Spatial correlation in glmmTMB
For glmmTMB, if your locations aren't otherwise grouped (e.g. into distinct sites), then you should use factor(rep(1,62)). As Alan Zuur suggests, 62 might be a fairly small sample for estimating spatial autocorrelation. If you give us more information about your model (e.g. post the results of summary(), it might help us diagnose and/or fix your convergence problems ... The mgcv package will also let you fit negative binomial/spatial models (with a Mat?rn structure, see ?smooth.construct.gp.smooth.spec; for the random effect, see ?smooth.construct.re.smooth.spec).
On 2019-07-18 6:33 a.m., Highland Statistics Ltd wrote:
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.