I suggest trying INLA. http://www.r-inla.org/ I hope that you have more than 62 observations in total? Kind regards, Alain --------------------------------- Hello, I would like to ask for help on how to account for spatial correlation in glmmTMB package. According to the help page ( https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html), I need to create a numFactor object grouping coordinates and a dummy grouping factor. mydata$pos <- numFactor(mydata$easting, mydata$northing)## spatial coordinates mydata$group <- factor(rep(1, nrow(mydata)))## dummy factor Regarding to the dummy variable, I have 62 locations in my dataframe. The dummy variable should be 1 for all observations, or go from 1 to 62? (Actually I have tried both possibilities. First one give me convergence problems, second one cracks my R). I have been trying to run the following negative binomial mixed model: m1 = glmmTMB(density ~ wave_exposure + (1|location) exp(pos + 0|group), data= mydata, family= nbinom1, ziformula= ~0) ## I also tried different covariance structures (gau and mat), but no success so far. Any ideas or suggestions here? Thank you in advance! Andre. -- Visiting PhD student School of Ocean Sciences Bangor University Menai Bridge, Anglesey, UK ***************************************************
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).