[R-sig-eco] question about beta regressions

[Simon Blomberg]

On 12/11/13 14:18, Simon Blomberg wrote:
> Hi Marie,
>
> betareg uses a logit link function, so the negative intercept just 
> means that the intercept proportion is less than one. 

My bad. Negative intercept means p < 0.5).

> 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
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email: S.Blomberg1_at_uq.edu.au
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