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.