spGLM unexpectedly large sill values
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
<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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