Nicolas Cherbuin wrote:
Dear Doug,
Thank you, this is very helpful! I had searched the web and the fs site extensively but did not find anything helpful on this problem before you rescued me.
I have some follow up questions:
1. Can the DeMeanFlag be used in qdec (qdec was segfaulting when doing similar analyses and presumably it is for the same reason)
No, you'll have to demean them by hand and put them into the qdec table
as a separate col.
2. When does correlations between covariates becomes a problem (e.g. 0.6 or 0.9)
Impossible to say. The threshold I use in mri_glmfit is a condition
number of 10,000 for the matrix M=X'*X, where X is the normalized
design matrix. This threshold is somewhat arbitrary. Newer versions of
glmfit allow it to proceed even if ill-conditioned (you probably won't
see anything of significance).
3. When using dods, what are my options with somewhat correlated measures. I research brain changes in ageing. Typically I control for age, gender, icv, and sometimes other variables. In ageing, age, icv, and cognition tend to be correlated but it is critical to control, say, for icv to determine whether an effect of interest is due to women having on average smaller heads and therefore smaller brains or due to brain atrophy in some individuals, or to other factors.
This is a tricky issue with lots of details, so it's hard to give
general advice. If you are just testing slopes and not intercepts, then
you can demean with impunity (note that you have to demean within
class with DODS). In this case you interpret the group intercept as a
nuisance variable. If you are testing intercepts (ie, the continuous
variables are nuisance), then you don't have a lot of room to manuever.
You could orthogonalize within the nuisance variables, but this won't
help with the problems of the means correlating with the intercepts.
doug
I really appreciate yours and your team's efforts both in developing great software and answering questions from challenged users like myself.
Regards,
Nic
-----Original Message-----
From: Doug Greve [mailto:greve@nmr.mgh.harvard.edu]
Sent: Wed 3/12/2008 5:34 AM
To: Nicolas Cherbuin
Cc: freesurfer@nmr.mgh.harvard.edu
Subject: Re: [Freesurfer] mri_glmfit problem
The problem is that there is a strong correlation in the two continuous
variables due to their large non-zero means. The knee-jerk reaction is
to simply demean the variables which is sometimes the right thing to do
and sometimes not. In this case, I think it is ok because you are using
DOSS. If you were to switch to DODS, it would be the wrong thing to do.
Note, you can instruct mri_glmfit to demean the continuous variables by
adding the following line to your fsgd file:
DeMeanFlag 1
Again, you should be careful with this and don't use it with DODS
doug
Nicolas Cherbuin wrote:
Hi,
I am analysing structural data with mri_glmfit. After pre-processing I have first used one group variable (gender) with one covariate age. I had no problem processing the data and visualising it in tksurfer.
I then tried to add another covariate (measure of immediate recall). So I had in total one group variable and two covariates. I reanalysed the data but got the following error:
FixVertexAreaFlag = 1
UseMaskWithSmoothing 1
OneSampleGroupMean 0
y /media/storage2/proc/freesurfer/60_w2/checked/analysis/lh.gender_age_immrec.thickness.10.mgh
logyflag 0
usedti 0
FSGD gender_age_immrec_fsgd.txt
glmdir lh.gender_age_immrec.glmdir
DoFFx 0
Creating output directory lh.gender_age_immrec.glmdir
Loading y from /media/storage2/proc/freesurfer/60_w2/checked/analysis/lh.gender_age_immrec.thickness.10.mgh
Matrix condition is 12866.7
ERROR: matrix is ill-conditioned or badly scaled, condno = 12866.7
Possible problem with experimental design:
Check for duplicate entries and/or lack of range of
continuous variables within a class.
0.000 1.000 68.000 10.000;
1.000 0.000 65.000 5.000;
[trunkated the fsgd file output but it is the same as the one used for the sucessful analysis except for one extra column]
After this error I thoroughly checked all my files but could not find anything wrong. So to check I could do an analysis using the extra variable I did another analysis with gender and immrec. This worked without problem.
I have attached all my fsgd files and mat files. They all use the same subjects and gender info and only differ by one column. Since permutations worked I cannot fathom why it does not work when all put together.
The mri_glmfit syntax used for each analysis was:
mri_glmfit --y lh.gender_age_immrec.thickness.10.mgh --fsgd gender_age_immrec_fsgd.txt doss --glmdir lh.gender_age_immrec.glmdir --surf average100 lh --C gender_age_immrec.mat
mri_glmfit --y lh.gender_immrec.thickness.10.mgh --fsgd gender_immrec_fsgd.txt doss --glmdir lh.gender_immrec.glmdir --surf average100 lh --C gender_immrec.mat
mri_glmfit --y lh.gender_age_immrec.thickness.10.mgh --fsgd gender_age_immrec_fsgd.txt doss --glmdir lh.gender_age_immrec.glmdir --surf average100 lh --C gender_age_immrec.mat
I use 4.01 (but also checked with 4.02) under Ubuntu 64 bit on a Xeon machine.
