Dear all, I want to model bird distribution at see. The data consists 10000 samples but 90% are zeros. I try first a gamm (mgcv)with quasipoisson but it might bee problematic with so many zeros. I look for a (spatial) model that can use Zero Inflated Poisson distribution or similar. Is there any? Thank you sincerely yours Sven
Zero Inflated Poisson
3 messages · Sven Adler, Edzer Pebesma, Virgilio Gomez Rubio
*I'm not sure if you're aware of the following publication: * *2005*: Mapping sea bird densities over the North Sea: spatially aggregated estimates and temporal changes <http://www.citeulike.org/article/3505281> Pebesma Edzer J. and Duin Richard N. M. and Burrough Peter A. /Environmetrics/ *The analysis done there is mostly reproduced by: * *library(gstat) demo(fulmar) # Fulmaris glacialis * *Another direction you might want to look at is package geoRglm. * *Best regads, -- Edzer *
Sven Adler wrote:
Dear all, I want to model bird distribution at see. The data consists 10000 samples but 90% are zeros. I try first a gamm (mgcv)with quasipoisson but it might bee problematic with so many zeros. I look for a (spatial) model that can use Zero Inflated Poisson distribution or similar. Is there any? Thank you sincerely yours Sven
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Edzer Pebesma Institute for Geoinformatics (ifgi), University of M?nster Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251 8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de/ http://www.springer.com/978-0-387-78170-9 e.pebesma at wwu.de
Sven, In addition to what Edzer has pointed out, I have been trying to fit some zero-inflated Binomial models recently, but I have found that including spatial random effects may not be a good idea when the number of zeros is large. In our case, we have 126 observations with only 17 non-zero observations. We used package VGAM to fit zero-inflated models and WinBUGS to fit models with random effects. In general, including random effects is problematic and convergence was terrible in some cases. My feeling is that the zero-inflated part of the model may clash with random effects. If you have several observations per site this may change. We also observed a better fit when we consider several observations per site (by splitting the observations into 3 age groups). I do not think that there is a way of fitting zero-inflated spatial models in R (but I may be wrong). I asked Paulo Ribeiro some months ago about whether geoR/geoRglm can handle that and the answer was no. Hope this helps. Virgilio El mi?, 11-03-2009 a las 16:21 +0100, Edzer Pebesma escribi?:
*I'm not sure if you're aware of the following publication: * *2005*: Mapping sea bird densities over the North Sea: spatially aggregated estimates and temporal changes <http://www.citeulike.org/article/3505281> Pebesma Edzer J. and Duin Richard N. M. and Burrough Peter A. /Environmetrics/ *The analysis done there is mostly reproduced by: * *library(gstat) demo(fulmar) # Fulmaris glacialis * *Another direction you might want to look at is package geoRglm. * *Best regads, -- Edzer * Sven Adler wrote:
Dear all, I want to model bird distribution at see. The data consists 10000 samples but 90% are zeros. I try first a gamm (mgcv)with quasipoisson but it might bee problematic with so many zeros. I look for a (spatial) model that can use Zero Inflated Poisson distribution or similar. Is there any? Thank you sincerely yours Sven
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