[R-sig-ME] Spatial correlation in glmmTMB

Highland Statistics Ltd h|gh@t@t @end|ng |rom h|gh@t@t@com
Thu Jul 18 12:33:13 CEST 2019

I suggest trying INLA.


I hope that you have more than 62 observations in total?

Kind regards,




I would like to ask for help on how to account for spatial correlation in
glmmTMB package.

According to the help page (
I need to create a numFactor object grouping coordinates and a dummy
grouping factor.

mydata$pos <- numFactor(mydata$easting, mydata$northing)## spatial
mydata$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!


Visiting PhD student
School of Ocean Sciences
Bangor University
Menai Bridge, Anglesey, UK

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Dr. Alain F. Zuur
Highland Statistics Ltd.
9 St Clair Wynd
AB41 6DZ Newburgh, UK
Email: highstat using highstat.com
URL:   www.highstat.com

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).

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