[R-sig-ME] spatial autocorrelation as random effect with, count data
Highland Statistics Ltd
highstat at highstat.com
Wed Jan 10 23:35:55 CET 2018
Try R-INLA.....much easier.
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
<CAFGTTqT8cDbDeKg2cikgdBkjAGz0spXakoEne7EmA28D3vVjgg at mail.gmail.com>
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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
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.
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!
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Dr. Alain F. Zuur
Highland Statistics Ltd.
9 St Clair Wynd
AB41 6DZ Newburgh, UK
Email: highstat at 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
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).
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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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