[R-sig-eco] glm for ratio [0,1] data

[Bálint Czúcz]

Thanks to everyone for the responses!
I think I will try first with the betareg approach, but it might not
be easy to implement since the data set in question also exhibits
symptoms of zero inflation. :(
I'll see.

Best regards,
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/


On Mon, Aug 31, 2009 at 18:10, <Farrar.David at epamail.epa.gov> wrote:
> 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 at 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 at 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 at 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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>> >
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>>
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