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
>
>
>
>
>
>
>
>
>
>
>
>
>
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>
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