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.

Thanks!

Sama

On Tue, Aug 1, 2017 at 2:18 PM, Patrick Schratz

<

[hidden email]> wrote:

> Correction: MASS::glmmPQL, not mgcv::

>

>

> On 1. Aug 2017, 22:07 +0200, Sama Winder <

[hidden email]>, 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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