[R-sig-Geo] spGLM unexpectedly large sill values

Sama Winder sgwinder at alaska.edu
Wed Aug 2 00:54:34 CEST 2017

Thanks Pat.

I will check out glmmPQL to see if I get similar results as I do in
spBayes::spGLM, since that could certainly be instructive.

Could you tell me more about how you fit the semivariograms?
Specifically, which residuals do you use, and then which semivariogram
function? I have explored this a bit but ran into a few threads
suggesting that semivariograms were more appropriate for normal data
and linear trends and never came to a solution I was happy with.

And, if I don't hear back from anyone else perhaps I will try the
r-sig-mixed-models group.


On Tue, Aug 1, 2017 at 2:18 PM, Patrick Schratz
<patrick.schratz at gmail.com> wrote:
> Correction: MASS::glmmPQL, not mgcv::
> On 1. Aug 2017, 22:07 +0200, Sama Winder <sgwinder at alaska.edu>, wrote:
> Hi all,
> I am running several fairly complicated presence/absence (binary)
> models, each of which includes ~700 data points and between 8 and 13
> predictor variables (a mix of continuous and factor variables).
> I'm using logistic regression, and first fit these without spatial
> effects using glm(). Since we're concerned about residual spatial
> autocorrelation, I also added spatial effects (with an exponential
> correlation structure) in spGLM. After a few attempts and many
> (500,000) iterations, these appear to be converging quite nicely.
> However, the sigma^2 values are much bigger than we expected (35, 50,
> 100). As a result (I suspect), my parameter coefficients are also much
> more extreme than they were in the non-spatial models. For example,
> without the spatial term my coefficients ranged from about -1.5 to
> 1.5, and now they range from -5 to 7. Since this is on the logistic
> scale, these result in nearly perfect 0 or 1 predicted probabilities.
> This feels like something has gone wrong, but I'm having trouble
> placing my finger on exactly what. If not, what is the interpretation?
> (As a side note, the phi values are within the range we expected).
> Any insights would be greatly appreciated!
> Thanks,
> Sama
> Sama Winder
> MS Statistics
> University of Alaska, Fairbanks
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