1 0
005927_3 1 0
005973_1 1 0
005973_3 1 0
***** End of data structure information *****
++++++++++++++++++++++++++++++++++++++++++++++++++++
Reading input files now...
Reading input files: Done!
Range of input data: [-24.445, 38.022]
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 test failed! ~~~~~~~~~~~~~~~~~~~
Possible reasons:
0) Make sure that R packages afex and phia have been installed. See the 3dMVM
help documentation for more details.
1) Inappropriate model specification with options -bsVars, -wsVars, or -qVars.
Note that within-subject or repeated-measures variables have to be declared
with -wsVars.
2) Incorrect specifications in general linear test coding with -gltCode.
3) Mistakes in data table. Check the data structure shown above, and verify
whether there are any inconsistencies.
4) Inconsistent variable names which are case sensitive. For example, factor
named Group in model specification and then listed as group in the table header
would cause grief for 3dMVM.
5) Not enough number of subjects. This may happen when there are two or more
withi-subject factors. For example, a model with two within-subject factors with
m and n levels respectively requires more than (m-1)*(n-1) subjects to be able to
model the two-way interaction with the multivariate approach.
** Error:
Quitting due to model test failure...
There were 50 or more warnings (use warnings() to see the first 50)
I am attempting to conduct a group-level analysis using 3dMVM. The design has 1 between-subject factor (group) and 1 within-subject factor (session). I would like to use 3dMVM because I have an unequal number of subjects in each group. I have followed the example set-up from the 3dMVM help documentation. When I attempt to run 3dMVM, it looks like the data structure and design is being read properly, but I then get the "Model test failed!" message. I can't figure out what the issue is. I think my script looks good based on my reading of the help docs, and I have the required R packages installed. I'm getting the same error when attempting to run this on Mac and on Ubuntu machines. I also tried running the 3dMVM validator but it returned various program dependency errors I could not resolve.
Any assistance would be much appreciated. Please see below for the 3dMVM script and output log.
Thanks!
Michael
3dMVM -prefix GroupAnalysis -jobs 9 \
-mask mask_union_group+tlrc \
-bsVars Group \
-wsVars Session \
-num_glt 7 \
-gltLabel 1 TrainingVsPlacebo -gltCode 1 'Group : 1*Training -1*Placebo' \
-gltLabel 2 PreVsPost -gltCode 2 'Session : 1*Pre -1*Post' \
-gltLabel 3 GroupXSession -gltCode 3 'Group : 1*Training -1*Placebo Session : 1*Pre -1*Post' \
-gltLabel 4 Training_PreVsPost -gltCode 4 'Session : 1*Pre -1*Post Group : 1*Training' \
-gltLabel 5 Placebo_PreVsPost -gltCode 5 'Session : 1*Pre -1*Post Group : 1*Placebo' \
-gltLabel 6 Pre_TrainingVsPlacebo -gltCode 6 'Group : 1*Training -1*Placebo Session : 1*Pre' \
-gltLabel 7 Post_TrainingVsPlacebo -gltCode 7 'Group : 1*Training -1*Placebo Session : 1*Post' \
-dataTable \
Subj Session Group InputFile \
000156_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000156_1.results/stats.000156_1_REML+tlrc'[19]' \
000156_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000156_3.results/stats.000156_3_REML+tlrc'[19]' \
000178_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000178_1.results/stats.000178_1_REML+tlrc'[19]' \
000178_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000178_3.results/stats.000178_3_REML+tlrc'[19]' \
000573_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000573_1.results/stats.000573_1_REML+tlrc'[19]' \
000573_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000573_3.results/stats.000573_3_REML+tlrc'[19]' \
000955_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000955_1.results/stats.000955_1_REML+tlrc'[19]' \
000955_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000955_3.results/stats.000955_3_REML+tlrc'[19]' \
001156_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001156_1.results/stats.001156_1_REML+tlrc'[19]' \
001156_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001156_3.results/stats.001156_3_REML+tlrc'[19]' \
001271_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001271_1.results/stats.001271_1_REML+tlrc'[19]' \
001271_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001271_3.results/stats.001271_3_REML+tlrc'[19]' \
001372_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001372_1.results/stats.001372_1_REML+tlrc'[19]' \
001372_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001372_3.results/stats.001372_3_REML+tlrc'[19]' \
002665_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/002665_1.results/stats.002665_1_REML+tlrc'[19]' \
002665_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/002665_3.results/stats.002665_3_REML+tlrc'[19]' \
004386_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004386_1.results/stats.004386_1_REML+tlrc'[19]' \
004386_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004386_3.results/stats.004386_3_REML+tlrc'[19]' \
004397_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004397_1.results/stats.004397_1_REML+tlrc'[19]' \
004397_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004397_3.results/stats.004397_3_REML+tlrc'[19]' \
005703_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005703_1.results/stats.005703_1_REML+tlrc'[19]' \
005703_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005703_3.results/stats.005703_3_REML+tlrc'[19]' \
