[R-sig-ME] spatial autocorrelation as random effect with count data
bbolker at gmail.com
Wed Jan 10 22:34:27 CET 2018
PS: depending on how badly you wanted this, it would be possible to
what Doug Bates said (impose spatial dependence on the random effects
for the 20 spatial points) via the modular machinery of glmer, but it
would take some effort and knowledge ...
On Wed, Jan 10, 2018 at 3:59 PM, Sima Usvyatsov <ghiaco at gmail.com> wrote:
> 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.
> 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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