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Hi again,
What about for DODS? I tried to create the contrast for this but got the
following error:
ERROR: matrix is ill-conditioned or badly scaled, condno = 1e+08
--------------------------------
Possible problem with experimental design:
Check for duplicate entries and/or lack of range of
continuous variables within a class.
If you seek help with this problem, make sure to send:
1. Your command line:
mri_glmfit.bin --y rh.5.lgi.centre.mgh --fsgd FSGD_centre.txt dods
--glmdir rh.5.lgi.centre.glmdir --surf fsaverage rh --C
H-NH_centre.dods.txt
2. The FSGD file (if using one)
3. And the design matrix above
Attempting to diagnose further
SumSq: Min=1.000000 (col 4), Max=183.117447 (col 31)
The scale is much different between columns 4 and 31, you may want to
normalize by subtracting the mean and dividing by the standard
deviation.
My contrast was 1 -1 -1 -1 -1 -1 -1 -1 -1 0.75 0.75 0.75 0.75 0.75 0.75
0.75 0.75 0.75 0.75 0.75 0.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
and FSGD attached (does not include full list of subjects).
Thanks again,
Colleen
On 2018-05-08 16:56, C.P.E. Rollins wrote:
Ah, I think I understand now, thanks. I've attached my contrast and
FSGD file to confirm. In the FSGD file, the format for the classes are
<group>_<sex>_<centre>.
Please let me know.
Thanks again,
Colleen
On 2018-05-04 09:49, C.P.E. Rollins wrote:
Hi,
Sorry for coming back to this, but I'm still confused about the issue
of having unbalanced classes. I thought that I could not remove the
class as you suggested since this would mean that the contrast
positive and negative values would no longer add up to 0. For
instance if I remove only class "patient_centre2_female", then my
contrast would have 11 (-1) and 12 (1) as opposed to 12 and 12. Could
you please clarify?
Thanks again,
Colleen
On 2018-04-30 14:43, C.P.E. Rollins wrote:
I thought that I could not remove the class since this would mean
that
the contrast positive and negative values would no longer add up to
0.
For instance if I remove only class "patient_centre2_female", then my
contrast would have 11 (-1) and 12 (1) as opposed to 12 and 12. Could
you please clarify?
Thanks again,
Colleen
-------- Original Message --------
Subject: Re: [Freesurfer] FSGD design for multi-centre study
Date: 2018-04-20 11:19
From: "C.P.E. Rollins" <[email protected]>
To: Freesurfer <[email protected]>
Thanks a lot for the explanation. The issue is that I don't think
Freesurfer will run a design for which there are no subjects for a
given class. So if I keep the classes (24 classes since 6 centres x 2
gender x 2 groups (patient vs. control), and have the contrast with
12
(-1) and 12 (1),
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1 1 1 1 1 1 0
I get the error:
--------------------------------
ERROR: matrix is ill-conditioned or badly scaled, condno = 1e+08
--------------------------------
Possible problem with experimental design:
Check for duplicate entries and/or lack of range of
continuous variables within a class.
I'm assuming this is because there are no participants in, for
example, patient_centre2_female
Is there any way to get around this issue, or should I remove those
centres from my analysis (since I can't only remove
"patient_centre2_female", as this would make the contrast unbalanced
(positives and negatives would not add to the same number). I hope
this was clear but please let me know if it was not.
Thanks again,
Colleen
GroupDescriptorFile 1
Class NH_Male_MAN
Class NH_Female_MAN
Class NH_Male_EDB
Class NH_Male_CAM
Class NH_Female_CAM
Class NH_Male_UCL
Class NH_Male_KCL
Class NH_Female_KCL
Class NH_Male_BIR
Class H_Male_MAN
Class H_Female_MAN
Class H_Male_EDB
Class H_Female_EDB
Class H_Male_CAM
Class H_Female_CAM
Class H_Male_UCL
Class H_Female_UCL
Class H_Male_KCL
Class H_Female_KCL
Class H_Male_BIR
Class H_Female_BIR
Variables Age
Input H_BMN001-002 H_Male_MAN 32
Input H_BMN001-003 H_Male_MAN 19
Input H_BMN001-006 H_Male_MAN 26
Input H_BMN001-015 H_Male_MAN 30
Input H_BMN001-040 H_Female_MAN 28
Input H_BMN001-041 H_Female_MAN 20
Input H_BMN001-042 H_Male_MAN 25
Input H_BMN001-043 H_Male_MAN 20
Input H_BMN001-044 H_Male_MAN 25
Input H_BMN001-046 H_Male_MAN 21
Input H_BMN001-055 H_Male_MAN 26
Input H_BMN001-056 H_Male_MAN 22
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