[R] specify the covariance matrix for random effect

[Ben Bolker]

Qiu, Weiyu <weiyu <at> ualberta.ca> writes:

> 
> Hi,
> 
> I'm doing a generalized linear mixed model, and I currently use an 
> R function called "glmm". However, in
> this function they use a standard normal distribution for the random 
> effect, which doesn't satisfy my
> case, i.e.  my random effect also follows a normal distribution, 
> but observations over time are somehow
> correlated, so the covariance matrix would be different the
> identity matrix. Besides, it's also
> different from the commonly used AR(1), AR(2) structures, so 
> actually I need a way to write down the
> covariance matrix for the random effect in GLMM by myself.
> 

  If you could get by with an AR or ARMA structure then you could
use lme() with the 'correlation' argument from the nlme package.
If you have enough data/are willing to fit a completely unstructured
correlation matrix, you could use corSymm.  See ?corStruct in
the nlme package documentation: it is *in principle* possible to
write functions to implement your own correlation structures, e.g.
see

corSymm
nlme:::corSymm.corMatrix
nlme:::coef.corNatural

  However, this will not be easy unless you are already a good
R programmer and familiar with the nlme package.  If you really
need to do this I definitely recommend that you buy and study
Pinheiro and Bates's 2000 book.
  It might also be worth taking a look at the cor* objects in the
"ape" package, which are coded slightly more transparently.

  Followups should probably go to r-sig-mixed-models at r-project.org

  good luck
    Ben Bolker