Help with nonlinear population trends & binomialregression
Hey Matthew, You should have a look at the Zuur et al. 2009 and the chapter 6 dealing with Violation of Independance (page 143). They explain how to fit GAMMs for different locations (species in your case). You can select models through AIC (but I guess you have to be pretty cautious with this type of model in model selection process). You should also consider adding a temporal correlation structure given you have times series (this is also explained in this book). HTH Alex
On 17/12/2010 10:55, Aitor Gast?n wrote:
Matthew, A more flexible model may be a better option than fitting two separate lines (e.g. restricted cubic splines, rcs function of the rms package or a generalized additive model, mgcv package). A restricted cubic spline with 4 knots may be enough to model the response curve that you describe. Hope that helps, Aitor -------------------------------------------------- From: "Matthew Forister" <forister at gmail.com> Sent: Friday, December 17, 2010 7:19 AM To: <r-sig-ecology at r-project.org> Subject: [R-sig-eco] Help with nonlinear population trends & binomialregression
Hello, I'm hoping someone can point me in a new direction with this particular issue... I have count data for many species across 20-30 years. I know that many species are declining, probably in association with habitat destruction, and I have been using binomial regression to model the declines (e.g. glm(cbind(presence,visits-presence)~years,binomial). I have also used the glm.binomial.disp function for overdispersion. So far so good, but here's the issue: not all species decline in the same way... Some go down steadily over the years, while others will be holding steady and then suddenly start on a decline. There are other patterns, but those two are dominant and I would like to be able to say that different species have these different dynamics. But how do I quantify those different curves? I have played with fitting quadratic and cubic terms within the binomial regression (e.g. glm(cbind(presence,visits-presence)~years+I(years^2),binomial)), and then comparing models with AIC to think about the better fit of the model with the quadratic. That kinda makes sense for some species, but it's far from satisfying... In the case I described (a species holding steady for 2 decades and then going into a steady decrease for another 10), what it really looks like is two different lines, one flat and one precipitous. Is there a way to ask if a given relationship is better fit by two lines than one? any other hints on how to describe these kinds of dynamics? thanks! Matt -- Matthew L Forister Assistant Professor Department of Biology / MS 314 1664 N. Virginia St. University of Nevada, Reno Reno, Nevada 89557 -- E-mail: forister at gmail.com Office phone: (775) 784 - 6770 Lab phone: (775) 784 - 7083 Fax: (775) 784 - 1302 Office: room 257 Fleischmann Agriculture Building -- Webpage: https://sites.google.com/site/greatbasinbuglab/ -- [[alternative HTML version deleted]]
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Alexandre Villers, PhD. Postdoc researcher Spatial Ecology & Population Dynamics Section of Ecology, Department of Biology University of Turku FIN-20014 Turku Finland @mail: alexandre.villers at utu.fi phone: 00358 (0)2 333 5039 Skype You can skype me (but think of using Ekiga instead !) <skype:aquila06?call> *Use open source and free softwares* <http://cran.r-project.org/> <http://grass.itc.it/> <http://www.qgis.org/> <http://fr.openoffice.org/> <http://www.mozilla-europe.org/fr/> <http://www.mozilla-europe.org/fr/>