If you want to assess an interaction (e.g. between group and gender)
then you'll need to include columns in the design matrix that allow for
modeling of the interaction. Your 2nd design matrix only permits
modeling main effects.
cheers,
-Mike H.
On Fri, 2009-12-04 at 11:00 -0500, Jiansong Xu wrote:
> Dear All:
>
> I know this issue has been discussed in the list, but I still have
> questions.
>
> I have 3 groups: A: control; B: patients 1; and C: patients 2.
>
> group A B C
> 1 1 0 0
> 1 1 0 0
> 1 1 0 0
> 1 1 0 0
> 1 1 0 0
> 2 0 1 0
> 2 0 1 0
> 2 0 1 0
> 2 0 1 0
> 2 0 1 0
> 3 0 0 1
> 3 0 0 1
> 3 0 0 1
> 3 0 0 1
> 3 0 0 1
>
> (We have > 170 subjects totally)
>
> Contrast for t- and f- test
>
> A-B: 1 -1 0
> B-A: -1 1 0
> A-C: 1 0 -1
> C-A: -1 0 1
> B-C: 0 1 -1
> C-B: 0 -1 1
> A 1 0 0 F
> B 0 1 0 F
> C 0 0 1 F
>
> I run randomise with following options:
>
> randomise -i input -o output -m mask -d design.mat -t design.con -f
> design.fts -n 5000 -c 2.0 -F 2.0 -V
>
> I got 9 outputs. I know the first 6 is for the 6 t-tests specified in
> above Contrast. My question is: how to interpret the last three
> outputs? Which one is the result of the F test?
>
> I also want to assess if the FA of male and female patients show
> different changes relative to male and female controls, respectively.
> I made following changes for design:
>
> group A B C Gender (0 for females and 1 for males)
> 1 1 0 0 0
> 1 1 0 0 0
> 1 1 0 0 0
> 1 1 0 0 1
> 1 1 0 0 1
> 2 0 1 0 0
> 2 0 1 0 1
> 2 0 1 0 1
> 2 0 1 0 1
> 2 0 1 0 1
> 3 0 0 1 0
> 3 0 0 1 0
> 3 0 0 1 1
> 3 0 0 1 1
> 3 0 0 1 1
>
> Question: How to set the contrast for assessing the interaction of
> gender and group on FA values?
>
>
>
> Best Regards
>
> Jiansong
>
> ===============
> Jiansong Xu, M.D., Ph.D.
> Dept. of Psychiatry
> Yale Medical School
> 2 Church St., Suite 215
> New Haven, CT 06519
> 203-785-5306
> [log in to unmask]
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