[R-sig-ME] Modeling correlation structure in mixed models

[Highland Statistics Ltd.]

>Hi Phillip,

>I am happy with the book "Mixed Effects Models and Extensions in Ecology
>with R"
>by Zuur, Ieno, Walker, Saveliev and Smith. In fact I am reading chapter by
>chapter,
>and the reading is very digestible, as well as the examples are quite easy
>to understand and to be reference for our "real world".

That is appreciated..:-). 

Anyway..we have started with a sequel. I wanted to 
call it "Analysing Ecological Data - Shit happens", but of course you 
can't use a title like that for an academic book. But the idea is that
we use rather tricky data....nested...full of zeros...correlation at different
levels. However...too often, we are ending up with RBugs to analsye it as it allows you 
to easily implement difficult correlation structures in GLMMs. I found Ntzoufras (2009)
quite useful for this. He has a couple of sections explaining how correlations between 
residuals work their way to correlations between raw data in GLMMs. For the Gaussian
distribution this is trivial..but it is a bit more difficult for other distributions
and link functions. So..to answer the original question..for complicated correlation
structures, dig yourself into MCMC. But I think this is only an issue for longer time
series.

If..by the way...in due course, anyone on this list is a Bayesian specialist and would like
to proof-read a couple of chapters on ZIPs (and related stuff), nested data and MCMC, then that 
would be highly appreciated.

Alain Zuur


>
Cheers

>milton
>brazil=toronto

>
> I have been trying to learn mixed models in R by reading the books by
> Pinheiro and Bates; Faraway (both linear models books); and Crawley (R
> Book), but I would appreciate some guidance from the more experience R
> users. (I have a fair amount of experience with mixed models in SAS.)
>
> 1. Is there another (other than the above) suggested reference for
> understanding the workings of the nlme and lme4 libraries?
>
> 2. Is it the case that lme accepts correlated structures ONLY in the error
> term? I have problems in which I would like model random effects (such as
> year) using a random term with an autocorrelated structure. In SAS I use
> options to the “repeated” statement to add correlation structure to the
> error term, and I use options to the “random” statement to give correlation
> structure to the other random effects. I haven’t found anything in lme or
> lmer that allows me to specify correlated random effects. gee only allows
> correlation structure in the error term and does not allow random effects.
>
> 3. All of the examples of random effects in lme seem to have nested error
> structures. Is it the case that lme does not allow crossed random effects?
> lmer allows much more flexible specification of random effects, but I don’t
> see anything that allows correlated error structures.
>
> Thanks in advance,