[R-sig-ME] Mixed-model-binary logistic model with dependence between individual repeated measures

[Jarrod Hadfield]

Hi All,

I think one issue is going to be constraining the within-individual  
residual structure to a correlation matrix, since the diagonal  
elements of the covariance matrix cannot be estimated from binary  
data. A residual structure of the form rcov=~cor(time):units  does  
this in MCMCglmm, but I would not advocate it if there are many time  
points because there will be N*(N-1)/2 parameters, rather than 1  
parameter with an appropriate AR structure. N is the number of time  
points.

Cheers,

Jarrod



Quoting Gavin Simpson <gavin.simpson at ucl.ac.uk>:

> On Fri, 2011-01-07 at 11:49 -0500, Ben Bolker wrote:
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>>  I do not
>> > want to assume that. In addition I would like to be able to chose
>> > other distributions than the normal for my random effect, which is
>> > not possible in SAS (proc NLMIXED).
>>
>>   It's not possible in R either as far as I know.
>
> I was reading the article in the latest issue of the R Journal on the
> hglm package, and although I was only giving it a cursory scan over
> lunch it looks like it might be able to fit the sort of model implied
> here; random effects distributed as a member of the exponential family.
>
> G
>
>>
>> The generalized estimating
>> > equation packages are probably not an option as I do not whant
>> > marginal models. I will look at the references you suggested. Thank
>> > you. /Anna
>> >
>>
>>   If you want a non-marginal model with non-normal random effects and
>> within-individual correlation structures other than compound symmetry
>> (i.e. simple block structures), you are probably going to have to
>> construct your own solution with WinBUGS or AD Model Builder or ... ? If
>> you're lucky, MCMCglmm may be able to do what you want -- I would check
>> it out. (Molenbergh and Verbeke's book on longitudinal models describes
>> approaches for non-normal random effects, but in the context of LMMs
>> (i.e. normally distributed errors) -- they may have done something to
>> extend this stuff to GLMMs more recently.  It's possible that someone
>> out there has done what you want and encapsulated it into a canned
>> package, but I doubt it.
>>
>>    cheers
>>     Ben Bolker
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