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
T: +61 7 3365 2506
email: S.Blomberg1_at_uq.edu.au
http://www.evolutionarystatistics.org
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