R help - Aug 2012 - Which R function for GLMM with binary response, nested random factors with temporal correlation?

Despite lots of investigation, I haven't found any R packages might be
suitable for the following problem. I'd be very grateful for suggestions.

I have three-way nested data, with a series of measures (obs) taken in quick
succession (equal time spacing) from each subject on different days. The
measures taken on the same day are temporally correlated, so I'd like to use
an AR1 correlation structure for those, but treat subjects and days as nested
random factors (random intercept) since there is little temporal correlation
between days. The response is binary.

So I need a GLMM with a correlation structure. I've tried using GEE, but the
R packages can't cope with multilevel nested data. The only R function
I've found that can do this is glmmPQL.

m <- glmmPQL(y ~ f1 * f2 * f3 + (1|subj/day), correlation=corAR1(form
=~obsno|subj/day))

f1 - f3 are fixed factors

However, PQL estimation is not recommended for binary response data. With no AIC
and unreliable p values, model selection seems impossible! So my question is:

1) are there any other functions which are suitable for a GLMM with multilevel
nested random effects and a AR1 correlation structure? Or is MCMC the only
option?
2) to make things more complicated, I'd also like to include a varFunc
variance structure to cope with heterogeneity. Is this possible in ML methods in
R? I'd also like to extend to a multinomial response at a later stage.

GEE seems the best bet, but I come unstuck with the three-way nested factors. 

Thanks for your help.

Have you posted on R-sig-mixed-models? That would be more likely to
yield useful responses than here.

-- Bert

On Tue, Aug 7, 2012 at 3:02 PM, Andrew Digby <andrewdigby at mac.com>
wrote:
>
> Despite lots of investigation, I haven't found any R packages might be
suitable for the following problem. I'd be very grateful for suggestions.
>
> I have three-way nested data, with a series of measures (obs) taken in
quick succession (equal time spacing) from each subject on different days. The
measures taken on the same day are temporally correlated, so I'd like to use
an AR1 correlation structure for those, but treat subjects and days as nested
random factors (random intercept) since there is little temporal correlation
between days. The response is binary.
>
> So I need a GLMM with a correlation structure. I've tried using GEE,
but the R packages can't cope with multilevel nested data. The only R
function I've found that can do this is glmmPQL.
>
> m <- glmmPQL(y ~ f1 * f2 * f3 + (1|subj/day), correlation=corAR1(form
=~obsno|subj/day))
>
> f1 - f3 are fixed factors
>
> However, PQL estimation is not recommended for binary response data. With
no AIC and unreliable p values, model selection seems impossible! So my question
is:
>
> 1) are there any other functions which are suitable for a GLMM with
multilevel nested random effects and a AR1 correlation structure? Or is MCMC the
only option?
> 2) to make things more complicated, I'd also like to include a varFunc
variance structure to cope with heterogeneity. Is this possible in ML methods in
R? I'd also like to extend to a multinomial response at a later stage.
>
> GEE seems the best bet, but I come unstuck with the three-way nested
factors.
>
> Thanks for your help.
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.


-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm