[R-sig-eco] Help with nonlinear population trends & binomialregression

[Alexandre Villers]

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 Gastón 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*
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