Posted by
Ryan on
Sep 27, 2018; 2:39am
URL: http://spssx-discussion.165.s1.nabble.com/Mixed-Model-Repeated-Measures-tp5726021p5736719.html
Carol,
The short answer is no—You do not always need to specify random intercepts with repeated measures data. The long answer deals with the need to model dependencies in your data through the random cov matrix and/or the residual cov matrix.
I cannot determine why you are receiving non-convergence from here, but it could very well be that you are specifying a model that is too complex for the data at hand.
Ryan
Sent from my iPhone
> On Sep 25, 2018, at 11:33 PM, parisec <
[hidden email]> wrote:
>
> I just tried that option and got a non-convergence warning. I can change the
> AR1 and make it work but i i'm wondering if I really need this in the
> model. Is there a definitive diagnostic i'm missing here?
>
> thanks.
>
>
> Ryan Black wrote
>> Carol,
>>
>> It is possible to model correlated residuals which follow a first-order
>> autoregressive structure, conditional on subject-specific intercepts. You
>> would capture this by specifying REPEATED and RANDOM statements as
>> follows:
>>
>> /RANDOM intercept SUBJECT(subjectid)
>> /REPEATED = time | SUBJECT(subjectid) COVTYPE(AR1)
>>
>> See:
>>
>> Littell, RC; Pendergast, J; Natarajan, R (2000) Modelling covariance
>> structure in the analysis of repeated measures data. Statistics in
>> Medicine
>> 19:1793-1819.
>>
>> Ryan
>
>
>
>
>
> --
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