All,

I wonder if glm with a quasibinomial option would work.  The variance 
would depend qualitatively on 
the mean in a seemingly reasonable way, but would be adjusted using a 
factor determined by the data. 

David Farrar,
National Center for Environmental Assessment, U.S.EPA, Cincinnati 

r-sig-ecology-bounces@r-project.org wrote on 08/31/2009 10:06:19 AM:

> [image removed] 
> 
> Re: [R-sig-eco] glm for ratio [0,1] data
> 
> Peter Solymos 
> 
> to:
> 
> Bálint Czúcz
> 
> 08/31/2009 10:07 AM
> 
> Sent by:
> 
> r-sig-ecology-bounces@r-project.org
> 
> Cc:
> 
> r-sig-ecology
> 
> Hi Bálint,
> 
> Here are my two cents.
> 
> By using LM with transformed data (which transformation can also be
> logit, loglog, cloglog, probit) you loose the Binomial error
> structure, because you won't follow the trial/success experiment
> scheme. But percent cover is not that kind of [0,1] data where this
> sampling is assumed, I think that's why you have asked :)
> 
> If your data is an estimate of a hidden response, than there must be
> ways to account for this, but I can only recall an example where e.g.
> Y is Poisson, but you observe it as ordinal (0, few, many). So you can
> establish cutoff values to get ordinal response from you percent
> cover, and use a hierarchical model in BUGS/JAGS (see WinBUGS manual
> for an example).
> 
> Cheers,
> 
> Peter
> 
> 
> On Mon, Aug 31, 2009 at 6:24 AM, Bálint Czúcz<czucz@botanika.hu> wrote:
> > Dear List,
> >
> > does anyone know a good way to perform GLM on ratio data (i.e. data
> > between 0 and 1)? Binomial GLM is quite straightforward to use if you
> > have integer numbers for successes/failures. But how to proceed if you
> > only have the ratio? This can occur in a multitude of ways, e.g the
> > response variable is the estimated cover of a species, percentage of
> > canopy lost, etc.
> >
> > One solution I know about is to try to transform such responses to
> > normal with the arcsine-squarroot transformation, and use lm on the
> > transformed response -- e.g. Crawley (2007, The R Book, p. 570.)
> > explicitely suggests this strategy.
> >
> > But I would still be interested if there is a glm approach that could
> > be used with the untransformed data. After hours spent with searching
> > for literature on such a glm, I couldn't find any. Do you know of
> > some?
> >
> > I would also be interested what happens if I just proceed with a
> > binomial glm with the response being between [0,1] and weights left to
> > 1. I know glm() will throw a warning -- but it also produces an
> > output. Can this output contain some valid, interpretable results, or
> > is it completely bullshit because of the violation of the assumptions?
> >
> > Thank you!
> > Bálint
> >
> >
> > --
> > Bálint Czúcz
> > Institute of Ecology and Botany of the Hungarian Academy of Sciences
> > H-2163 Vácrátót, Alkotmány u. 2-4. HUNGARY
> > Tel: +36 28 360122/137  +36 70 7034692
> > magyar nyelvű blog: http://atermeszettorvenye.blogspot.com/
> >
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> >
> >
> 
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