I am using binom.krige() function of the R package geoRglm for determining the spatial predictions of a binary (0, 1) response variable with several continuous as well as discrete covariates.
Using glm() with binomial logit link function I found that the response variable is showing significant dependency on several covariates.
I included the trend into binom.krige() using krige.glm.control() where I specified the two trend models as
> trend.d=trend.spatial(~ rivers + roads + annual_pre + annual_tem + elevation_ + host_densi + lulc + moist_dq + moist_in + moist_wq, data_points)
> trend.l=trend.spatial(~ rivers1 + roads + annual_pre + annual_tem + elevation_ + host_densi + lulc + moist_dq + moist_in + moist_wq, pred_grid)
The question, which is confusing me, is when trend.d and trend.l go into krige.glm.control() and eventually into binom.krige(), does it actually fit a glm with binomial logit or just linear model (because the above equations seem to be a linear model)?
