I think just using DOSS instead of DODS will do it (--fsgd your.fsgd doss)


On 03/03/2015 12:42 AM, Anders Hougaard wrote:
> Thanks Doug.
> Any suggestions for a more appropriate design?
> I guess this is a common situation where two or more parameters only
> apply to one group (in this case patients) and are zero or constant in
> the other.
>
> Best,
> Anders
>
> 2015-03-02 21:21 GMT+01:00 Douglas N Greve <gr...@nmr.mgh.harvard.edu>:
>> It looks like the 4th column is always -0.9 and the 6th col is always
>> -9.138 which makes them redundant with column 2 and causes the error.
>> doug
>>
>> On 03/02/2015 03:16 PM, Anders Hougaard wrote:
>>> Dear Doug,
>>>
>>> Thanks for the answer. I deliberately chose not to demean.
>>> With demeaning I get the same error (see below). Any suggestions?
>>>
>>> Best,
>>> Anders
>>>
>>> Design matrix ------------------
>>>    1.000   0.000  -0.600   0.000   17.137   0.000;
>>>    1.000   0.000  -0.800   0.000   11.962   0.000;
>>>    1.000   0.000   7.100   0.000   11.190   0.000;
>>>    1.000   0.000   0.700   0.000   30.736   0.000;
>>>    1.000   0.000   3.100   0.000   23.735   0.000;
>>>    1.000   0.000   0.100   0.000   21.121   0.000;
>>>    1.000   0.000   1.100   0.000   12.395   0.000;
>>>    1.000   0.000   2.100   0.000   24.912   0.000;
>>>    1.000   0.000   0.100   0.000  -8.021   0.000;
>>>    1.000   0.000  -0.700   0.000   8.877   0.000;
>>>    1.000   0.000   0.100   0.000   0.893   0.000;
>>>    1.000   0.000   7.100   0.000   29.849   0.000;
>>>    1.000   0.000  -0.400   0.000  -1.825   0.000;
>>>    1.000   0.000   2.100   0.000   14.361   0.000;
>>>    1.000   0.000   2.100   0.000   7.267   0.000;
>>>    1.000   0.000   0.100   0.000   34.169   0.000;
>>>    1.000   0.000   0.100   0.000   30.500   0.000;
>>>    1.000   0.000   1.100   0.000   9.219   0.000;
>>>    1.000   0.000   0.100   0.000   26.270   0.000;
>>>    1.000   0.000   1.100   0.000   2.320   0.000;
>>>    1.000   0.000   0.100   0.000   22.325   0.000;
>>>    1.000   0.000   0.100   0.000   13.323   0.000;
>>>    1.000   0.000   1.100   0.000  -2.628   0.000;
>>>    1.000   0.000   7.100   0.000   12.379   0.000;
>>>    1.000   0.000   1.100   0.000  -2.581   0.000;
>>>    1.000   0.000   0.100   0.000  -4.492   0.000;
>>>    1.000   0.000   1.100   0.000   11.549   0.000;
>>>    1.000   0.000   0.100   0.000  -8.413   0.000;
>>>    1.000   0.000   1.100   0.000   9.663   0.000;
>>>    1.000   0.000   1.100   0.000   8.655   0.000;
>>>    1.000   0.000   1.100   0.000   25.778   0.000;
>>>    1.000   0.000   3.100   0.000   2.739   0.000;
>>>    1.000   0.000   0.100   0.000   21.745   0.000;
>>>    1.000   0.000  -0.400   0.000  -5.762   0.000;
>>>    1.000   0.000   0.100   0.000   14.969   0.000;
>>>    1.000   0.000   0.100   0.000   3.535   0.000;
>>>    1.000   0.000   0.100   0.000  -0.287   0.000;
>>>    1.000   0.000   1.100   0.000  -2.283   0.000;
>>>    1.000   0.000   0.100   0.000  -0.257   0.000;
>>>    1.000   0.000   0.100   0.000  -1.207   0.000;
>>>    1.000   0.000   0.100   0.000   1.140   0.000;
>>>    1.000   0.000   0.600   0.000  -4.829   0.000;
>>>    1.000   0.000   1.100   0.000   2.213   0.000;
>>>    1.000   0.000   0.100   0.000   2.503   0.000;
>>>    1.000   0.000   0.600   0.000   3.193   0.000;
>>>    1.000   0.000   0.100   0.000   10.361   0.000;
>>>    1.000   0.000   0.100   0.000   3.546   0.000;
>>>    1.000   0.000   6.600   0.000   12.532   0.000;
>>>    1.000   0.000   0.100   0.000  -1.286   0.000;
>>>    1.000   0.000   0.100   0.000  -5.428   0.000;
>>>    1.000   0.000  -0.400   0.000  -1.661   0.000;
>>>    1.000   0.000  -0.800   0.000   21.444   0.000;
>>>    1.000   0.000   0.100   0.000   21.517   0.000;
>>>    1.000   0.000  -0.600   0.000   8.515   0.000;
>>>    1.000   0.000   0.100   0.000   19.530   0.000;
>>>    1.000   0.000   1.100   0.000   1.824   0.000;
>>>    1.000   0.000   0.100   0.000   15.795   0.000;
>>>    1.000   0.000   0.600   0.000  -6.140   0.000;
>>>    1.000   0.000   2.600   0.000   0.803   0.000;
>>>    1.000   0.000   0.100   0.000   16.904   0.000;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>>    0.000   1.000   0.000  -0.900   0.000  -9.138;
>>> --------------------------------
>>> 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.
>>>
>>> 2015-03-01 19:41 GMT+01:00 Douglas Greve <gr...@nmr.mgh.harvard.edu>:
>>>> Try demeaning your covariates. By demeaning, I mean to compute the mean
>>>> across all subjects, then subtract the mean from all values (for each
>>>> covariate).
>>>> doug
>>>>
>>>>
>>>>
>>>> On 3/1/15 2:40 AM, Anders Hougaard wrote:
>>>>
>>>> Dear all,
>>>>
>>>> I'm comparing cortical thickness of a group of patients to a group of
>>>> controls.
>>>> I want to regress out the effects of gender, age, disease severity and
>>>> disease duration.
>>>> For the controls, the last two parameters equals zero.
>>>>
>>>> When running mri_glmfit, i get ERROR: matrix is ill-conditioned or
>>>> badly scaled, condno = 1e+08
>>>> See the design matrix attached. My contrasts are set as
>>>> 1 -1 0 0 0 0 0 0 0 0
>>>> and
>>>> -1 1 0 0 0 0 0 0 0 0
>>>>
>>>> What should I do to improve this?
>>>>
>>>> All the best,
>>>> Anders
>>>>
>>>>
>>>>
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>> --
>> Douglas N. Greve, Ph.D.
>> MGH-NMR Center
>> gr...@nmr.mgh.harvard.edu
>> Phone Number: 617-724-2358
>> Fax: 617-726-7422
>>
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-- 
Douglas N. Greve, Ph.D.
MGH-NMR Center
gr...@nmr.mgh.harvard.edu
Phone Number: 617-724-2358
Fax: 617-726-7422

Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2
www.nmr.mgh.harvard.edu/facility/filedrop/index.html
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