Hi,
You do need to include the group membership EV's in your design -
you don't need to use the -e ( "exchangeability-blocks" ) option...
Many Regards
Matthew
> Hi,
> Thank you very much for your reply.
>
> First thing I am testing is where correlation between FA values and
> behavior
> is larger (smaller) in patient and control group.
>
> My design is :
>
> Group EV1 EV2
> 1 2 0
> 1 3 0
> 1 -5 0
> 2 0 -1
> 2 0 -2
> 2 0 -3
>
> C1 1 -1
> C2 -1 1
> Where EV1 and EV2 are demeaned behavioral scores, group 1 is
> controls, group
> 2 patients (my real design includes of course more participants). I
> included
> no F-tests.
>
> My command line would be:
> randomise -i all_FA_skeletonised.nii -o output -m mask1.nii -d
> design.mat -t
> design.con -c 1.5 -D -V
>
> Should I include 2 further Evs, specyfying group membership?
> EV3 EV4
> 1 0
> 1 0
> 1 0
> 0 1
> 0 1
> 0 1
>
> Thank you!
> Cheers,
> Aga
>
> On 3/29/09 7:59 AM, "Steve Smith" <[log in to unmask]> wrote:
>
>> Hi - this is now the "-e" option - see the randomise manual for more
>> information on this. For most simple designs you don't need this -
>> we'd need to know more about your model to be sure though.
>>
>> Cheers, Steve.
>>
>>
>> On 28 Mar 2009, at 15:17, Agnieszka Burzynska wrote:
>>
>>>
>>> Dear all
>>> I found this post below in the archives and wonder whether I need to
>>> use the –g flag for a desing similar to the one below. I dont find
>>> the -g option in the current version of randomise and therefore I am
>>> not sure what to do.
>>>
>>> My second question is: how much it matters to specify a group in the
>>> first “group” column in the Glm-GUI? Is it just for user’s own
>>> information? If I left all inputs with “1” but I specify groups in
>>> the Evs( for EV only one subgroup has non-zero values, etc) , does
>>> this first column still matter?
>>>
>>> Thank you,
>>> Aga
>>>
>>>
>>>>>> On 5 Mar 2008, at 16:55, :
>>>>>>
>>>>>>>> Hi Steve,
>>>>>>>>
>>>>>>>> Firstly I am not very familiar with the mathematics of design
>>> matrix
>>>>>>>> and contrast matrix. I am trying to get myself familiarized
>>> with it,
>>>>>>>> so kindly excuse me if any of my questions sound silly.
>>>>>>>>
>>>>>>>> I have two groups
>>>>>>>>
>>>>>>>> No of Controls: 13
>>>>>>>> No of Patients: 12
>>>>>>>> Behavioral data: Type1 (for both patients and controls)
>>>>>>>> Behavioral data: Type 2 (for both patients and controls)
>>>>>>>>
>>>>>>>> I would like to set up the randomise option in TBSS to do
>>>>>>>> correlation between the FA values and a behavioral measure
>>> (Actually
>>>>>>>> both - separately). There are two types of tests that I would
>>> like
>>>>>>>> to carry out.
>>>>>>>>
>>>>>>>> 1. Test whether the FA of all the subjects in both the
>>> groups
>>>>>>>> correlate with a behavioral measure.
>>>>>>>> 2. Contrast the behavioral correlation between the two
>>> groups.
>>>>>>>>
>>>>>>>> From some of the previous posts I initially assumed that I
>>> needed
>>>>>>>> two design matrices and one contrast matrix for both the
>>> tests. But
>>>>>>>> in the latest version of randomise that I downloaded it said
>>> that
>>>>>>>> confound regressors can be given in the main design matrix
>>> itself.
>>>>>>>> So I am assuming that we need only one design matrix and one
>>>>>>>> contrast matrix for both of the above tests that I had
>>> mentioned
>>>>>>>> above.
>>>>>>>>
>>>>>>>> If the above assumptions I have made are right then I will
>>> move on
>>>>>>>> with the design and contrast matrices that I have for both of
>>> the
>>>>>>>> above tests.
>>>>>>>>
>>>>>>>> Test 2 - Contrast the behavioral correlation between the two
>>> groups.
>>>>>>>>
>>>>>>>> So in this design matrix I had 2 groups and 4 EVs. In the
>>> groups
>>>>>>>> column I gave the value for first 13 subjects as 1 and for
>>> the rest
>>>>>>>> of the 12 subjects to be 2. The matrix given below is the
>>> design
>>>>>>>> matrix that I used for this testing. (b1-b25 is the behavioral
>>>>>>>> values for each of the 25 subjects.)
>>>>>>>>
>>>>>>>> EV1 EV2 EV3 EV4
>>>>>>>> b1 0 1 0
>>>>>>>> b2 0 1 0
>>>>>>>> b3 0 1 0
>>>>>>>> b4 0 1 0
>>>>>>>> b5 0 1 0
>>>>>>>> b6 0 1 0
>>>>>>>> b7 0 1 0
>>>>>>>> b8 0 1 0
>>>>>>>> b9 0 1 0
>>>>>>>> b10 0 1 0
>>>>>>>> b11 0 1 0
>>>>>>>> b12 0 1 0
>>>>>>>> b13 0 1 0
>>>>>>>> 0 b14 0 1
>>>>>>>> 0 b15 0 1
>>>>>>>> 0 b16 0 1
>>>>>>>> 0 b17 0 1
>>>>>>>> 0 b18 0 1
>>>>>>>> 0 b19 0 1
>>>>>>>> 0 b20 0 1
>>>>>>>> 0 b21 0 1
>>>>>>>> 0 b22 0 1
>>>>>>>> 0 b23 0 1
>>>>>>>> 0 b24 0 1
>>>>>>>> 0 b25 0 1
>>>>>>>>
>>>>>>>> From some of the past archives I found that the matrix that
>>> contains
>>>>>>>> the behavioral measures is the main matrix(used with -d) and
>>> the
>>>>>>>> matrix with the group membership information should be taken
>>> as the
>>>>>>>> confound matrix (used with -x).
>>>>>>
>>>>>> No, the group membership, which controls the exchangeability
>>> blocks,
>>>>>> should be passed in using the -g flag. There is no longer any
>>> need for
>>>>>> 'confound' matrices.
>>>
>>
>>
>> ---------------------------------------------------------------------------
>> Stephen M. Smith, Professor of Biomedical Engineering
>> Associate Director, Oxford University FMRIB Centre
>>
>> FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
>> +44 (0) 1865 222726 (fax 222717)
>> [log in to unmask] http://www.fmrib.ox.ac.uk/~steve
>> ---------------------------------------------------------------------------
>
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