[R-sig-ME] Help with script for adding X and Y coordinates into a correlation formula

[Gavin Simpson]

On Tue, 2011-03-08 at 08:07 -0600, Douglas Bates wrote:
> On Tue, Mar 8, 2011 at 4:15 AM, Karen Moore <kmoore at tcd.ie> wrote:
> > Thanks for getting back to me. Do you mean that I'm incorrect in adding
> > random factor AND trying to add correlation structure??
> 
> It's the nature of a generalized linear model or generalized linear
> mixed model.  When the distribution of the response is multivariate
> Gaussian you can model the mean and the variance-covariance structure
> separately.  For other distributions, like a Bernoulli or binomial or
> Poisson you can't.  Once you have specified the mean you have
> specified the entire distribution.

Dear Doug, fellow mixed modellers,

> For spatial structure one approach is to incorporate the spatial
> correlation into the Gaussian distribution of the random effects where
> you define one random effect per location.

Would the same approach apply to time series - one random effect per
time point? What sort of covariance structure would that fit - a
correlation between each time point?

I'm quite interested in this but, if the above hasn't given it away, I'm
not particularly as au fait with my mixed modelling knowledge as I
probably should be. I deal a lot with environmental time series for
which a Gamma mixed model would be a natural way to handle the data -
but the problem is the temporal autocorrelation.

With lme() we could fit on a log scale and use corStruct() objects, and
via glmmPQL() we could do the computations for non-Gaussian models and
include smooth terms via gamm(). Would I be misunderstanding you Doug if
I took your comments to mean fitting a GLMM or GAMM with a corStruct()
object for argument correlation in lme() is inappropriate?

Thanks in advance,

G

> May I suggest that re-route the discussion to the
> R-SIG-Mixed-Models at R-project.org mailing list, which I have cc:'d on
> this reply?  Several of those who read that list have more experience
> than I do in this area.
> 
> > On 7 March 2011 18:33, Douglas Bates <bates at stat.wisc.edu> wrote:
> >>
> >> On Mon, Mar 7, 2011 at 12:07 PM, Karen Moore <kmoore at tcd.ie> wrote:
> >> > opps for got to send data
> >> >
> >> > On 7 March 2011 18:05, Karen Moore <kmoore at tcd.ie> wrote:
> >> >>
> >> >> Hi,
> >> >>
> >> >> Tryign to add correlation strcuture to a glm model as there are
> >> >> geographical pairs of sites in my data set and resids of model plotted
> >> >> against Site no. showed clear patterns
> >> >>
> >> >> VASCmix3<-glm( AVVAS~ fForesttype + CANOPEN +TREENO10+ LOI+ WELLF+
> >> >> DISTWOOD+ MATNAT1000+ MATNAT1000:CANOPEN +DISTEDGE +MATFOR1000
> >> >> ,family=poisson, data = VASCmix3stanb)
> >> >>
> >> >> Here trying to add structures:
> >> >>
> >> >> globalmix<-  formula (AVVAS~ fForesttype + CANOPEN +TREENO10+ LOI+
> >> >> WELLF+
> >> >> DISTWOOD+ MATNAT1000+ MATNAT1000:CANOPEN +DISTEDGE
> >> >> +MATFOR1000+(1|fSITENO) )
> >> >>
> >> >> globalmixSpher <- lmer(globalmix, correlation = corSpher(form = ~ X +
> >> >> Y ,
> >> >> nugget= TRUE), family = poisson, data =  VASCmix3stanb)
> >> >>
> >> >> How do I tell R I want to take the X and Y variables from my data sheet
> >> >> "VASCmix3stanb.txt" (attached FYI) and put into the formula adding a
> >> >> correlation structure
> >>
> >> You don't.  The definition of a generalized linear model or a
> >> generalized linear mixed model requires that the responses are
> >> conditionally independent, given the random effects.  It is not
> >> possible to modify the correlation structure of the responses given
> >> the random effects because the distribution is completely determined
> >> by the conditional mean.
> >>
> >
> >
> >
> > --
> > Karen Moore
> > PhD Researcher,
> > FORESTBIO,
> > Department of Botany,
> > Trinity College Dublin
> > Ireland
> >
> > Ph: 00 353 (0)87 9422 502
> >
> > http://www.ucc.ie/en/planforbio
> >
> 
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> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

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