[R-sig-ME] Combining spatial and temporal correlation structures - possible?

[Ben Bolker]

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On 05/26/2011 12:50 PM, Jens Oldeland wrote:
> Dear Mixed-Model SIG
> 
> I am trying to use serial and spatial correlation structures in a gamm
> model.
> 
> g2 <- gamm(sqrt(pp10M.day) ~
>          s(tempsurf, bs="cr"),
>          data=data,
>          random= list(Station= ~1),
>          correlation=corARMA(form = ~ 1|Station, p =2,q=1),
>          family=gaussian,
>          control=lmc)
> 
> how do I put in my spatial correlation structure?
> 
> correlation=corExp(form = ~ Station,nugget=T)
> 
> I tried using correlation=list()  but did not suceed. I did not find
> anything in Zuur or Pinheiro & Bates etc.
> 
> would be glad if someone has an idea (or even says that it is not possible)
> 

  I won't say it's impossible, but it's probably pretty tricky.
Spatio-temporal correlation structures are a research frontier (in my
opinoin at least). Separable correlation structures are easier than
non-separable ones (library("sos"); findFn("{spatio-temporal
correlation}") finds the ramps package ...
   I think you'd probably have to build your own corClass. If you do
decide to go this route, I would strongly recommend looking at some
simple examples in packages *outside* of nlme -- e.g. the ape package --
findFn("corClasses") is a good start.
   However, my recommendation would be to try to model the spatial
correlation via a spatial GAM (see examples in Simon Wood's book), and
leave the correlation stuff for the temporal component.

  I'd be happy to hear other ideas.

  Ben Bolker
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