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Dear Hans, > Thanks for the reply. It does seem pretty straightforward. However, > the devil is often in the details. Oh dear! > (1) Let the residual vector for a voxel be r = [r_1,r_2,...r_n] > (after fitting the null only model, n = timepoints in the model) > > (2) This "raw" residual vector r is permuted. It is *not* modified > or standardized in any fashion before permuting. I don't know the "details" well enough to say for certain. But I don't see any reason why they would be modified in any way. Why, have you observed some "funny" values? Jesper > > Is that accurate? > > Thanks, > Hans. > > > On Thu, Jul 2, 2009 at 11:02 AM, Jesper Andersson <[log in to unmask] > > wrote: > Dear Hans, > >> Thanks, but I am interested in a more detailed answer about exactly >> how the residuals are permuted :). Here's my question again for >> reference: >> >> --- >> I have a question regarding randomise residuals. As per my >> understanding randomise fits the null model only to the data and >> calculates null only residuals. Then it permutes these null only >> residuals and adds them back onto the fitted null model to create >> realizations of null data. My question is: >> >> Are these null only residuals modified (or standardized) in any way >> before permuting them? If so, exactly how? > > I'm no expert on randomise, but is seems pretty straightforward to me. > > Let's say you have a model with two groups and age as a covariate, > and that your contrast happens to be [1 0], i.e. you are interested > in effects of group after affects of age have been removed. > > By virtue of you contrast not spanning the age regressor randomise > can identify it as a "confound" and regress out all effects of age. > What is left is the residuals, i.e. that which in our model can be > explained either by group or not at all. randomise will the permute > these residuals (equivalent to permuting the group indicators), for > each permutation fitting the GLM to all voxels and calculating the t- > statistic. Depending on your inference it may then save away maximum > voxel, maximum cluster size etc, thus building an empirical > distribution of that statistic. > > I hope this is clear? > > Good Luck Jesper > >> --- >> >> Thanks, >> Hans. >> >> >> >> On Thu, Jul 2, 2009 at 10:48 AM, Matthew Webster <[log in to unmask] >> > wrote: >> Hello Hans, >> Randomise separates the input model into tested >> and nuisance effects, the input data is adjusted for the nuisance >> effects and this adjusted data is then fitted to the full permuted >> model.. >> >> Many Regards >> >> Matthew >> >> Hi FSL experts, >> >> I have a question regarding randomise residuals. As per my >> understanding randomise fits the null model only to the data and >> calculates null only residuals. Then it permutes these null only >> residuals and adds them back onto the fitted null model to create >> realizations of null data. My question is: >> >> Are these null only residuals modified (or standardized) in any way >> before permuting them? If so, exactly how? >> >> Thanks, >> >> Hans Tissot. >> > >