Dear Donald,
thank you very much for the help!
After calculating the contrasts, some new questions came up
(especially related to OrthoView). Sorry for bothering you again, but
hope you can help.
1. Why you set "I.Cons(x).Levs=2" to "2"?
2. Is it possiple to display the number of voxels of a cluster in OrthoView?
3. Is there an opportunity to plot effect sizes of defined ROIs in
OrthoView. Up to now I did that by rfxplot, but I find no way to
realize this with the data of the GLM Flex analysis.
4. If I tried to find local maxima via right-click and "Go to local
maxima" always the following error occurs: ??? Undefined function or
method 'neighbors' for input arguments of
type 'double'. Error in ==> OrthoView>gotoMinMax at 2298
neigh = neighbors(currloc,Obj(vn).h.dim);
What is the problem?
5. You recommended to take imcalc for calculation of the F-contrasts (T^2).
I tried this: took 1 of the T-maps as input file and set current item
expression to i^2
The process failed because an "incompatible image" has been produced.
Could you see the mistake?
6. Why you set ET to 1 in the following contrast. Isn't it a
withinn-subject comparison?
Any condition different than 0:
I.Cons(x).Groups={columns representing factor 2} [4 5]
I.Cons(x).Levs=0;
I.Cons(x).ET=1;
Thank's a lot in advance!
Anja
2012/11/27, MCLAREN, Donald <[log in to unmask]>:
> The F-contrast is simply T^2 with DF(1,df of T) when then are only 2
> levels being compared. For simplicity we GLM Flex only reports the T.
> You could create the F by using imcalc can squaring the T-statistic
> image.
>
> If you use OrthoView to view the results, then you can see the +/-
> values. When +- values are both shown, the test is treated as
> two-tailed and will be identical to the F-test.
>
> Best Regards, Donald McLaren
> =================
> D.G. McLaren, Ph.D.
> Research Fellow, Department of Neurology, Massachusetts General Hospital
> and
> Harvard Medical School
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Website: http://www.martinos.org/~mclaren
> Office: (773) 406-2464
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> On Mon, Nov 26, 2012 at 6:25 PM, MCLAREN, Donald
> <[log in to unmask]> wrote:
>> Please see in line responses. Sorry for the delay.
>>
>> On Fri, Nov 2, 2012 at 8:59 AM, Anja Dietrich <[log in to unmask]>
>> wrote:
>>> Sorry for the empty message. Here is the question:
>>>
>>> Dear experts,
>>>
>>> since there is no opportunity to compare groups in a flexible factorial
>>> design in spm, I set up a mixed design ANOVA in GLM flex (see attachment
>>> for the design matrix).
>>>
>>> The model consists of 3 factors:
>>> group (3 levels)
>>> condition (2 levels)
>>> the obligatory subject factor
>>>
>>> and a group/ condition interaction
>>>
>>> Because contrast definition differs from that in SPM, I would be very
>>> happy if one can help me to define the contrasts correctly.
>>>
>>> First of all I'd like to define a giant f contrast to check if any column
>>> of the design matrix differs from 0.
>>
>>>>>> This doesn't make any sense to do. I presume you want to test if any
>>>>>> of your group/conditions are different than 0. Here is how to do it.
>>
>> Any group different than 0:
>> I.Cons(x).Groups={columns representing factor 1} [1 2 3]
>> I.Cons(x).Levs=0;
>> I.Cons(x).ET=1;
>>
>> Any condition different than 0:
>> I.Cons(x).Groups={columns representing factor 2} [4 5]
>> I.Cons(x).Levs=0;
>> I.Cons(x).ET=1;
>>
>> Any group/condition pair different than 0:
>> I.Cons(x).Groups={columns representing the interaction} [6 7 8 9 10 11]
>> I.Cons(x).Levs=0;
>> I.Cons(x).ET=1;
>>
>>>
>>> Further I'd like to create F and t contrasts like the following:
>>> Group1Condition1 / Group2Condition1
>>
>> did you mean less than?? Group1Condition1 < Group2Condition1
>> I.Cons(x).Groups={column for G2C1 then G1C1} [8 6]
>> I.Cons(x).Levs=2;
>> I.Cons(x).ET=1;
>>
>>> Group1Condition1 > Group2Condition1
>>
>> I.Cons(x).Groups={columns for G1C1 then G2C1} [6 8]
>> I.Cons(x).Levs=2;
>> I.Cons(x).ET=1;
>>
>> ET is 1 for both because these are between-subject comparisons.
>> [ ] indicates what columns should be in the { }.
>>
>>
>>>
>>> Thank's a lot in advance!
>>>
>>> Anja
>>>
>>>
>>>
>>
>> Hope this answers your questions.
>
