[R-sig-ME] spatial autocorrelation as random effect with count data

[Paul Buerkner]

It seems as if GAM(M)s are well suited for this situation, why would you
prefer to stay with the GLM approach?

2018-01-10 21:59 GMT+01:00 Sima Usvyatsov <ghiaco at gmail.com>:

> Hello,
>
> 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.
>
> library(MASS)
>
> 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!
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]