[R-sig-eco] question about beta regressions

[Simon Blomberg]

Hi Marie,

betareg uses a logit link function, so the negative intercept just means 
that the intercept proportion is less than one. To convert it back to a 
proportion, use plogis(InterceptValue).

The intercept is the logit(proportion) that you would get if all your 
covariates were set to zero and the landfill treatment is set to its 
baseline. You might get a more interpretable intercept if you centre 
your explanatory variables (see ?scale). You could also look at changing 
the default contrasts to "contr.sum" which will give comparisons to the 
grand mean for your landfill variable. See ?contr.sum. To do this, just use:

options(contrasts=c("contr.sum", "contr.poly"))

Hope this helps,

Simon.

On 12/11/13 12:29, marieline gentes wrote:
> Hello,
>
> I have a question regarding the function Betareg. I am a bit new to this package, and maybe I did not understand all the theory behind...
>   
> My data are proportions (%) of a contaminant (DecaBDE) in blood of birds (n=78) which were GPS-tagged so that we could see what kind of habitat they visited. We are investigating the effects of habitat use on the proportions of that contaminant.
>   
> The full model is as follow: %contaminant = % time spent in agriculture + %
> time in urban + % time in St-Lawrence River + having visited a landfill (yes/no).
>
> Coding:
> Deca.sumBDE <- betareg (DecaJb.sumBDEs ~ Agri.outCol24 + AllLawren.outCol24 + LandfiWstwater.YesNo
> + UrbanCov1.outCol24, data = mydata).
>
> Because I am not yet completely familiar with all the theory underlying beta regressions, I followed one of the examples in the package and created a basic model i.e., no second part specified to model the precision (as you can see from the coding).
>   
> Somehow all my models produced with betareg (including the intercept only) end up with a negative intercept - but I have no negative data. I was also expecting that the intercept of the null model would be the average of my dependant variable (as in a regular linear regression), but it is not....
>
> Any suggesions ? Could this be happening because I did not fit a two parts model ? Or did I simply misunderstand
> how to interpret and use beta regressions?
>
> Thank you for your time,
>
> Marie
> PhD candidate
> Canada
>
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-- 
Simon Blomberg, BSc (Hons), PhD, MAppStat, AStat.
Lecturer and Consultant Statistician
School of Biological Sciences
The University of Queensland
St. Lucia Queensland 4072
Australia
T: +61 7 3365 2506
email: S.Blomberg1_at_uq.edu.au
http://www.evolutionarystatistics.org

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