Hi Allie, the 5th column of your design matrix is all 0s, which causes the ill conditioning. I don't think this design fits into our standard framework. You can still analyze and test, we'll just have to figure out exactly what needs to be done. In the end, what is it that you want to test? Just a difference in intercepts between the two groups? doug

On 05/07/2012 09:38 AM, Allie Rosen wrote:

Hello,

I am analyzing cortical thickness in a patient and control group. I have two covariates: age and length of disease. I am using the model from:

http://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G2V

However, for the control group I put length of disease as 0 in my FSGD file. When I run the GLM, I get the following error message:

matrix is ill-conditioned or badly scaled, condno = 1e+08

Can you please tell me what I am doing wrong? Below is the required information.

1. Your command line:

   mri_glmfit --y lh.disease_length.thickness.20.mgz --fsgd

fsgd_disease_length.txt dods --glmdir lh.disease_length.thickness.20.glmdir --surf fsaverage lh --C groupdiff_length_disease.mat

2. The FSGD file

GroupDescriptorFile 1 Title Anorexia vs Controls Class Control plus blue Class Patient circle green Variables Age Disease_Length

Input C07 Control 37 0 Input C28 Control 28 0 Input C39 Control 37 0 Input C54 Control 40 0 Input C57 Control 32 0 Input C10 Control 54 0 Input C33 Control 31 0 Input C29 Control 22 0 Input A01 Patient 39 20 Input A02 Patient 24 11 Input A03 Patient 35 15 Input A04 Patient 40 10 Input A05 Patient 35 20 Input A06 Patient 57 20 Input A07 Patient 31 16 Input A08 Patient 20 8

3. And the design matrix

Design matrix ------------------ 1.000 0.000 37.000 0.000 0.000 0.000; 1.000 0.000 28.000 0.000 0.000 0.000; 1.000 0.000 37.000 0.000 0.000 0.000; 1.000 0.000 40.000 0.000 0.000 0.000; 1.000 0.000 32.000 0.000 0.000 0.000; 1.000 0.000 54.000 0.000 0.000 0.000; 1.000 0.000 31.000 0.000 0.000 0.000; 1.000 0.000 22.000 0.000 0.000 0.000; 0.000 1.000 0.000 39.000 0.000 20.000; 0.000 1.000 0.000 24.000 0.000 11.000; 0.000 1.000 0.000 35.000 0.000 15.000; 0.000 1.000 0.000 40.000 0.000 10.000; 0.000 1.000 0.000 35.000 0.000 20.000; 0.000 1.000 0.000 57.000 0.000 20.000; 0.000 1.000 0.000 31.000 0.000 16.000; 0.000 1.000 0.000 20.000 0.000 8.000;

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Thank you, Allie

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