Hi Cathy
If you only have three groups in your
data (Control subjects, High risk patients, Ill patients) then you should drop
the
“Controls versus all others and controls*time interaction” terms
from your design matrix. Otherwise it is ill-conditioned. One way to
check your design matrix is by performing a simple univariate
analysis using data from a single vertex, eg. at vertex 1000:
lhstats = lme_fit_FS(X,[1
2],Y(:,lhcortex(1000)),ni);
and
check the behavior of the optimization procedure. Make
sure that both your design matrix X and the cortical thickness data Y are
ordered in a
way that they contain all the repeated measures for the first
subject (ordered by time), then all the repeated measures for the second
subject and
so on. The first element of the vector ni must indicate the number of
repeated measures in the design matrix X for the first subject, the
second element of that vector must indicate the number of repeated
measures in the design matrix for the second subject and so on.
Finally you should include the data Y as an argument of the fitting
function:
lhstats = lme_mass_fit_vw(X,[1
2],Y,ni,lhcortex);
This will fit a linear mixed effects model with two random effects (intercept
and time).
Best
-Jorge
>
> De: Catherine Bois
>Para: Jorge
>CC: freesurfer@nmr.mgh.harvard.edu
>Enviado: Lunes 24 de junio de 2013 5:14
>Asunto: Re: [Freesurfer] lme issues + failure to converge
>
>
>Hi,
>
>So, the model we fitted was one with only 1 random effect apparently
>(the intercept term), so we used the script you sent for older
>versions of matlab;
>
>lhstats = lme_mass_fit_vw1(X,[1],ni,lhcortex)
>
>The model took 861 minutes to run, and at the end it now said that the
>model failed to converge at ca 85% of locations...
>
>The matrices columns are as follows; the intercept term, time (I guess
>due to only using one random effect in our model it will be treated as
>a fixed effect by Matlab?), High risk versus all other patients, high
>risk versus time interaction, Controls versus all others,
>controls*time interaction, ill*all others, ill*time interaction,
>Gender, Age. There are ca 170 subjects, with varying and unbalanced
>repeated measures (ranging up to 5/subject).
>
>Since we would like to fit a model with both intercept and time (and
>in the long run) also family as random effects, perhaps we need to use
>the spatiotemporal models instead to make our model converge? If so,
>will the scripts for fitting these in older versions of Matlab be
>available soon?
>
>Have we missed an obvious step which is making our model not converge
>at 85% of the positions?
>
>Thank you for your help,
>
>Best Wishes,
>
>Cathy
>
>
>X = [ones(length(M),1) M(:,1) Mat(:,1) Mat(:,1).*M(:,1) Mat(:,2)
>>> Mat(:,2).*M(:,1) Mat(:,3) Mat(:,3).*M(:,1) M(:,3)-1 M(:,4)];
>
>
>Quoting Jorge on Fri, 21 Jun 2013 10:42:11 -0700:
>
>> Hi Cathy
>>
>> You should put a comma between X and [1 2]
>>
>>> lhstats = lme_mass_fit_vw(X, [1 2],ni,lhcortex);
>>
>> The model with a single random effect for the intercept term must
>> always converge:
>>
>>> lhstats = lme_mass_fit_vw(X, [1],ni,lhcortex);
>>
>>
>> Can you tell me with words what the columns of your design matrix
>> are? How many subjects and how many repeated measures for each
>> subject do you have?
>>
>> Best
>> -Jorge
>>
>>
>>
>> Sent from my iPad
>>
>> On Jun 21, 2013, at 4:53, Catherine Bois wrote:
>>
>>> Dear Jorge/freesurfer group,
>>>
>>> I am using the scripts you sent me that do not require the newer
>>> version of matlab, and now using a computer that has the Statistics
>>> toolbox. I can get the scripts (with the suffix 1) to run with;
>>> lhstats = lme_mass_fit_vw(X[1 2],ni,lhcortex);) however it "fails to
>>> reach convergence" at most vertices. We have tried simplifying the
>>> model to include only one random effect at a time, however the problem
>>> persists. Our design matrix is as follows;
>>>
>>> X = [ones(length(M),1) M(:,1) Mat(:,1) Mat(:,1).*M(:,1) Mat(:,2)
>>> Mat(:,2).*M(:,1) Mat(:,3) Mat(:,3).*M(:,1) M(:,3)-1 M(:,4)];
>>>
>>> I have read on the mailing list that non-convergence at some vertices
>>> is normal, however what we are getting far exceeds 10%. Any help on
>>> this matter would be greatly appreciated!
>>>
>>> Best Wishes,
>>>
>>> Cathy
>>>
>>> --
>>> The University of Edinburgh is a charitable body, registered in
>>> Scotland, with regi
