Try R-INLA.....much easier. Alain ------------------------------ Message: 4 Date: Wed, 10 Jan 2018 16:59:02 -0400 From: Sima Usvyatsov <ghiaco at gmail.com> To: R-sig-mixed-models at r-project.org Subject: [R-sig-ME] spatial autocorrelation as random effect with count data Message-ID: <CAFGTTqT8cDbDeKg2cikgdBkjAGz0spXakoEne7EmA28D3vVjgg at mail.gmail.com> Content-Type: text/plain; charset="UTF-8" Hello, I am working on a spatially autocorrelated dataset with a negative binomial (count) response variable. I have been using the glmmPQL approach (MASS), but I seem to have a hard time fitting the fixed effects. I came across the mention that one could build the spatial autocorrelation into a random effect (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/015364.html). I've done some searching but could not find a straightforward example of this practice. I have 20 sampling locations (sampled repeatedly to a 4,000 point dataset) and I know that there is spatial autocorrelation between them (by looking at autocorrelation plots of a naive model). The 20 grid points are clustered into 4 strata, and I am interested in the strata effects (so would like to keep the strata as fixed). How would I go about expressing the spatial autocorrelation in this setup? In the future I'd like to explore GAMs for this application, but for now I'm stuck with a GLM approach... I would love to be able to use glmer() with a random effect that expresses spatial autocorrelation. Here's a fake dataset. library(MASS) df <- data.frame(Loc = as.factor(rep(1:20, each = 5)), Lat = rep(rnorm(20, 30, 0.1), each = 5), Lon = rep(rnorm(20, -75, 1), each = 5), x = rnegbin(100, 1, 1), Stratum = rep(1:5, each = 20)) Thank you so much!
Dr. Alain F. Zuur Highland Statistics Ltd. 9 St Clair Wynd AB41 6DZ Newburgh, UK Email: highstat at highstat.com URL: www.highstat.com And: NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, and Utrecht University, P.O. Box 59, 1790 AB Den Burg, Texel, The Netherlands Author of: 1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. (2017). 2. Beginner's Guide to Zero-Inflated Models with R (2016). 3. Beginner's Guide to Data Exploration and Visualisation with R (2015). 4. Beginner's Guide to GAMM with R (2014). 5. Beginner's Guide to GLM and GLMM with R (2013). 6. Beginner's Guide to GAM with R (2012). 7. Zero Inflated Models and GLMM with R (2012). 8. A Beginner's Guide to R (2009). 9. Mixed effects models and extensions in ecology with R (2009). 10. Analysing Ecological Data (2007).