[R-sig-Geo] question on the difference between spdep function spautolm() and glm() with autocovariate

[Li, Han]

Dear list,

I am currently working on spatial autoregression modeling for my dissertation research. I want to use regression models to identify socioeconomic/landscape variables (15 total, var1$bat_survey - var15$bay_survey) that can affect the presence/absence of bats (p/a$bat_survey). Since spatial autocorrelation exists in my P/A data, I tried different spatial models.

My question is:

If I use the same way (same neighboring criteria, same weight style) to define neighbors, and build model #1 spatial simultaneous autoregression model (SAR) by function spautolm(), and model #2 glm() with autocovariate generated by function autocov_dist(), should I expect the same result, or not?

I understood that if I use glm() with autocovariate it will include one more variable (the autocovariate) in the result. I also learned that glm() is more a predicting model and spautolm() is more an explanatory model. But I am not sure whether the significant variables selected by these two models will be same.


##r code example##
model_1 <- spautolm (p/a ~ var1 + var2 + ... + var15, data = bat_survey, listw = neighbor_regime1, family="SAR")
####
autocov_model_2 <- autocov_dist (p/a$bat_survey, xy = coords, style = "W", type = "one")
model_2 <- glm (p/a ~ var1 + var2 + ... + var15 + autocov_model_2, family = "binomial", data = bat_survey)

Thanks in advance. Your insight will be deeply appreciated.

Han


Han Li
Department of Biology
Baylor University
Waco, TX  76798-7388
Phone: (254) 710-2151
Fax: (254) 710-2969
han_li at baylor.edu<mailto:han_li at baylor.edu>