 |
 |
Hi Nancy Li, To do a statistical comparison of the two groups, in the unpaired 2-sample t-test, the contrasts are [1 -1] and [-1 1]. To have the overall mean (across both groups), then use [1 1] for positive and [-1 -1] for negative. All the best, Anderson On 19 January 2017 at 14:21, Nancy Li <[log in to unmask]> wrote: > Hi Anderson, > > I want to make it clear, if there are two groups, we want to compare the > mean activation of two groups. Just said before we should add (1,0) and (0 > 1) to the dsign.con. But if we want to get the whole mean activaion(two > groups together), we should add (1 1) and (-1 -1) to the design.con, is it > right? Thank you so much for further confirmation. > > Best > > Nancy > > > > > > 在 2017-01-19 18:15:11，"Anderson M. Winkler" <[log in to unmask]> 写道： > > Hi Nancy, > > Please see below: > > > On 18 January 2017 at 14:06, Nancy Li <[log in to unmask]> wrote: > >> Hi Anderson, >> >> Great, It is what I want to know. So in the randomise command It seemed >> like this way:" >> >> randomise -i 4D image -o rando -d design.mat -t design.con -n 5000 >> -T , design.con may be: 1 0 >> >> >> >> 0 1 >> >> >> >> 1 -1 >> >> >> -1 1 >> >> >> Is it right? >> > > > Yes, contrasts are fine. For the overall mean activation, however, you > need to add two contrasts more: [1 1] and [-1 -1]. In randomise, you'd test > these latter two by adding the option -1, that does sign-flippings. > > > >> And How about one sample ttest, it also the way to get the mean >> activation, but how to understand "There should be no repeated measures, >> i.e., there should only be one image per subjec ", It equals one subject >> only have one volume? Need your help to confirmation. Thank you so so much! >> > > Yes, one volume per subject. > > All the best, > > Anderson > > > >> >> Best >> >> Nancy >> >> >> At 2017-01-18 17:26:21, "Anderson M. Winkler" <[log in to unmask]> >> wrote: >> >> Hi Nancy, >> >> For the mean activation, using the same design shown in the Wiki, create >> a contrast that is [1 1]. >> >> All the best, >> >> Anderson >> >> >> On 17 January 2017 at 14:45, Nancy Li <[log in to unmask]> wrote: >> >>> Hi Anderson, >>> >>> Thank you so much for sincere reply. Could you help me make sure if I >>> want to get the mean activation of two different group, It could be done >>> through one sample ttest(seperate group) or unpaired two group ttest, the >>> results are same, is it right? Looking forward to more instruction. Thanks >>> a lot! >>> >>> Best >>> >>> Nancy >>> >>> >>> >>> >>> >>> >>> At 2017-01-16 18:26:55, "Anderson M. Winkler" <[log in to unmask]> >>> wrote: >>> >>> Hi Nancy Li, >>> >>> An example of a two-sample t-test is in the manual, please see here: >>> https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#Two-Group_D >>> ifference_.28Two-Sample_Unpaired_T-Test.29 >>> >>> All the best, >>> >>> Anderson >>> >>> >>> On 13 January 2017 at 18:29, Nancy Li <[log in to unmask]> wrote: >>> >>>> Hi Colin Hawco, >>>> Great thanks. It is more clear. We could also get the result of mean >>>> activation in two group contrast by design matris(1 0; 0 1; 1 -1; -1 1). >>>> In my opinion it is same to the result of seperate one sample test, is it >>>> right? Looking forward to your confirmation. Thanks a lot! >>>> Best >>>> Nancy >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> At 2017-01-14 01:43:04, "Colin Hawco" <[log in to unmask]> wrote: >>>> >>>> Ahh I see. The means of permuting is different. Instead of randomizing >>>> group assignments, half the data is sign flipped (i.e. multiplied by -1). >>>> In this case, the data should follow a distribution centered around zero. >>>> >>>> >>>> >>>> Everything in that wiki vis a vis the estimatablility of the true >>>> p-value remains true. >>>> >>>> >>>> >>>> If you can possibly run 5000 permutations you should do so. if for no >>>> other reason than to please reviewer. I would recommend a minimum of 1000 >>>> or 2000 permutations. >>>> >>>> >>>> >>>> >>>> >>>> Colin Hawco, PhD >>>> >>>> Neuranalysis Consulting >>>> >>>> Neuroimaging analysis and consultation >>>> >>>> www.neuranalysis.com >>>> >>>> [log in to unmask] >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> *From:* FSL - FMRIB's Software Library [mailto:[log in to unmask]] *On >>>> Behalf Of *Nancy Li >>>> *Sent:* January-13-17 11:31 AM >>>> *To:* [log in to unmask] >>>> *Subject:* Re: [FSL] Question about randomise of one sample ttest >>>> >>>> >>>> >>>> Hi Colin Hawco, >>>> >>>> >>>> >>>> Thank you so much. It may be understood easily if there is two group >>>> test. But in one sample test we want to get the mean activation, there is >>>> no permutation. How to undrestand the impact of different number of >>>> permutation. looking forward to your further help. >>>> >>>> Best >>>> >>>> >>>> >>>> Nancy >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> At 2017-01-14 00:19:51, "Colin Hawco" <[log in to unmask]> wrote: >>>> >>>> more permutations give you a more accurate estimation of true >>>> significance. It also defines the possible increments and confidence limits >>>> of the calculated p-value, with more permutations providing greater >>>> confidence. >>>> >>>> >>>> >>>> see: >>>> >>>> https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/Theory >>>> >>>> >>>> >>>> >>>> >>>> Good luck, >>>> >>>> >>>> >>>> Colin Hawco, PhD >>>> >>>> Neuranalysis Consulting >>>> >>>> Neuroimaging analysis and consultation >>>> >>>> www.neuranalysis.com >>>> >>>> [log in to unmask] >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> *From:* FSL - FMRIB's Software Library [mailto:[log in to unmask]] *On >>>> Behalf Of *Nancy Li >>>> *Sent:* January-13-17 11:03 AM >>>> *To:* [log in to unmask] >>>> *Subject:* [FSL] Question about randomise of one sample ttest >>>> >>>> >>>> >>>> Helo experts and folks, >>>> >>>> >>>> >>>> I want to get results of mean group activation using randomise of one >>>> sample. The default permutation is 5000 in the command. My question is "is >>>> there difference between 500 and 5000 permuttaion of one sample test and >>>> why?" Looking forward to your help or may suggest some paper about this >>>> question. Thanks a lot in advance. >>>> >>>> >>>> >>>> Best >>>> >>>> >>>> >>>> Nancy >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>> >>> >>> >>> >>> >> >> >> >> >> > > > > >