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Hi Sophie,
it's a bit hard to tell, but maybe one option would be to try fitting the model
with just a random-intercept effect instead of both random-intercept and
random-slope effects.
I.e. using [1] instead of [1 2] for the Zcols argument to lme_mass_fit_EMinit.
In my experience the random-intercept models are a bit easier to fit.
Please write if the error still persists.
Best regards,
Kersten
On Fri, 2023-11-24 at 01:42 +0000, Sophie Matis wrote:
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Hello FreeSurfer experts!
I have carried out some analysis of changes in cortical thickness over time
using the LME Matlab (R23b) Toolbox distributed within Freesurfer (7.4.1) in
line with the instructions on this page: MailScanner has detected a possible
fraud attempt from "secure-web.cisco.com" claiming to be
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At this stage I just want to look at changes over time within each of three
groups. For two groups I have been able to proceed through the steps fine and
have my significance files which look normal. For the last group I am running
into an error on the “[lhTh0,lhRe] = lme_mass_fit_EMinit(X,[1
2],Y,ni,lhcortex,3); “ step. MATLAB returns “Warning: Matrix is singular, close
to singular or badly scaled” and lhTh0 and lhRe are created as matrices with
only NaN values.
Has anyone run into this error before or have any tips for troubleshooting it?
I haven’t had any errors on the longitudinal processing steps and visual QC on
my files looks normal. No errors on mris_preproc or mri_surf2surf. An
aparcstats2table for thickness doesn’t reveal any NaNs.
My design matrix is created with X = [ones(length(M),1) M] after following the
suggested sortData code from the wiki
Contrast is [0 1]
This is my qdec file:
fsid fsid-base yearsfromBL
108_MR1 108 0.00
199_MR1 199 0.00
199_MR2 199 1.10
199_MR3 199 2.25
202_MR1 202 0.00
204_MR1 204 0.00
204_MR2 204 1.16
204_MR3 204 2.30
223_MR1 223 0.00
0821_MR1 0821 0.00
0931_MR1 0931 0.00
1010_MR1 1010 0.00
1010_MR2 1010 1.84
1031_MR1 1031 0.00
1031_MR2 1031 1.00
1031_MR3 1031 2.13
1064_MR1 1064 0.00
1064_MR2 1064 2.01
1125_MR1 1125 0.00
1134_MR1 1134 0.00
1134_MR2 1134 1.17
1134_MR3 1134 2.28
1233_MR1 1233 0.00
1233_MR2 1233 1.05
1373_MR1 1373 0.00
1439_MR1 1439 0.00
1439_MR2 1439 1.14
1439_MR3 1439 2.28
1475_MR1 1475 0.00
1475_MR2 1475 1.04
1475_MR3 1475 2.03
1522_MR1 1522 0.00
1522_MR2 1522 2.07
1541_MR1 1541 0.00
1541_MR2 1541 1.47
1551_MR1 1551 0.00
1551_MR2 1551 0.98
1551_MR3 1551 2.05
1572_MR1 1572 0.00
1572_MR2 1572 1.03
1625_MR1 1625 0.00
1625_MR2 1625 1.06
1637_MR1 1637 0.00
1641_MR1 1641 0.00
1656_MR1 1656 0.00
1656_MR2 1656 0.94
1656_MR3 1656 2.26
1755_MR1 1755 0.00
1755_MR2 1755 1.04
1755_MR3 1755 2.01
1829_MR1 1829 0.00
09154_MR1 09154 0.00
09154_MR2 09154 1.01
09154_MR3 09154 2.01
12110_MR1 12110 0.00
12110_MR2 12110 1.04
Any help appreciated!
Thank you,
Sophie Matis | Data Management & Research Officer
School of Health Sciences & Brain and Mind Centre
Faculty of Medicine and Health
The University of Sydney
Brain and Mind Centre, 94-100 Mallett Street, Camperdown (M02G) | The
University of Sydney | NSW | 06
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