Hi,
I would like to double-check a couple of things:
1. After randomise on TBSS data (a simple group comparison with two contrast
1 -1 and -1 1), if I consider this as a 2-tailed test in displaying the
result of p-values (eg. Maxc_tstat) I view it as 0.975-1 (if the p-value I
want is p<0.05). Is it correct?
2. Demeaning the values in randomise: There are two options:
- to add an additional EV (with "1" only) and add -D in the command line
- to enter already demeaned values into the design.
Is it correct?
3. What will really happen if I don't demean my data (in case I should)?
4. Is the design below correct for the correlation of TBSS data with 1 EV of
some demeaned parameter x? Should I run it with -D or without?
/ContrastName1 x
/NumWaves 1
/NumContrasts 1
/PPheights 6.651000e+03
/RequiredEffect 5.308
/Matrix
1.000000e+00
/NumWaves 1
/NumPoints 17
/PPheights 6.651000e+03
/Matrix
-1.492000e+03
5.159000e+03
8.620000e+02
-1.361000e+03
-9.570000e+02
-1.162000e+03
-1.444000e+03
6.610000e+02
-4.190000e+02
-9.280000e+02
-1.287000e+03
3.091000e+03
2.097000e+03
-1.013000e+03
-1.038000e+03
-6.870000e+02
-7.600000e+01
5. Is this a correct design for correlation of TBSS data with parameter x,
with parameter y as a covariate ("controlling for y")? Do I enter other
parameters in randomise as before (eg. t-value threshold, etc.?)
/NumWaves 2
/NumPoints 17
/PPheights 6.651000e+03 8.000000e+00
/Matrix
-1.492000e+03 0.000000e+00
5.159000e+03 1.000000e+00
8.620000e+02 -4.000000e+00
-1.361000e+03 -4.000000e+00
-9.570000e+02 0.000000e+00
-1.162000e+03 -1.000000e+00
-1.444000e+03 0.000000e+00
6.610000e+02 -1.000000e+00
-4.190000e+02 0.000000e+00
-9.280000e+02 0.000000e+00
-1.287000e+03 2.000000e+00
3.091000e+03 -2.000000e+00
2.097000e+03 -1.000000e+00
-1.013000e+03 4.000000e+00
-1.038000e+03 -1.000000e+00
-6.870000e+02 2.000000e+00
/ContrastName1 x
/ContrastName2 y
/ContrastName3 x(y)
/NumWaves 2
/NumContrasts 3
/PPheights 7.017095e+03 8.046171e+00 8.045551e+00
/RequiredEffect 5.870 5.827 5.827
/Matrix
1.000000e+00 0.000000e+00
0.000000e+00 1.000000e+00
1.000000e+00 1.000000e+00
-7.600000e+01 -3.000000e+00
6. What is the minimum of subjects one needs to perform a decent correlation
analysis in such case?
7. Is it possible to perform a hierarchical multiple regression in glm?
I will be very grateful for your advice.
Best,
Aga
