You could try estimating the conditional cumulative distribution function
with quantile regression by estimating a large interval of quantiles (e.g.,
0.01 to 0.99 if your n is large enough).  Quantile regression will readily
handle skewed and heterogeneous responses.  Some finessing required to
check when estimates are above a mass of zeros but this is all doable.

Brian

Brian S. Cade, PhD

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  cadeb@usgs.gov <brian_cade@usgs.gov>
tel:  970 226-9326



On Mon, Jun 16, 2014 at 5:57 AM, Johannes Björk <bjork.johannes@gmail.com>
wrote:

> Dear all,
>
> Im looking into how to fit a GLM model (Im using rjags) with data that are
> heavily right skewed. In addition, some variables also zero-inflated. The
> data are species area distribution measured as "total area (km^2)" which is
> subsetted into "area in tropical zone" and "area in temperate zone". The
> last two variables contain zeros.
>
> I have google zero-inflated models... and most that come up is
> "zero-inflated negative binomial" and zero-inflated negative poisson" for
> count data. I reckon I cannot use any of these distributions since my
> variables are not discrete.
>
> Any pointer to which distribution(s) that might fit this kind of data
> would be much appreciated.
>
> Attached: Histograms of the data
> _______________________________________________
> R-sig-ecology mailing list
> R-sig-ecology@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>

	[[alternative HTML version deleted]]