Thanks very much for your help
Nic
------------------------------------------------------------------------
GroupDescriptorFile 1
Class Male plus blue
Class Female circle red
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DefaultVariable age
------------------------------------------------------------------------
GroupDescriptorFile 1
Class Male
Class Female
Variables immrec
Input 60047 Female 10
Input 60092 Male 5
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DefaultVariable immrec
------------------------------------------------------------------------
GroupDescriptorFile 1
Class Male plus blue
Class Female circle red
Variables age immrec
Input 60047 Female 68 10
Input 60092 Male 65 5
Input 60094 Female 69 11
Input 60129 Female 69 11
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Input 62306 Female 67 6
Input 62346 Male 69 8
Input 62355 Male 68 8
Input 62390 Male 67 4
Input 62395 Female 65 9
Input 62416 Male 67 5
Input 62443 Male 68 8
Input 62465 Male 65 4
Input 62474 Male 64 6
Input 62480 Male 69 6
Input 62484 Male 65 7
Input 62496 Female 64 7
Input 62500 Male 66 6
Input 62516 Male 66 8
Input 62523 Female 66 6
Input 62529 Female 64 7
Input 62538 Male 69 7
Input 62571 Male 65 4
Input 62581 Female 67 8
Input 62585 Male 69 6
Input 62630 Male 67 7
Input 62640 Male 66 8
Input 62649 Female 66 5
Input 62671 Male 65 9
Input 62675 Male 67 10
Input 62685 Male 66 7
Input 62691 Male 69 5
Input 62703 Male 65 6
Input 62713 Female 67 6
Input 62749 Female 66 8
Input 62751 Female 65 6
Input 62768 Male 67 8
Input 62770 Female 69 8
Input 62773 Female 67 5
Input 62793 Female 68 7
Input 62841 Male 65 7
Input 62848 Male 66 5
Input 62850 Male 67 4
Input 62858 Female 68 5
Input 62866 Male 67 8
Input 62916 Female 66 8
Input 62920 Female 67 8
Input 62935 Female 68 6
Input 62955 Male 68 5
Input 62965 Female 65 9
Input 62975 Male 65 9
Input 63001 Male 66 7
Input 63010 Female 65 10
Input 63015 Male 68 7
Input 63026 Female 68 6
Input 63028 Female 65 10
Input 63035 Female 66 9
Input 63037 Male 67 6
Input 63040 Female 65 8
Input 63044 Female 65 9
Input 63053 Male 67 5
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Input 63121 Male 65 7
Input 63136 Male 65 7
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Input 63194 Male 65 6
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Input 63365 Male 68 7
Input 63407 Female 66 8
Input 63411 Male 67 7
Input 63429 Male 66 9
Input 63433 Female 65 5
Input 63434 Male 65 8
Input 63435 Female 68 8
Input 63450 Female 68 6
Input 63470 Male 68 6
Input 63471 Male 66 8
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Input 63492 Male 68 6
Input 63534 Female 64 12
Input 63545 Female 65 6
Input 63568 Female 69 10
Input 63575 Male 66 5
Input 63586 Female 67 5
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Input 63637 Male 66 5
Input 63649 Male 66 10
Input 63674 Female 69 8
Input 63693 Female 65 12
Input 63750 Female 65 7
Input 63755 Female 68 8
Input 63766 Male 66 6
Input 63770 Male 67 6
Input 63773 Male 65 4
Input 63801 Female 65 8
Input 63819 Male 64 2
Input 63842 Female 67 6
Input 63860 Female 67 7
Input 63870 Male 67 5
Input 63897 Female 65 6
Input 63909 Female 67 6
Input 63943 Female 68 9
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Input 64058 Male 65 6
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Input 64119 Female 67 4
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Input 64186 Male 70 8
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Input 64387 Male 65 8
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Input 64531 Female 64 6
Input 64537 Female 64 6
Input 64542 Female 65 8
Input 64552 Female 67 11
Input 64590 Male 66 3
Input 64592 Female 68 7
Input 64596 Male 66 9
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Input 64640 Male 64 8
Input 64657 Female 66 6
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Input 64706 Male 69 4
Input 64734 Male 67 4
Input 64745 Female 66 7
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Input 64781 Male 66 10
Input 64797 Male 66 7
Input 64798 Male 68 7
Input 64806 Male 67 8
Input 64812 Male 66 7
Input 64818 Male 66 9
Input 64824 Female 65 6
Input 64829 Male 67 8
Input 64843 Female 67 9
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Input 64886 Male 69 6
Input 64897 Female 68 10
Input 64899 Male 65 4
Input 64910 Male 65 6
Input 64913 Female 68 12
Input 64937 Male 68 5
Input 64947 Female 65 9
Input 64961 Female 68 8
Input 64987 Male 67 11
Input 64997 Male 68 5
DefaultVariable age
------------------------------------------------------------------------
$Id: mri_glmfit.c,v 1.138.2.1 2007/09/12 15:38:19 nicks Exp $
cwd /media/storage2/proc/freesurfer/60_w2/checked/analysis
cmdline mri_glmfit --y lh.gender_age_immrec.thickness.10.mgh --fsgd gender_age_immrec_fsgd.txt doss --glmdir lh.gender_age_immrec.glmdir --surf average100 lh --C gender_age_immrec.mat
sysname Linux
hostname mri-lab
machine x86_64
user u3184049
FixVertexAreaFlag = 1
UseMaskWithSmoothing 1
OneSampleGroupMean 0
y /media/storage2/proc/freesurfer/60_w2/checked/analysis/lh.gender_age_immrec.thickness.10.mgh
logyflag 0
usedti 0
FSGD gender_age_immrec_fsgd.txt
glmdir lh.gender_age_immrec.glmdir
DoFFx 0
------------------------------------------------------------------------
_______________________________________________
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
--
Douglas N. Greve, Ph.D.
MGH-NMR Center
greve@nmr.mgh.harvard.edu
Phone Number: 617-724-2358
Fax: 617-726-7422
In order to help us help you, please follow the steps in:
surfer.nmr.mgh.harvard.edu/fswiki/BugReporting