[R-sig-Geo] Spatial filtering with glm with grid sampling

[Sima Usvyatsov]

Thank you so much for your response.

Yes, I managed to muck up the fake data in 2 (!) ways - the 1,000 lons and
the fact that the lon/lats weren't repeated. Here's the correct structure.

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))

In the meantime, I (somewhat) resolved the issue with the glmmPQL fixed
effects - my fault, of course.

My current model is set up as follows:

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

where RoundStart is the date/time of starting each count. I'm assuming that
by "using a standard GLMM" you were thinking of MASS's glmmPQL()?
Does this look correct for specifying the full space/time dependence? The
variogram and pacf look very decent, but one can never have too many
checks...

On Thu, Jan 11, 2018 at 5:01 AM, Roger Bivand <Roger.Bivand at nhh.no> wrote:

> On Wed, 10 Jan 2018, Sima Usvyatsov wrote:
>
> Hello,
>>
>> I am running a negative binomial model (MASS) on count data collected on a
>> grid. The dataset is large - ~4,000 points, with many predictors. Being
>> counts, there are a lot of zeroes. All data are collected on a grid with
>> 20
>> points, with high spatial autocorrelation.
>>
>> I would like to filter out the spatial autocorrelation. My question is:
>> since I have very limited spatial info (only 20 distinct spatial
>> locations), is it possible to simplify ME() so that I don't have to run it
>> on the whole dataset? When I try to run ME() on a 100-point subset of the
>> data, I get error in glm.fit: NA/NaN/Inf in 'x'. When I run it on a single
>> instance of the grid, I "get away" with a warning ("algorithm did not
>> converge").
>>
>> Here's a fake dataset. It was grinding for a while but not throwing errors
>> (like my original data would). Regardless, it demonstrates the repeated
>> sampling at the same points and the large number of zeroes.
>>
>
> The data set has 1000 values in Lon, so is probably bigger than you
> intended, and when 100 is used is not autocorrelated. You seem to have a
> hierarchical model, with repeated measurements at the locations, so a
> multi-level treatment of some kind may be sensible. If you want to stay
> with ME-based spatial filtering, maybe look at the literature on spatial
> panel (repeated measurements are in time) with ME/SF, and on network
> autocorrelation (dyadic relationships with autocorrelation among origins
> and/or destinations). Both these cases use Kronecker products on the
> selected eigenvectors, I think.
>
> Alternatively, use a standard GLMM with a grouped iid random effect and/or
> a spatially structured random effect at the 20 location level. If the
> groups are repeated observations in time, you should model the whole
> (non-)separable space-time process.
>
> Hope this helps,
>
> Roger
>
>
>> Any advice would be most welcome.
>>
>> library(spdep)
>> library(MASS)
>>
>> df <- data.frame(Loc = as.factor(rep(1:20, each = 5)), Lat = rnorm(100,
>> 30,
>> 0.1), Lon = rnorm(1000, -75, 1), x = rnegbin(100, 1, 1))
>> coordinates(df) <- ~Lon + Lat
>> proj4string(df) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
>> nb <- dnearneigh(x=coordinates(df), d1=0, d2=200,longlat = TRUE)
>> dists <- nbdists(nb, coordinates(df), longlat=TRUE)
>> glist <- lapply(dists, function(x) 1/x)
>> lw <- nb2listw(nb, glist, style="W")
>> me <- ME(x ~ 1, data = df, family = "quasipoisson", listw = lw, alpha =
>> 0.5)
>>
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>>
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>>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; e-mail: Roger.Bivand at nhh.no
> Editor-in-Chief of The R Journal, https://journal.r-project.org/index.html
> http://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
>

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