Thank you so much Guillaume! I will also look at the documents you
recommended to Isadora.
Thank you again!
Verónica
On 6 October 2016 at 18:23, Guillaume Flandin <[log in to unmask]> wrote:
> Dear Verónica,
>
> your initial model was ill-specified and its estimation was probably
> becoming sensitive to numerical precision errors that occur, eg, with
> multithreading.
> For the contrast you are looking at in your screenshot, you could have
> used a paired t-test with data for that group alone, or, equivalently, a
> one-sample t-test of the difference between the two levels of your
> within-subject factor D for that group.
> Also look at previous emails on this list, such as the thread with
> Isadora yesterday, to see how to use models with partitioned error.
>
> Best regards,
> Guillaume.
>
>
> On 06/10/16 12:15, Verónica García wrote:
>> Dear Guillaume,
>>
>> Thank you so much for your reply. We have done your second suggestion
>> and now the results are the same changing the MATLAB version. So the
>> old model was not stable. I have attached the results of one of them
>> (newResults.jpg). There are much less significant voxels.
>> Nevertheless, there are two clusters that are located very close to
>> the first two clusters of the previous results I sent you. I still
>> wonder why changing the MATLAB version changed the results because I
>> think the estimation is an analytical solution, doesn´t it? Maybe some
>> functions in MATLAB have slightly been changed (the precision was the
>> same, I checked it).
>>
>> We wanted to create a linear model similar to those found in some
>> statistics books (see anova_model.jpg). It´s true that the model was
>> very complex for the number of images we have.
>>
>> Thank you again.
>>
>> Best regards,
>>
>> Verónica
>>
>>
>> On 5 October 2016 at 17:53, Guillaume Flandin <[log in to unmask]> wrote:
>>>
>>> Dear Verónica,
>>>
>>> there are many earlier posts on this mailing list about how to proceed
>>> here. The recommended way would be to specify several second level
>>> models, for each question you have (main effect, interaction, ...).
>>>
>>> Otherwise, what you can do here with the flexible factorial design is to
>>> specify three factors (simplifying your two between-subject factors with
>>> two levels each to a single factor with four levels):
>>> * subject: equal, independent
>>> * group: 4 levels, unequal, independent
>>> * cond: 2 levels, equal, dependent
>>> then enter:
>>> * main effect: subject [1]
>>> * interaction: group x cond [2 3]
>>> The design matrix and covariance components will be much simpler and it
>>> is likely that the results will be less dependent to the MATLAB version
>>> you are using.
>>>
>>> Best regards,
>>> Guillaume.
>>>
>
> --
> Guillaume Flandin, PhD
> Wellcome Trust Centre for Neuroimaging
> University College London
> 12 Queen Square
> London WC1N 3BG
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