 |
 |
Hi Jonathan > Dear John, > I have a design matrix with 3 parametric regressors PA, PB and PC (in >> that order) >> >> Now, i create a second DM with the following regressors PA,PB,PD,PE. >> >> Whereas the results for PA remain the same, the results for PB change. >> One reason could be that PE and PB are somehow correlated; >> however, I thought that the correlation would have an effect on PE and >> not on PB, given that PB appears first in the DM >> >> Could somebody explain why results from PB in the first DM are different >> from results of PB in the second DM? > > Most likely you are right - the correlation of PB with other > regressors (PC vs. PD/PE) is not the same, which will give you > different results. > (Also it's important to know what you mean by "results" - the beta > estimate for PB, or the t statistic from a contrast on PB? The > inclusion of additional regressors may not only affect the estimate of > the PB beta, but also the error term, which would give you a different > t statistic image.) given the orthogonalization, this has to be the same Beta value, but the results will differ since residuals will be smaller > > You can look at the correlation between columns in your design matrix > by clicking the "review" button, and then under the "design" menu > select "design orthogonality". > Alternatively you can look at the modulators from your design matrix > as entered, which is stored in SPM.xX.X. Something like this: > load SPM > PB = SPM.xX.X(:,2); > PC = SPM.xX.X(:,3); > [r, p] = corrcoef(PB, PC); > plot(PB, PC, 'o'); remember that corrcoef is the mean centered correlation whereas in spm that is just the cos(angle) between regressors (not such a big difference but worthwhile remembering - cos(angle) = corr only is the vectors are normalized) Cyril -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.