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

[Sima Usvyatsov]

I was introduced today (by Roger Bivand) to the glmmTMB package that looks
very exciting. As a co-author, I was wondering why you didn't suggest it -
is there a reason it's a no-go in my situation? From a super quick read,
and a very naive thinking, is this not equivalent to the gpmmPQL setup
below?

mod1 <- glmmTMB(Count ~ Stratum + SiteInStratum + ...other predictors +
# random variable
(1 | RoundStart) +
# autocorrelation
exp(site.Easting + site.Northing | RoundStart),

family = nbinom2, # or nbinom1 - I guess decide based on residuals?
correlation = corExp(form=~site.Easting + site.Northing + RoundStart)

where RoundStart is the time of starting sampling along the repeated, set,
20-point sampling grid, easting and northing are the 20 points' coords,
Stratum is the allocation of the 20 sampling points to 5 strata and
SiteInStratum is  the 1:4 allocation within stratum.

This is my current setup:

mod1 <- glmmPQL(Count ~ Stratum + SiteInStratum + ...other predictors,
random = ~ 1 |RoundStart,
family = quasipoisson,
correlation = corExp(form=~site.Easting + site.Northing + RoundStart)

I have INLA and GAMs on my to-do list for this year - sounds like really
helpful ways to go about things, I just haven't gotten there yet...

Thank you so much!

On Wed, Jan 10, 2018 at 5:34 PM, Ben Bolker <bbolker at gmail.com> wrote:
>
> 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:
> > 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]]