Hi Jorge, Thank you for your careful explanations, it did help :) Regards, -Ting
On Wed, Dec 12, 2012 at 11:35 PM, jorge luis <jbernal0...@yahoo.es> wrote: > Hi Ting > > Just use the default procedure. We limited the number of iterations for > the estimation procedure to 20 for the mass-univariate analysis. If the > model for the covariance is correct, that is to say, you have correctly > specified the set of random effects that is supported by your data then the > estimation at each vertex usually converges in a few iterations (between 5 > and 9). > > In your case, you have only two repeated measures in your data. In that > scenario, you should only use a single random effect for the intercept term > (or maybe for the head_motion time-varying covariate, see who produces > better convergence results). You cannot use a model with two random > effects when you only have two repeated measures because you don't have > enough degrees of freedoms in your data. > For example, if you had to estimate a completely unstructured covariance > then you would only need to estimate three covariance parameters for two > repeated measures, on the other hand if you impose structure on the > covariance using the mixed effects model with two random effects then you > are estimating four covariance components which will impose too much > structure on the covariance and the estimation algorithm will fail to > converge most of the time. > > Finally, even when the set of random effects is correctly specified the > iterative estimation procedure may not converge at several vertices (eg. > 10% or may be more of the total number of vertices under analysis). > > Hope this helps. > > Best > -Jorge > > > > ------------------------------ > *De:* ting xu <xutin...@gmail.com> > *Para:* freesurfer@nmr.mgh.harvard.edu > *Enviado:* Miércoles 12 de diciembre de 2012 9:22 > *Asunto:* [Freesurfer] issue about LME Matlab tools > > > Dear all, > > A few questions come to me when I used LME Matlab toolbox. Then Jorge > kindly answered my questions and he also asked me to post our discussion to > Freesurfer list so everyone knows what's going on here :) > > Cheers, Ting > ------------------------------------------------------------------- > *Subject: *Re: Re: issue about LME Matlab tools > *Sent: *Tue, Dec 11, 2012 21:41:26 > Hi Ting > Please could you post this question to the Freesurfer list, so other > people can benefit from this discussion? > I'll answer you ASAP. > Best > -Jorge > ------------------------------------------------------ > *Subject: *Re: Re: issue about LME Matlab tools > *Sent: *Wed, Dec 12, 2012 6:44:36 AM > > Hi Jorge, > > Thank you for your explanation in more details. I followed your > suggestion, set the convergence epsilon to 10^-5. It truly improved. I am > now running the whole brain data, estimations in several voxels were not > converge after 50 iterations. Given that iterations takes more > computational time, how many iterations do you recommend and what do you > usually do if it still not converge? > Thank you again:) > > Warmly regards, > > Ting > ------------------------------------------------------ > *Subject:* Re: issue about LME Matlab tools > *Sent: *Wed, Dec 12, 2012 00:50:35 > > Hi Ting > > You needed more iterations to make the algorithm stop closer to the > optimal values. Try: > > statsFS= lme_fit_FS(X,[1 2],Y,ni,10^-5); > > or the EM algorithm > > statsEM = lme_fit_EM(X,[1 2],Y,ni,10^-10); > > Neither of these algorithms can be guaranty to converge but I have found > the FS algorithm to be the most robust and fast. > > For the mass-univariate setting we limited the number of iterations for > the FS algorithm to only 20 due to computational time. > > Note that for only two repeated measures (as in your data) compound > symmetry (a model with a single random effect for the intercept term) > likely holds for the covariance matrix among the repeated measures. > Although a likelihood ratio test here comparing the model with one random > effects against the model with two random effects is barely significant it > will not likely survive a multiple comparisons correction. > > You can not impose structure on D in our toolbox nor it is recommended for > general longitudinal data. > > Let me know any doubt you might have. > > Best > -Jorge > ------------------------------------------------------ > *Subject: *issue about LME Matlab tools > *Sent: *Tue, Dec 11, 2012 14:47:14 > > Dear Dr. Sabuncu > > Your recent work "Statistical Analysis of Longitudinal Neuroimage Date > with Linear Mixed Effects Models" provided us a great matlab toolbox to > apply Mixed Model, especially for imaging data. Now, I am investigating > the intraclass correlation based on this toolbox. Sometime I found the > function "lme_fit_FS" could not get the accurate estimations. I think the > problem may be relevant to the initial value from OLS method, in the case > that the within-subject variability estimated from "lme_fit_init" is very > close to zero. > I attached an example. Y is test-retest imaging data for one voxel, X is > design matrix including intercept, head_motion, age, gender. I set random > effect for intercept and head_motion. I also attached result from SAS and > the model is ok. > > stats = lme_fit_FS(X,[1,2],Y,ni); > > Here I'm wondering is there any way to fixed this in matlab? > > PS, though you have mentioned that no imposed structure on D (covariance > matrix of random effect), I am curious if it is possible to define the > structure of D in this toolbox. > > Thanks for your kind attention and look forward to your reply soon. > > Regards, Ting > > > > > > > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > The information in this e-mail is intended only for the person to whom it > is > addressed. If you believe this e-mail was sent to you in error and the > e-mail > contains patient information, please contact the Partners Compliance > HelpLine at > http://www.partners.org/complianceline . If the e-mail was sent to you in > error > but does not contain patient information, please contact the sender and > properly > dispose of the e-mail. > > > -- Xu,Ting Postdoc Laboratory for Functional Connectome and Development (LFCD) Institute of Psychology, Chinese Academy of Sciences Beijing, China, 100101 Email: xutin...@gmail.com; xut...@psych.ac.cn
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