Hi Tim You haven't really explained where your group variable in the glmm has come from. Moving from glm to glmm you've changed two things, adding the grouping and the autocorrelation as well.
You have to be very careful when using the autocorrelation function. As it stands the model will assume that the points on your gradient are evenly spaced and sorted in order. Regards Mike -----Original Message----- From: r-sig-ecology-boun...@r-project.org [mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of Tim Seipel Sent: 25 August 2011 10:04 To: r-sig-ecology@r-project.org Subject: [R-sig-eco] logistic regression and spatial autocorrelation 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 -- This message (and any attachments) is for the recipient only. NERC is subject to the Freedom of Information Act 2000 and the contents of this email and any reply you make may be disclosed by NERC unless it is exempt from release under the Act. Any material supplied to NERC may be stored in an electronic records management system. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