005760_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005760_1.results/stats.005760_1_REML+tlrc'[19]' \
005760_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005760_3.results/stats.005760_3_REML+tlrc'[19]' \
005782_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005782_1.results/stats.005782_1_REML+tlrc'[19]' \
005782_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005782_3.results/stats.005782_3_REML+tlrc'[19]' \
005894_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005894_1.results/stats.005894_1_REML+tlrc'[19]' \
005894_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005894_3.results/stats.005894_3_REML+tlrc'[19]' \
005927_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005927_1.results/stats.005927_1_REML+tlrc'[19]' \
005927_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005927_3.results/stats.005927_3_REML+tlrc'[19]' \
005973_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005973_1.results/stats.005973_1_REML+tlrc'[19]' \
005973_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005973_3.results/stats.005973_3_REML+tlrc'[19]'
Output log:
3dMVM -prefix GroupAnalysis -jobs 9 -mask mask_union_group+tlrc -bsVars Group -wsVars Session -num_glt 7 -gltLabel 1 TrainingVsPlacebo -gltCode 1 Group : 1*Training -1*Placebo -gltLabel 2 PreVsPost -gltCode 2 Session : 1*Pre -1*Post -gltLabel 3 GroupXSession -gltCode 3 Group : 1*Training -1*Placebo Session : 1*Pre -1*Post -gltLabel 4 Training_PreVsPost -gltCode 4 Session : 1*Pre -1*Post Group : 1*Training -gltLabel 5 Placebo_PreVsPost -gltCode 5 Session : 1*Pre -1*Post Group : 1*Placebo -gltLabel 6 Pre_TrainingVsPlacebo -gltCode 6 Group : 1*Training -1*Placebo Session : 1*Pre -gltLabel 7 Post_TrainingVsPlacebo -gltCode 7 Group : 1*Training -1*Placebo Session : 1*Post -dataTable Subj Session Group InputFile 000156_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000156_1.results/stats.000156_1_REML+tlrc[19] 000156_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000156_3.results/stats.000156_3_REML+tlrc[19] 000178_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000178_1.results/stats.000178_1_REML+tlrc[19] 000178_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000178_3.results/stats.000178_3_REML+tlrc[19] 000573_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000573_1.results/stats.000573_1_REML+tlrc[19] 000573_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000573_3.results/stats.000573_3_REML+tlrc[19] 000955_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000955_1.results/stats.000955_1_REML+tlrc[19] 000955_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/000955_3.results/stats.000955_3_REML+tlrc[19] 001156_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001156_1.results/stats.001156_1_REML+tlrc[19] 001156_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001156_3.results/stats.001156_3_REML+tlrc[19] 001271_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001271_1.results/stats.001271_1_REML+tlrc[19] 001271_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001271_3.results/stats.001271_3_REML+tlrc[19] 001372_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001372_1.results/stats.001372_1_REML+tlrc[19] 001372_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/001372_3.results/stats.001372_3_REML+tlrc[19] 002665_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/002665_1.results/stats.002665_1_REML+tlrc[19] 002665_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/002665_3.results/stats.002665_3_REML+tlrc[19] 004386_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004386_1.results/stats.004386_1_REML+tlrc[19] 004386_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004386_3.results/stats.004386_3_REML+tlrc[19] 004397_1 Pre Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004397_1.results/stats.004397_1_REML+tlrc[19] 004397_3 Post Training /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/004397_3.results/stats.004397_3_REML+tlrc[19] 005703_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005703_1.results/stats.005703_1_REML+tlrc[19] 005703_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005703_3.results/stats.005703_3_REML+tlrc[19] 005760_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005760_1.results/stats.005760_1_REML+tlrc[19] 005760_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005760_3.results/stats.005760_3_REML+tlrc[19] 005782_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005782_1.results/stats.005782_1_REML+tlrc[19] 005782_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005782_3.results/stats.005782_3_REML+tlrc[19] 005894_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005894_1.results/stats.005894_1_REML+tlrc[19] 005894_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005894_3.results/stats.005894_3_REML+tlrc[19] 005927_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005927_1.results/stats.005927_1_REML+tlrc[19] 005927_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005927_3.results/stats.005927_3_REML+tlrc[19] 005973_1 Pre Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005973_1.results/stats.005973_1_REML+tlrc[19] 005973_3 Post Placebo /media/canlab/CANLABMRI/r61/r61ertt/first_level_analysis/2021_10_19_first_level/005973_3.results/stats.005973_3_REML+tlrc[19]
Loading required package: lme4
Loading required package: Matrix
************
Welcome to afex. For support visit: [afex.singmann.science]
- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
- Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
- NEWS: emmeans() for ANOVA models now uses model = 'multivariate' as default.
