There's a massive literature out there about species distribution
modelling. Some of it even takes into account such spatial
autocorrelation (which I think it a really good idea!) - Latimer's
descriptions of heirarchical models would be a good place to start:
http://hydrodictyon.eeb.uconn.edu/people/silander/silander%20web%20site%20stuff/LatimerWuGelfSil06EcolApp.pdf
http://www.eeb.uconn.edu/people/latimer/BAnnmain10-24-04.pdf
etc.
You'll also want to think about including responses that aren't simply
linear (maybe there's both a lower and upper tolerance to Growi_sea).
Good luck!
C
On 25/08/2011 12:04, Tim Seipel wrote:
Dear List,
I am trying to determine the best environmental predictors of the
presence of a species along an elevational gradient.
Elevation ranges from 400 to 2050 m a.s.l. and the ratio of presences to
absences is low (132 presences out 2800 samples)
So to start I fit the full model of with the variable of interest.
sc.m<-glm(PA~sp.max+su.mmin+su.max+fa.mmin+fa.max+Slope+Haupt4+Pop_density+Dist_G+Growi_sea+,data=sc.pa,'binomial')
First, I performed univariate and backward selection using Akaike
Information Criteria, and the fit was good and realistic given my
knowledge of the environment though the D^2 was low 0.08. My final model
was:
---------------------------------
glm(formula = PA ~ Slope + sp.mmin + su.max + fa.mmin + Haupt4,
family = "binomial", data = sc.pa)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.5415 -0.3506 -0.2608 -0.1762 3.0768
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -73.45212 23.13842 -3.174 0.00150 **
Slope -0.03834 0.01174 -3.265 0.00109 **
sp.mmin -15.34594 5.30360 -2.893 0.00381 **
su.max 5.09712 1.70332 2.992 0.00277 **
fa.mmin 13.52262 4.64021 2.914 0.00357 **
Haupt42 -0.72237 0.27710 -2.607 0.00914 **
Haupt43 -0.95730 0.37762 -2.535 0.01124 *
Haupt44 -0.25357 0.24330 -1.042 0.29731
---
Null deviance: 958.21 on 2784 degrees of freedom
Residual deviance: 896.10 on 2777 degrees of freedom
AIC: 912.1
----------------------
I then realized that my residuals were all highly correlated (0.8-0.6)
when I plotted them using acf() function.
So to account for this I used glmmPQL to fit the full model:
model.sc.c<- glmmPQL(PA ~
sp.mmin+su.mmin+su.max+fa.mmin+Slope+Haupt4+Pop_density+Dist_G+Growi_sea,
random=
~1|group.sc, data=sc.dat, family=binomial, correlation=corAR1())
However, the algorithm failed to converge and all the p-vaules were
either 0 or 1 and coefficient estimates approached infinity.
Additionally the grouping factor of the random effect is slightly
arbitrary and accounts a tiny amount of variation.
---
So know I feel stuck between a rock and a hard place, on the one hand I
know I have a lot of autocorrelation and on the other hand I don't have
a clear way to include it in the model.
I would appreciate any advice on the matter.
Sincerely,
Tim
[[alternative HTML version deleted]]
_______________________________________________
R-sig-ecology mailing list
R-sig-ecology@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
--
Dr Colin Beale
Safari Ecology Blog: http://safari-ecology.blogspot.com
_______________________________________________
R-sig-ecology mailing list
R-sig-ecology@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
