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: [email protected] <[email protected]> tel: 970 226-9326 On Mon, Jun 16, 2014 at 5:57 AM, Johannes Björk <[email protected]> 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 > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > [[alternative HTML version deleted]]
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