- Get and set global package options with: afex_options()
- Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()
- For example analyses see: browseVignettes("afex")
************
Attaching package: ‘afex’
The following object is masked from ‘package:lme4’:
lmer
Loading required package: car
Loading required package: carData
++++++++++++++++++++++++++++++++++++++++++++++++++++
***** Summary information of data structure *****
32 subjects : 000156_1 000156_3 000178_1 000178_3 000573_1 000573_3 000955_1 000955_3 001156_1 001156_3 001271_1 001271_3 001372_1 001372_3 002665_1 002665_3 004386_1 004386_3 004397_1 004397_3 005703_1 005703_3 005760_1 005760_3 005782_1 005782_3 005894_1 005894_3 005927_1 005927_3 005973_1 005973_3
32 response values
2 levels for factor Session : Post Pre
2 levels for factor Group : Placebo Training
7 post hoc tests
Contingency tables of subject distributions among the categorical variables:
Session
Group Post Pre
Placebo 10 10
Training 6 6
Tabulation of subjects against each of the categorical variables:
~~~~~~~~~~~~~~
lop$nSubj vs Session:
Post Pre
000156_1 0 1
000156_3 1 0
000178_1 0 1
000178_3 1 0
000573_1 0 1
000573_3 1 0
000955_1 0 1
000955_3 1 0
001156_1 0 1
001156_3 1 0
001271_1 0 1
001271_3 1 0
001372_1 0 1
001372_3 1 0
002665_1 0 1
002665_3 1 0
004386_1 0 1
004386_3 1 0
004397_1 0 1
004397_3 1 0
005703_1 0 1
005703_3 1 0
005760_1 0 1
005760_3 1 0
005782_1 0 1
005782_3 1 0
005894_1 0 1
005894_3 1 0
005927_1 0 1
005927_3 1 0
005973_1 0 1
005973_3 1 0
~~~~~~~~~~~~~~
lop$nSubj vs Group:
Placebo Training
000156_1 1 0
000156_3 1 0
000178_1 0 1
000178_3 0 1
000573_1 1 0
000573_3 1 0
000955_1 1 0
000955_3 1 0
001156_1 0 1
001156_3 0 1
001271_1 0 1
001271_3 0 1
001372_1 0 1
001372_3 0 1
002665_1 1 0
002665_3 1 0
004386_1 0 1
004386_3 0 1
004397_1 0 1
004397_3 0 1
005703_1 1 0
005703_3 1 0
005760_1 1 0
005760_3 1 0
005782_1 1 0
005782_3 1 0
005894_1 1 0
005894_3 1 0
005927_1 1 0
005927_3 1 0
005973_1 1 0
005973_3 1 0
***** End of data structure information *****
++++++++++++++++++++++++++++++++++++++++++++++++++++
Reading input files now...
Reading input files: Done!
Range of input data: [-24.445, 38.022]
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 test failed! ~~~~~~~~~~~~~~~~~~~
Possible reasons:
0) Make sure that R packages afex and phia have been installed. See the 3dMVM
help documentation for more details.
1) Inappropriate model specification with options -bsVars, -wsVars, or -qVars.
Note that within-subject or repeated-measures variables have to be declared
with -wsVars.
2) Incorrect specifications in general linear test coding with -gltCode.
3) Mistakes in data table. Check the data structure shown above, and verify
whether there are any inconsistencies.
4) Inconsistent variable names which are case sensitive. For example, factor
named Group in model specification and then listed as group in the table header
would cause grief for 3dMVM.
5) Not enough number of subjects. This may happen when there are two or more
withi-subject factors. For example, a model with two within-subject factors with
m and n levels respectively requires more than (m-1)*(n-1) subjects to be able to
model the two-way interaction with the multivariate approach.
** Error:
Quitting due to model test failure...
There were 50 or more warnings (use warnings() to see the first 50)
| Subject | Author | Posted |
|---|---|---|
|
mjss | May 20, 2022 01:11PM |
|
gang | May 21, 2022 04:17PM |
|
mjss | May 24, 2022 01:01PM |
|
mjss | May 24, 2022 03:42PM |
|
gang | May 25, 2022 11:17AM |
