From: Beale, Colin <Colin.Beale_at_rspb.org.uk>

Date: Wed 13 Jul 2005 - 21:14:56 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Jul 13 21:20:44 2005

Date: Wed 13 Jul 2005 - 21:14:56 EST

My data are indeed bernoulli and not binomial, as I indicated. The
dataset consists of points (grid refs) that are either locations of
events (animals) or random points (with no animal present). For each
point I have a suite of environmental covariates describing the habitat
at this point. I was anticipating some sort of function that could run:

function(present ~ env1 + env2 + env3 + x + y, correlation = corSpher(form=~x+y), family = binomial)

where env1 to env3 are the habitat covariates, x & y the grid refs. If my data were normal, I undertand I would use gls() with exactly this, but drop the family requirement. As my data are bernoulli this is clearly not possible, but I was hoping the analysis may be analagous? The eventual aim is to firstly understand which environmental covariates are important in determining presence and then to use habitat maps to identify the areas expected to be most important.

Colin

-----Original Message-----

From: Prof Brian Ripley [mailto:ripley@stats.ox.ac.uk]
Sent: 13 July 2005 11:30

To: Beale, Colin

Cc: r-help@stat.math.ethz.ch

Subject: Re: [R] nlme, MASS and geoRglm for spatial autocorrelation?

You seem to want to model spatially correlated bernoulli variables. That's a difficult task, especially as these are bernoulli and not binomial(n>1). With a much fuller description of the problem we may be able to help, but I at least have no idea of the aims of the analysis.

glmmPQL is designed for independent observations conditional on the random effects.

On Wed, 13 Jul 2005, Beale, Colin wrote:

*> Hi.
**>
*

> I'm trying to perform what should be a reasonably basic analysis of

*> some spatial presence/absence data but am somewhat overwhelmed by the
**> options available and could do with a helpful pointer. My researches
**> so far indicate that if my data were normal, I would simply use gls()
**> (in nlme) and one of the various corSpatial functions (eg. corSpher()
**> to be analagous to similar analysis in SAS) with form = ~ x+y (and a
**> nugget if appropriate). However, my data are binomial, so I need a
**> different approach. Using various packages I could define a mixed
**> model (eg using
**> glmmPQL() in MASS) with similar correlation structure, but I seem to
**> need to define a random effect to use glmmPQL(), and I don't have any.
**> Could this requirement be switched off and still use the mixed model
**> approach? Alternatively, it may be possible to define the variance
**> appropriately in gls and use logits directly, but I'm not quite sure
**> how and suspect there's a more straight-forward alternative. Looking
**> at geoRglm suggests there may be solutions here, but it seems like it
**> might be overkill for what is, at first appearance at least, not such
**> a difficult problem. Maybe I'm just being statistically naive, but I
**> think I'm looking for a function somewhere between gls() and glmmPQL()
*

> and would be grateful for any pointers.

*>
**> Thanks very much,
**>
**> Colin Beale
**>
*

...

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Jul 13 21:20:44 2005

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