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

[Ben Bolker]

  Because the spatial correlation machinery is extremely experimental
(and, to be honest, I didn't realize it was actually working already).
Kasper Kristensen added the spatial example to the "covstruct" vignette
a couple of months ago, and I just looked at it now - it seems
interesting.  Try it out: if it works (or doesn't work), I'd be happy to
hear about it. If you encounter problems (and please do check your
results **very** carefully), post an issue at
https://github.com/glmmtmb/glmmTMB/issues ...

  I think your "correlation=" specification below is
incorrect/redundant. Reading vignette("covstruct",package="glmmTMB") and
looking at the volcano example, I would try:

your_data$pos <- numFactor(d$site.Easting, d$site.Northing)

mod1 <- glmmTMB(Count ~ Stratum + SiteInStratum + ...other predictors +
> # random variable  ["nugget effect"]
> (1 | RoundStart) +
> # autocorrelation
> exp(0 + pos | RoundStart),
> data= your_data)

  cheers
   Ben Bolker

On 18-01-11 05:15 PM, Sima Usvyatsov wrote:
> 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
> <mailto: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
> <mailto: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]]
>> >
>> > _______________________________________________
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